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CHARACTERIZATION OF THE E3 UBIQUITIN LIGASE
SIAH2 AS AN ANTI-CANCER TARGET
ANUPRIYA GOPALSAMY
M.Sc (Bioinformatics), University of Madras
A THESIS SUBMITTED FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
YONG LOO LIN SCHOOL OF MEDICINE
DEPARTMENT OF OBSTETRICS AND GYNAECOLOGY
NATIONAL UNIVERSITY OF SINGAPORE
2013
For my parents...
i
DECLARATION
I hereby declare that this thesis is my original work and it has been written by
me in its entirety. I have duly acknowledged all the sources of information
which have been used in the thesis.
This thesis has also not been submitted for any degree in any university
previously.
Anupriya Gopalsamy
21 May 2014
ii
ACKNOWLEDGEMENTS
First and foremost, I thank GOD for blessing me with privilege and
much-needed strength to pursue this program. It is only with His grace that I
have come so far in this journey of learning.
I express my supreme gratitude to Prof. Kunchithapadam
Swaminathan, Department of Biological sciences for kindly agreeing to be my
supervisor when my former supervisor left. I feel profound privilege to thank
Prof. Swaminathan for believing in me and took me under his wings to embark
on the same project with different prospective. I am thankful to him for
patiently tolerating my mistakes and politely pointing them out to me and, in
the process, moulding me into a better researcher. I will be deeply indebted to
him for all his support over the years.
I express my heartfelt gratitude and thanks to Prof. Thilo Hagen,
Department of Biochemistry for agreeing to be a collaborator and for hosting
me in his lab, providing with all facilities and guidance. Prof. Hagen taught
me several experimental techniques and also taught me how to think as a
researcher. He also expertly showed me how to design experiments and
communicate results. I want to thank all the wonderful people in his lab for
their valuable discussions on my project. I extend my special thanks to
Christine, Yee Liu and Shin Yee for voluntarily providing timely help in my
experiments.
I also express my appreciation and thanks to Dr. Lijun, Department of
obstetrics and Gynaecology, who allowed me to share their lab facilities to
carry out certain experiments. I thank Zhang Zhi Wei and Seok Eng for
providing me the needful in their lab.
I am grateful to my former supervisor Dr. Annamalai Loganath for his
support and guidance for the first year of my candidature. I would like to
extend my sincere thanks to my other supervisors Prof. Arijit Biswas and Prof.
Yap Seng Chong of the department of Obstetrics and Gynaecology, who have
given me financial, academic and technical support to pursue my research
smoothly.
iii
A word of praise will not be sufficient to acknowledge Lilly, Liha and
Apple for their timely help to solve all the administrative works.
I am thankful to the all my present and former SBL4 labmates Roopa,
Deepthi, Umar, Shani, Pavithra, Madhuri, Divya, Raghu, Jobi, Pankaj,
Fengxia, Jikun, and Kanmani for their help as and when required. My Special
thanks to Thomas, undergraduate student for his contribution to this work. I
also would like to thank Prof. Sivaraman and his lab members (SBL5) for their
help and valuable discussions on my project.
A few people were not only my lab mates but also happened to be my
closest friends during the course of my research. I would like to say “Thank
you” to Roopa, (my close friend, senior and labmate) for always being there
for me and shared most of the best moments in Singapore. She always
provided a source of help in research, advice on life and living, and support
during hard times. I would also take this opportunity to thank Lakshmi Akka
(former colleague) for providing a great company and unforgettable
friendship throughout the stay at NUS. I would like to thank Lalitha (my close
friend and rooomate) for all the good times and also for listening to and
letting me vent out all my thoughts, worries and frustrations. I want to thank
my housemates Ambica, Deepthi, Divya, Lavanya, Lalitha and Roopa for all
the fun, memorable times in Singapore and for their kind help and cooperation
during this dissertation.
I thank NUS for having given me the opportunity to pursue my Ph.D.
with a research scholarship.
Finally and most importantly, my indebtedness to my parents for their
constant support for the choices I made and their sacrifice are beyond
expression. I dedicate this thesis for them as a humble tribute to their love and
blessings that has brought me to where I am now.
iv
TABLE OF CONTENTS
DECLARATION ............................................................................................... i
ACKNOWLEDGEMENTS ............................................................................ ii
TABLE OF CONTENTS ............................................................................... iv
SUMMARY ................................................................................................... viii
LIST OF TABLES ........................................................................................... x
LIST OF FIGURES ........................................................................................ xi
LIST OF PUBLICATIONS ......................................................................... xiii
LIST OF SYMBOLS AND ABBREVIATIONS ........................................ xiv
CHAPTER 1. INTRODUCTION ................................................................... 1
1.1 Biological background ......................................................................... 1
1.1.1 The importance of regulated protein degradation .............................. 1
1.1.2 Ubiquitination and protein degradation ............................................. 1
1.1.3 E3 ubiquitin ligases ............................................................................ 4
1.1.4 The Siah family of proteins ................................................................ 5
1.1.5 Siah Structure ..................................................................................... 6
1.1.6 Diverse role and substrates of Siah .................................................... 8
1.1.7 Siah1 and cancer .............................................................................. 10
1.1.8 Role of Siah2 in cancer .................................................................... 10
1.1.8.1 Hypoxia signaling connected to Siah2 in cancer ..................... 12
1.1.8.2 RAS signaling connected to Siah2 in cancer ........................... 14
1.1.9 E3 ubiquitin ligases as a cancer target ............................................. 15
1.1.10 Protein-Protein Interactions as drug targets ..................................... 16
1.2 Drug Development ............................................................................. 17
1.2.1 Introduction to drug discovery ......................................................... 17
1.2.2 Stages in drug development and the role of in silico approaches .... 17
1.2.3 Computer-aided drug design (CADD) ............................................. 20
1.2.3.1 Homology modeling ................................................................ 21
1.2.3.2 Molecular dynamics simulation ............................................... 23
v
1.2.3.3 Structure based drug design (SBDD) ....................................... 24
1.2.3.4 Active site identification ....................................................... 26
1.2.3.5 Virtual screening .................................................................. 26
1.2.3.6 Docking ................................................................................ 28
1.2.3.7 Scoring methods ................................................................... 30
1.2.3.8 Adsorption, distribution, metabolism and excretion (ADME) .
.............................................................................................. 32
1.2.4 Successes and limitations of SBDD ................................................. 33
1.3 Protein crystallization and data collection ...................................... 35
Objectives ........................................................................................................ 36
CHAPTER 2. MATERIALS AND METHODS .......................................... 37
2.1 Homology modeling ........................................................................ 37
2.2 Validation of the modeled structure ................................................. 37
2.3 Protein simulation ............................................................................ 38
2.4 Docking ............................................................................................ 38
2.5 Virtual screening .............................................................................. 39
2.6 Cell lines and cell culture ................................................................. 39
2.7 Molecular cloning ............................................................................ 40
2.7.1 Human plasmid constructs ........................................................... 40
2.7.2 Bacterial expression plasmid constructs ...................................... 40
2.8 Transient transfection ....................................................................... 41
2.8.1 Plasmid transfection ..................................................................... 41
2.8.2 Small-interfering (siRNA) transfection ....................................... 41
2.9 Protein isolation and concentration determination........................... 42
2.10 SDS-PAGE ...................................................................................... 42
2.11 Immunoblotting and detection ......................................................... 42
2.12 Co-immunoprecipitaction ................................................................ 43
2.13 GST pull down ................................................................................. 43
2.14 RNA isolation .................................................................................. 44
2.15 Real-time polymerase chain reaction using SYBR green ................ 44
2.16 Domain swapping using fusion PCR and mutants ........................... 45
2.17 Drug treatments and preparation ...................................................... 47
vi
2.18 MTS assay ........................................................................................ 47
2.19 Protein expression and purification of MBP-SBD-Siah2 and MBP-
PHD3 ................................................................................................ 48
2.20 Statistical analysis ............................................................................ 49
2.21 Isothermal titration calorimetry (ITC) ............................................. 49
2.22 Crystallization .................................................................................. 50
CHAPTER 3. IDENTIFICATION OF DRUG LIKE MOLECULES
AGAINST SBD OF SIAH2 BY AN IN SILICO APPROACH .................. 51
3.1 Introduction ........................................................................................ 51
3.2 Results ................................................................................................. 52
3.2.1 Generation of SBD of Siah2 by homology modeling ...................... 52
3.2.2 Evaluation of the modeled structure ................................................ 53
3.2.3 Molecular dynamics simulation of stability ..................................... 55
3.2.4 Docking and HTVS screening of compounds ................................. 56
3.2.5 Selection of inhibitors ...................................................................... 57
3.2.6 Bioavailability of the lead molecules ............................................... 60
3.3 Discussions .......................................................................................... 62
CHAPTER 4. EXPERIMENTAL VALIDATION OF DRUG LIKE
MOLECULES BY IN VITRO AND IN VIVO BINDING ASSAYS .......... 64
4.1 Introduction ........................................................................................ 64
4.2 Results ................................................................................................. 65
4.2.1 SBD of Siah2 alone can independently interact with PHD3
irrespective of the N terminal Ring Domain ................................................ 65
4.2.2 GST pull down for SBD of Siah2 with PHD3 ................................. 67
4.2.3 Expression and purification of MBP-SBD of Siah2 ........................ 67
4.2.4 Expression and purification of MBP-PHD3 .................................... 70
4.2.5 ITC analyses ..................................................................................... 73
4.2.6 Compounds partially decrease SBD interaction with PHD3 ........... 77
4.2.6.1 Coimmunoprecipitation of SBD with PHD3 in the presence of
compounds ............................................................................................... 77
vii
4.2.6.2 GST pull down of SBD with PHD3 in the presence of
compounds ............................................................................................... 79
4.2.7 Basal level expression of Siah2 in breast cancer ............................. 81
4.2.8 siRNA mediated gene silencing ....................................................... 83
4.2.9 Endogenous Siah2 protein expression and antibody validation....... 84
4.2.10 Effect of Compounds on Cell Viability ........................................... 87
4.2.11 Analysis of the specificity of the compound 1 for Siah2 in MCF7
cells .................................................................................................. 89
4.3 Discussions .......................................................................................... 90
4.3.1 Advantages ....................................................................................... 90
4.3.2 Limitations ....................................................................................... 90
CHAPTER 5. IDENTIFICATION OF CRITICAL RESIDUES
INVOLVED IN SUBSTRATE SPECIFICITY OF SIAH2 ........................ 92
5.1 Introduction ........................................................................................ 92
5.2 Results ................................................................................................. 94
5.2.1 Siah1-PHD3 interaction ................................................................... 94
5.2.2 SBD of Siah1 and Siah2 region swapped to obtain chimeric forms 96
5.2.3 Binding of wild type (WT) and chimera SBD's with substrates ...... 96
5.2.4 Identification of critical residues unique for Siah2 .......................... 97
5.2.5 Mutagenesis of Siah1-2 and Siah2-1 ............................................... 98
5.2.6 Binding of wild type and mutated chimeric SBD's with substrates .....
........................................................................................................ 100
5.2.7 Crystallization ................................................................................ 102
5.3 Discussion .......................................................................................... 104
CHAPTER 6. CONCLUSION AND FUTURE DIRECTIONS .............. 105
6.1 Conclusion ..................................................................................... 105
6.1.1 Siah2 inhibition as anti-cancer therapy ...................................... 105
6.2 Future directions ............................................................................ 107
APPENDICES .............................................................................................. 109
REFERENCES ............................................................................................. 114
viii
SUMMARY
The Siah ubiquitin ligases play an important role in a number of
signaling pathways in cancer progression and metastasis. One such critical
pathway regulated by Siah2 in cancer is the hypoxia inducible factor 1α (Hif-
1α) dependent pathway, which gets activated in response to hypoxia. The
domain architecture of Siah2 consist of a ‘really interesting new gene’ (RING)
domain at its N-terminus, followed by two novel zinc-finger motifs and a
highly conserved substrate binding domain (SBD) at its C-terminus. The
substrate binding domain (SBD) of Siah2 is of importance in the induction of
the Hif-1α dependent pathway by regulating prolyl hydroxylase 3 (PHD3), a
well-studied substrate of Siah2 under hypoxia. Siah2 is also found to be
involved in a Hif-1α independent pathway by regulating sprouty2 (SPRY2) via
the Ras/ERK pathway. Since these pathways are critical in human cancers,
various studies have suggested that Siah2 may be a suitable target for
developing inhibitors.
This study has three aims: the first is to identify specific drug like lead
molecules towards the SBD of Siah2 using in silico high throughput virtual
screening (HTVS); the second is to validate the identified compounds using in
vitro and in vivo binding assays and the third is to identify the critical residues
involved in Siah2 substrate specificity.
Firstly, we prepared a structure of the SBD of Siah2 using a homology
modeling approach, followed by molecular dynamics simulation in order to
analyze the stability of the model. Eventually, we applied in silico structure-
based HTVS and identified 5 ligands from the Maybridge database which
interact with the SBD of Siah2. Menadione, a compound that has earlier been
identified and characterized as a Siah2 inhibitor was used as a reference ligand
for screening and selection of lead molecules.
Secondly, since Siah2 is a non-catalytic protein, further experimental
validation was performed targeting protein-protein interaction with partners.
We experimentally validated the computationally identified potential and
selective Siah2 inhibitors in vitro and in vivo through binding assays. The
ix
results obtained show that the identified drug-like small molecules could
partially inhibit protein-protein interaction. In addition, the compounds also
possess anti proliferative and cytotoxic activity. Furthermore the backbone
structural scaffold of these inhibitors could serve as building blocks in
designing suitable drug-like molecules for cancer.
The unique role of Siah1 and Siah2 is still unclear. Siah1 and Siah2
have their respective substrates as well as several common substrates. Our
third study explores the substrate specificity of Siah1 and Siah2. We intend to
identify the critical region or residues of Siah2 that confer substrate
specificity. We have identified a few key residues that might play a crucial
role in the binding of selective Siah2 substrates. Further mutational studies are
being carried out to identify the specific residues involved in binding. This
study might help in further understanding of the detailed roles of Siah1 and
Siah2 and in designing more specific Siah based therapeutic strategies.
x
LIST OF TABLES
Table 1. Oncogenic role of Siah2 in various cancers and their respective
effects .............................................................................................. 11
Table 2. List of primers and corresponding template used for fusion PCR .. 46
Table 3. Ramachandran Plot statistics on 3D model of SBD of Siah2
calculated using PROCHECK ......................................................... 55
Table 4. Glide extra-precision (XP) results for the five lead molecules and
menadione using Schrodinger 9.0. .................................................. 60
Table 5. QikProp properties of the identified drug-like molecules and
menadione using Schrodinger 9.0. .................................................. 61
Table 6. Thermodynamic parameters for interaction between SBD of Siah2
and PHD3 or compounds................................................................. 76
Table A1. List of constructs and corresponding primers .............................. 109
xi
LIST OF FIGURES
Figure 1. The ubiquitin proteolytic pathway. .................................................... 3
Figure 2. Domain architecture of Siah. ............................................................. 6
Figure 3. Outline of Siah regulation and function............................................. 9
Figure 4. Hypoxic signaling connected to Siah2. ........................................... 14
Figure 5. Sequential procedure and steps involved in traditional drug
development ..................................................................................................... 18
Figure 6. Integration of structural and informatics methods ........................... 25
Figure 7. A docked pose of a ligand and protein receptor in its active site .... 29
Figure 8. Prediction of best-fit ligand to the binding site of the target by
scoring function. .............................................................................................. 30
Figure 9. Schematic flow of virtual screening process. .................................. 33
Figure 10. Docking pose of zanamivir (Relenza) bound to influenza
neuraminidase .................................................................................................. 34
Figure 11. Modeled structure for SBD of Siah2 obtained from Modeller9v7.
.......................................................................................................................... 53
Figure 12. Model validation ............................................................................ 54
Figure 13. Total energy calculated by MD Simulation. .................................. 56
Figure 14. Diagrammatic representation of the 13 predicted active site
residues of SBD. .............................................................................................. 57
Figure 15. Chemical structure of the five lead molecules. .............................. 58
Figure 16. Binding poses of the five lead molecules and menadione to SBD of
Siah2. ............................................................................................................... 59
Figure 17. Coimmunoprecipitation of Siah2 with PHD3. .............................. 66
Figure 18. GST pull down of SBD with PHD3. ............................................. 67
Figure 19. SDS-PAGE affinity purification profile of MBP-SBD of Siah2. .. 68
Figure 20. Gel filtration profile of purified MBP-SBD of Siah2. ................... 69
Figure 21. SDS-PAGE affinity purification profile of MBP-PHD3 ............... 70
Figure 22. The ion-exchange chromatography profile of PHD3. ................... 71
Figure 23. Gel filtration profile of purified MBP-PHD3. ............................... 72
Figure 24. ITC data for SBD with compounds and PHD3. ............................ 75
xii
Figure 25. Coimmunoprecipitation of SBD with PHD3 in the presence of the
compounds. ...................................................................................................... 78
Figure 26. GST pull down of SBD with PHD3 in the presence of compounds.
.......................................................................................................................... 80
Figure 27. Agarose gel showing the size of single PCR products generated by
Siah2 gene-specific primers for q-PCR ............................................................ 82
Figure 28. mRNA expression analysis of Siah2 in breast cancer cell lines. .. 82
Figure 29. Validation of knockdown of Siah2 in HEK293T and MCF7 cell
lines .................................................................................................................. 83
Figure 30. Rescue experiments using the Siah2 full length constructs. .......... 84
Figure 31. Analysis of endogeneous Siah2 protein expression and test for its
stability. ............................................................................................................ 85
Figure 32. Antibody validation using siRNA oligos. ...................................... 86
Figure 33. Cell proliferative effects of compounds in MCF7 cell line. .......... 88
Figure 34. Specificity of Compound 1 to Siah2 in MCF 7 cell line. .............. 89
Figure 35. Association of Siah1 with PHD3. .................................................. 95
Figure 36. Diagrammatic representation of SBD of Siah1 and Siah2. ........... 96
Figure 37. Association of wild type and chimeric SBD's of Siah with its
substrates. ......................................................................................................... 97
Figure 38. Sequence alignment of SBD of Siah1 and Siah2........................... 98
Figure 39. Representation of mutagenesis and distribution of unique amino
acids in chimeric forms of Siah........................................................................ 99
Figure 40. Siah2-1Mut restores its binding with its substrates. .................... 101
Figure 41. Analyses of Binding between mutated 2-1 and 1-2 chimeric SBD
........................................................................................................................ 102
Figure 42. DLS Profile of MBP-SBD of Siah2 at 10mg/ml. ........................ 104
Figure 43. Example illustration of steps involved in combinatorial chemistry
based design of ligands. ................................................................................. 108
Figure 44. Purification of PHD3-His by refolding........................................ 111
Figure 45. Identification of MBP-SBD of Siah2 and MBP-PHD3 by peptide
mass fingerprint. ............................................................................................ 112
xiii
LIST OF PUBLICATIONS
Anupriya G, Kothapalli R, Basappa S, Chong YS, Annamalai L: Homology
modeling and in silico screening of inhibitors for the substrate binding domain
of human Siah2: implications for hypoxia-induced cancers. Journal of
molecular modeling 2011, 17(12):3325-3332.
Kothapalli R, Khan AM, Basappa, Anupriya G, Chong YS, Annamalai L:
Cheminformatics-based drug design approach for identification of inhibitors
targeting the characteriztic residues of MMP-13 hemopexin domain. PloS one
2010, 5(8):e12494.
xiv
LIST OF SYMBOLS AND ABBREVIATIONS
G Gibbs free energy change
H Enthalpy change
S Entropy change
ADMET Adsorption, Distribution, Metabolism, Excretion and
Toxicity
APC Adenomatous polyposis coli
AR Androgen receptor
BLAST Basic local alignment search tool
C/EBP The transcription factor CCAAT/enhancer-binding
protein delta
CaCl Calcium chloride
CADD Computer aided drug design
cDNA Complementary DNA
CRPC Castration-resistant human prostate cancer
DCC Deleted in colorectal carcinoma
DCIS Ductal carcinoma in situ
DLS Dynamic light scattering
DMEM Dulbecco’s Modified Eagle Medium
DMSO Dimethyl sulfoxide
DNA Deoxyribonucleic acid
DSBC Disulfide bond isomerase
DTT Dithiothreitol
DUBs Deubiquitinating enzymes
E. coli Escherichia coli
ER Estrogen receptor
ERK Extracellular signal-regulated kinases
FBS Fetal Bovine serum
FoxA2 Forkhead box A2
FPLC Fast protein liquid chromatography
GI Geninfo identifier
GnHCL Guanidine hydrochloride
xv
GST Glutathione S-transferase
HCC Human hepatocellular carcinoma
HECT Homologous to E6-AP carboxy-terminus
HEK 293T Human embryonic kidney 293T
Hif-1α Hypoxia inducible factor 1α
HIPK2 Homeodomain-interacting protein kinase 2
HTVS High throughput virtual screening
IP Immunoprecipitation
IPTG Isopropyl β-D-1-thiogalactopyranoside
ITC Isothermal titration calorimetry
Ka Association constant
Kd Dissociation constant
kDa kilo Daltons
Ki Inhibition constant
LB Luria-bertani broth
MAPK Mitogen-activated protein kinase
MBP Maltose Binding Protein
MD Molecular Dynamics
mRNA Messenger ribonucleicacid
MS Mass spectrometry
MTD Maximum tolerance dose
MTS 3-(4,5-dimethylthiazol-2-yl)-5-(3-
carboxymethoxyphenyl)-2-(4- sulfophenyl)-2H-
tetrazolium, inner salt
NaCl Sodium chloride
NCBI National centre for biotechnology information
NcoR Nuclear receptor co-repressor 1
NED Neuroendocrine differentiation
Ni-NTA Nickel-nitrilotriacetic acid
NMR Nuclear magnetic resonance
Nrf2 Nuclear factor (erythroid-derived 2)-like 2
OD Optical density
OGDC-E2 2-oxoglutarate (also known as α-ketoglutarate
xvi
dehydrogenase complex)
OPLS-AA Optimized potentials for liquid simulations all-atom
PBS Phosphate buffered saline
PCa Prostate adenocarcinomas
PCR Polymerase chain reaction
PDB Protein data bank
Pdfs Probability density functions
PHD Prolyl hydroxylases
PHYL Phyllopod
pI Isoelectric point
PSI-BLAST
Position-specific iterative-Basic local alignment
search tool
RING Really interesting new gene
RMSD Root mean square deviation
RNA Ribonucleic acid
RPMI -1640 Roswell park memorial institute-1640 medium
RT-PCR Real-time polymerase chain reaction
SBD Substrate binding domain
SBDD Structure based drug design
SDS-PAGE
Sodium dodecyl sulfate polyacrylamide gel
electrophoresis
SEC Size exclusion chromatography
SIAH Seven in absentia homologue
SINA Seven in absentia
SIP Siah interacting protein
SPRY2 Sprouty2
TBS Tris buffered saline
TGF Tumor growth factor
TIEG-1 TGF-beta induced early gene-1
TNFα Tumor necrosis factor alpha
TRAF2 Tumor necrosis factor (TNF) receptor associated
factor
Tris Trisaminomethane
xvii
Tris HCl Tris (hydroxymethyl) aminomethane
TTk Tramtrack
UBC Ubiquitin-conjugating enzyme
UPP Ubiquitin proteasome pathway
VDW Van der Waals
WT Wild type
ZnF Zinc fingers
1
CHAPTER 1. INTRODUCTION
1.1 Biological background
1.1.1 The importance of regulated protein degradation
In eukaryotes, intracellular protein degradation is a highly selective
process, whereby proteins are broken down into their constituent amino acids
at widely differing rates. This selective process impacts a range of cellular
functions and serves several important homeostatic purposes. For example,
gene expression patterns and developmental programs can be altered via
coordinated destruction of transcriptional regulatory proteins (Conaway,
Brower et al. 2002). Additionally, aberrant proteins that are mutated, damaged,
or mis-folded and fail to degrade may pose a threat to cellular integrity. This is
essential in protecting cells against environmental stress. The function and
dysfunction of protein degradation have been implicated in the pathogenesis of
many diseases including numerous cancer, neurodegenerative, immunological
and several other disorders (Schwartz and Ciechanover 1999). Understanding
the mechanisms involved in this process are therefore important for
understanding and treating human disease.
The intracellular degradation of protein is mainly achieved in two
ways. First is by a lysosomal pathway, which is normally a non-selective
process. The second and the major pathway is the ubiquitin proteasome
pathway (UPP), a selective process which involves three phases. Proteins
marked for degradation are covalently linked to ubiquitin known as
Ubiquitination and the polyubiquinated protein is targeted to an ATP-
dependent protease complex, the proteasome, for degradation and the
ubiquitin is released (termed deubiquitination) and reused.
1.1.2 Ubiquitination and protein degradation
Ubiquitin is a highly conserved, small protein of 76 amino acids and
has a molecular mass of about 8.5 kDa. It acts as a tag for proteins to signal
degradation, subcellular localization, membrane trafficking, DNA repair and
chromatin dynamics (Murata, Yashiroda et al. 2009). The key feature of
ubiquitin includes 7 lysine residues through which it forms a polymer by
2
forming covalent linkages through any one of the lysine residues.
Polyubiquitin linkages through lys48 and lys63 are well-characterized. The
substrates with lys48 linkages undergo degradation, whereas Lys63 linked
chains tend to be directly involved in signal transduction (Thrower, Hoffman
et al. 2000). The functional consequences of other ubiquitin linkages remain
unclear, and current research aims to determine how those lysine linkages
affect the protein substrates.
The ubiquitination reaction requires at least three enzymes: an E1
ubiquitin activating enzyme, an E2 ubiquitin conjugating enzyme, an E3
ubiquitin ligase that functions in a hierarchical system that allows ubiquitin to
be activated and become covalently linked to protein substrates (Hershko
1996; Hershko and Ciechanover 1998). This conjugation process is reversible,
and ubiquitin is removed from substrates by deubiquitinating enzymes
(DUBs). An E4 ubiquitin chain assembly factor may be required if the E3
ubiquitin ligase is unable to polyubiquitinate a protein substrate (Koegl, Hoppe
et al. 1999).
Protein modification by ubiquitin also has non-conventional (non-
degradative) functions that are dictated by the number of ubiquitin units
attached to proteins (mono versus polyubiquitination). Monoubiquitination is
the attachment of a single ubiquitin molecule to a substrate and is implicated
in many cellular functions including protein trafficking between various
cellular compartments, DNA repair and transcriptional regulation (Hicke and
Dunn 2003). Polyubiquitination involves the addition of several ubiquitin
molecules to the first ubiquitin moiety. These polyubiquitin chains target
proteins for destruction, by a process known as proteolysis by the multiprotein
complex 26S proteasome (Fig. 1).
The 26S proteasome is a 2.5 MDa complex that is made up of two
components: a 20S core particle and the 19S regulatory particle (Hanna and
Finley 2007). The 20S core particle is a barrel-shaped complex that consists of
seven proteins which are responsible for the peptidolytic activity of the
proteasome. The 19S component is the regulatory subunit that can be further
subdivided into two sub complexes the base and the lid. The base aids in
substrate unfolding prior to degradation and is located proximal to the core
particle; unfolding of the protein is required since the barrel of the 20S
3
proteasome core particle is too small to degrade folded proteins. The lid of the
regulatory subunit is involved in substrate recognition (Braun, Glickman et al.
1999).
Figure 1. The ubiquitin proteolytic pathway. 1: Activation of ubiquitin by
the ubiquitin-activating enzyme E1, a ubiquitin-carrier protein, E2 (ubiquitin-
conjugating enzyme, UBC), and ATP. The product of this reaction is a high-
energy E2∼ubiquitin thiol ester intermediate. 2: Binding of the protein
substrate, via a defined recognition motif, to a specific ubiquitin-protein ligase,
E3. 3: Multiple (n) cycles of conjugation of ubiquitin to the target substrate
and synthesis of a polyubiquitin chain. E2 transfers the first activated ubiquitin
moiety directly to the E3-bound substrate, and in following cycles, to
previously conjugated ubiquitin moiety. Direct transfer of activated ubiquitin
from E2 to the E3-bound substrate occurs in substrates targeted by RING
finger E3s. 3′: As in3, but the activated ubiquitin moiety is transferred from E2
to a high-energy thiol intermediate on E3, before its conjugation to the E3-
bound substrate or to the previously conjugated ubiquitin moiety. This reaction
is catalyzed by HECT domain E3s. 4: Degradation of the ubiquitin-tagged
substrate by the 26S proteasome complex with release of short peptides. 5:
Ubiquitin is recycled via the activity of deubiquitinating enzymes (DUBs).
Adapted from (Glickman and Ciechanover 2002).
4
1.1.3 E3 ubiquitin ligases
The roles of E3 ubiquitin ligases can be broadly characterized as
substrate recognition and polyubiquitin chain formation with the aid from E2
enzyme. E3 binds to a substrate via the degron – the ubiquitination signal,
conferring substrate specificity, and E3 also catalyzes the ligation of one or
more ubiquitins to the substrate (Pickart 2001) . In the human there are about
600 E3 ubiquitin ligases. However the detailed molecular mechanisms of
protein selection are poorly understood and the exact protein substrates for
each E3 are mostly unknown.
Generally, E3 ubiquitin ligases can be classified into three main types
based on their structure and mechanism of action. These include the HECT
(homologous to E6-AP carboxy-terminus) domain family, the U-box family
and the RING (really interesting new gene) finger family (Jackson, Eldridge et
al. 2000). The HECT domain proteins are large monomeric E3’s which
contain a cysteine residue that forms a covalent bond with the activated
ubiquitin before transferring it to the substrate (Scheffner, Nuber et al. 1995).
The RING and U-box E3 ligases however do not possess an active-site
cysteine residue and these E3’s simply serve as a scaffold, bringing the E2-
ubiquitin complex and substrate in close proximity to mediate the transfer of
ubiquitin from the E2 enzyme directly to the substrate.
The majority of E3 ubiquitin ligases are members of the RING finger
family and over 600 human genes encoding RING finger E3s have been
identified (Deshaies and Joazeiro 2009), The RING finger constitutes a small
zinc-binding domain that binds to specific E2 enzymes (Lorick, Jensen et al.
1999; Joazeiro and Weissman 2000). The U-box E3s have a similar tertiary
structure to that of the RING finger except that the U-box scaffold is stabilised
by salt-bridges and hydrogen bonds rather than zinc ions (Aravind and Koonin
2000; Hatakeyama and Nakayama 2003).
5
1.1.4 The Siah family of proteins
SIAH (Seven in Absentia Homolog) is a mammalian homolog of
Seven in Absentia (SINA), a Drosophlila protein which was the first of the
family to be defined that has a function in eye development (Carthew and
Rubin 1990). The Siah family of proteins are evolutionarily conserved E3
ubiquitin ligases containing an N-terminal RING-finger domain required for
interaction with E2 ubiquitin conjugating enzymes, and a C-terminal substrate
binding domain (SBD) which interacts with target proteins (Hu, Chung et al.
1997). Specific target protein degradation via the ubiquitin–proteasome
pathway is accomplished through the N-terminal RING domain of Siah
proteins (Hu and Fearon 1999). Drosophila SINA was first isolated in a
screen for mutations which affect the morphology of the Drosophila eye and
SINA targets the transcriptional repressor, tramtrack 88 (TTK88) for
degradation by cooperating with phyllopod (PHYL) and is essential for the
correct development of R7 photoreceptor cells in the compound eye(Carthew,
Neufeld et al. 1994). Another Drosophilia protein Sina-homologue, SinaH
(46% identical to SINA) was recently identified (Cooper, Murawsky et al.
2008). Characterization of murine Sina homologues (Della, Senior et al. 1993)
revealed that the mouse genome contains a family of five Siah genes, located
at unlinked chromosomal positions (Holloway, Della et al. 1997). Two of
these genes, Siah1-ps1 and Siah1-ps2, are pseudogenes as they contain a
number of frame shifts and in-frame stop codons within the expected coding
region. However, the remaining three genes, Siah1a, Siah1b and Siah2 have
open reading frames and are expressed.
In contrast, only two SINA homologues have been identified in the
human genome, Siah1 and Siah2 (Nemani, Linares-Cruz et al. 1996), both of
which encode functional proteins.
6
a
b
1.1.5 Siah Structure
Figure 2. Domain architecture of Siah. (a) Siah 1 domain organization
containing structurally characterized SBD. (b) Siah2 domain organization
containing structurally uncharacterized SBD.
Structurally, the Siah family has a divergent N-terminal domain, and a
highly conserved C-terminus, SBD that encompasses two Zinc fingers (ZnF)
(Fig. 2). So far three SBD crystal structure of Siah1 have been solved and
listed as follows.
1. The crystal structure of the SBD domain of murine Siah-1a was determined,
and it shows that the cysteine-rich region forms a pair of zinc fingers and that
the C-terminal region self-dimerizes. It was found that the SBD adopts eight-
stranded β-sandwich fold that is homologous to tumor necrosis factor (TNF)
receptor associated factor (TRAF) proteins (Polekhina, House et al. 2002).
Later studies showed that Siah2, via degradation of TRAF2, was a regulator of
TRAF2 signaling (Habelhah, Frew et al. 2002).
It has been reported that many Siah binding proteins contain a common
binding motif that may act as a degradation signal. This core sequence,
PxAxVxP, often referred to as the ‘degron motif’, was found to be conserved
and functional in a number of Siah-interacting proteins (examples such as SIP,
TEIG-1, DCC). Other Siah interacting proteins, including KID, APC,
synphylin-1, carry similar peptides that do not perfectly match the reported
consensus degron motif (Dinkel, Michael et al. 2011). High affinity peptides
derived from phyllopod and plectin have been shown to bind very strongly to
Siah1 with a Kd of ~100 nM and to compete effectively with a range of Siah-
binding proteins, including SIP (House, Frew et al. 2003)
2. The crystal structure of SBD of human Siah1 bound to a degron motif
containing peptide from SIP, was determined (Santelli, Leone et al. 2005). The
7
peptide binds to SBD with a kd of 24 ± 4µM. This study reported two acid
patches on SBD of Siah1 which includes the residues Glu161, Asp162,
Glu226 and Glu237 in the shallow groove, forms salt bridges with SIP
peptide.
3. The crystal structure of SBD of human Siah1 bound to a degron motif-
containing peptide from PHYL was determined and reveals a binding site for
Siah-interacting proteins (House, Hancock et al. 2006). This site is distinct
from those previously hypothesized as protein interaction sites. Various
interacting residues that interact with the peptide were identified. Some
residues that are reported to bind to the peptide includes, Thr156, Leu158,
Asp162, Val164, Phe165, Leu166, Thr168, Ala175, Trp178, Asp177, Met180
and Asn276. Further mutations of the Siah1 residues that interact with the
degron motif abrogate binding to the peptide. Mutations were performed based
on the crystal structure of the SBD-peptide complex as a guide. Mutation of
Met180Lys in the hydrophobic pocket abolished the binding of the target to a
greater extent by Siah underlying the importance of the hydrophobic
interaction observed in the crystal structure, and suggesting that it is a key
recognition point. However other mutations in strands β0 and β1 do not have
any pronounced effect as Met180.
These Siah structures reveal a fold that has not been seen in other E3
structures (Schulman, Carrano et al. 2000; Zheng, Wang et al. 2000). To date
no crystal structure of Siah2 has been determined although both Siah1 and
Siah2 are expected to have a similar fold because of high sequence similarity.
The domain structure of Siah2 is also similar to Siah1
8
1.1.6 Diverse role and substrates of Siah
The Drosophila version of this protein, Sina is required for targeting
the TTK88 for degradation , an essential process for neuronal differentiation
of R7 photoreceptor cells in the developing fly eye (Tang, Neufeld et al.
1997). In mammals the substrates targeted for degradation are quite diverse.
The role of the RING finger E3 ubiquitin ligase Siah is implicated in the
control of diverse cellular biological events. The Siah proteins regulate
ubiquitination-dependent degradation of multiple substrates, including
transcriptional repressors NcoR/TIEG-1, activators β-catenin, netrin receptor,
a microtuble-associated motor protein (Kid), TRAF2, OGDC-E2, PHD3,
SPRY2, HIPK2, Nrf2, DCC and other proteins thus influencing an array of
regulatory functions such as angiogenesis, DNA damage response,
mitochondrial dynamics and Ras- and estrogen-receptor (ER) signaling (Fig.
3). More than 30 substrates of the Siah ubiquitin ligases have been identified
to date and have been reviewed (Nakayama, Qi et al. 2009; Qi, Kim et al.
2013). It can directly mediate or bind to adaptor proteins to mediate
degradation. For example, substrates such as transforming growth factor beta
TGFβ, interacts directly with Siah (Johnsen, Subramaniam et al. 2002)
whereas the interaction with substrates such as β-catenin and TTK88 are
mediated through the adaptor proteins SIP/APC and PHYL (Matsuzawa and
Reed 2001). As described earlier, SIP and PHYL peptides engage the human
Siah SBD in a near identical manner with an apparent 100-fold higher affinity
for the PHYL degron peptide than the SIP degron peptide (Santelli, Leone et
al. 2005; House, Hancock et al. 2006).
In addition to substrate ubiquitination, Siah1 and Siah2 can also self or
auto-regulate their own stability in the presence of an E2 enzyme, a property
that may be used as an auto-regulatory mechanism to control its own
intracellular levels (Hu and Fearon 1999; Depaux, Regnier-Ricard et al. 2006).
Siah2 mutant mice under normal conditions are fertile and phenotypically
normal whereas Siah2 mutants under stress conditions show marked
phenotypes, suggesting central role for it in maintaining normal cellular
homeostasis and response to stress (Frew, Hammond et al. 2003). It is
9
interesting to note that most signaling pathways regulated by Siah2 are
deregulated in human cancer.
Figure 3. Outline of Siah regulation and function. Factors involved in the
regulation of Siah ligases are associated with stress response, including
hypoxia, ER stress and genotoxic stress, which induce respective transcription
factors, microRNA to induce Siah2 transcription, as well as post-translational
modifications that determine Siah subcellular localization and activity.
Consequently, Siah activities as an E3 ubiquitin ligase affects growing number
of substrates associated with fundamental cellular processes including
hypoxia, DNA damage response, neurodegeneration and cancer (Qi, Kim et al.
2013).
10
1.1.7 Siah1 and cancer
Studies revealed that Siah1 plays an important role in regulating cell
proliferation and cell apoptosis. Role of Siah1 in cancer is poorly understood.
In this regard, most of the studies point Siah1 towards tumor suppressive role
in contrast to oncogenic role of Siah2. Some of the early studies have reported
Siah1 as a tumor suppressor that causes growth arrest or has pro-apoptotic
properties (Matsuzawa, Takayama et al. 1998). It is also involved in the
degradation of the oncogene β-catenin, via interactions with adenomatous
polyposis coli (APC) (Liu, Stevens et al. 2001) and Siah-interacting protein
(SIP) (Matsuzawa and Reed 2001) . So far there is only limited evidence to
support this role
The expression levels of Siah1 are reported to be reduced or down
regulated in various cancers. Also inhibition or low levels of Siah1 has been
shown to reduce apoptosis, thereby promoting cancer progression (Kim, Cho
et al. 2004; Brauckhoff, Ehemann et al. 2007; Yoshibayashi, Okabe et al.
2007; Wen, Yang et al. 2010).
1.1.8 Role of Siah2 in cancer
In contrast to the role of Siah1 in cancer, Siah2 in cancer is well
characterized. Growing evidences highlight the functional role and supporting
inhibition studies of Siah2 in the progression of multiple types of cancer,
including breast (Sarkar, Sharan et al. 2012; Wong, Sceneay et al. 2012), lung
(Ahmed, Schmidt et al. 2008), pancreatic (Schmidt, Park et al. 2007), prostate
(Qi, Nakayama et al. 2010; Qi, Tripathi et al. 2013), liver (Malz, Aulmann et
al. 2012) and melanoma (Qi, Nakayama et al. 2008). Additionally, Siah2
protein levels are significantly increased in the ductal carcinoma in situ
(DCIS) tumor tissue in high-grade breast cancer (Behling, Tang et al. 2011;
Wong, Sceneay et al. 2012), lung cancer (Ahmed, Schmidt et al. 2008), and
prostate cancer (Qi, Nakayama et al. 2010), compared with its expression in
the corresponding low-grade cancers or in normal tissues (Table 1).
11
Table 1. Oncogenic role of Siah2 in various cancers and their respective
effects. (Wong and Moller 2013)
Cancer
type
Studies Role
Bre
ast
can
cer
Mouse model of
breast cancer
Inhibition of Siah2 with PHYL reduced tumor
growth (Moller, House et al. 2009)
Mouse model of
spontaneous
breast cancer
Siah2-knockout mice show delayed breast
tumor onset, reduced stromal infiltration, and
altered tumor angiogenesis (Wong, Sceneay
et al. 2012)
Human breast
cancer DCIS
tissue
High Siah expression predicts DCIS
progression to invasive breast cancer
(Behling, Tang et al. 2011)
Human breast
cancer tissue
Siah2 upregulation in basal-like breast
cancers (Chan, Moller et al. 2011)
Human cell line
Src/Siah2 mediates degradation of C/EBP, a
tumor suppressor protein (Sarkar, Sharan et
al. 2012). Inhibition of Siah2 in fibroblasts
and in the non-transformed mammary
epithelial MCF10A cells confers resistance to
the transforming activity of Ras and Src
oncogenes, respectively
Human cell line Estrogen increases Siah2 levels in estrogen
receptor ER positive breast cancer cells,
resulting in degradation of the nuclear
receptor corepressor 1 (N-CoR) and a
reduction in N-CoR–mediated repressive
effects on gene expression, thus promoting
cancer growth (Frasor, Danes et al. 2005).
Mel
an
om
a
Mouse model of
melanoma
Inhibition of Siah2 activity reduces
melanoma progression via HIF dependent and
independent pathways (Qi, Nakayama et al.
2008)
Mouse model of
melanoma
Siah2 inhibition with vitamin K3 blocks
melanoma progression by disrupting hypoxia
and MAPK signaling (Shah, Stebbins et al.
2009)
Pro
state
Can
cer
Mouse model of
spontaneous
prostate cancer
and Human
prostate cancer
tissues
Reduced tumor progression and metastasis in
Siah2-null prostate cancer mice (Siah2-
dependent regulation of Hif and FoxA2
interaction). Increased Siah2 expression in
human NE prostate tumor samples (Qi,
Nakayama et al. 2010)
12
The above studies support an oncogenic role of the Siah2 protein in
promoting cancer and suggest that two main pathways are associated with it,
for its activity: the hypoxic response pathway (prostate, melanoma, and breast
cancer models) and the Ras signaling pathway (lung, pancreatic, and
melanoma cancer models). The role of siah2 in these pathways are reviewed
by Moller (House, Moller et al. 2009).
1.1.8.1 Hypoxia signaling connected to Siah2 in cancer
Growth of solid tumors is associated with a poor oxygen supply
(hypoxia) within the tumor mass, triggering a hypoxic response necessary to
recruit new vasculature resulting in tumor cell survival (Seta, Spicer et al.
2002). HIF, the master regulator of hypoxia responses, has been implicated in
cancer. HIF consists of a heterodimer between α-subunit (HIF-1α or HIF-2α)
and β-subunit (HIF-1β). In a normoxic condition, the HIF-α level is regulated
by the PHD (prolyl-hydroxylase) mediated hydroxylation of two proline
residues that leads to VHL-dependent degradation (Maxwell, Wiesener et al.
Mouse model
of prostate
cancer, Human
prostate cancer
tissue and cell
line
Siah2 expression is upregulated in Castration-
Resistant Human Prostate Cancer [CRPC]
tissues. Saih2 mediates the degradation of
NCOR1-bound, transcriptionally-inactive AR
(Androgen Recepor) there by promoting
expression of AR target genes, a key player in
CRPC. Knockdown of Siah2 or AR
significantly reduced proliferation of Rv1 and
LNCaP cells. Inhibition of Siah2 promotes
prostate cancer regression upon castration.
(Qi, Tripathi et al. 2013)
Lu
ng
Can
cer
Xenograft
mouse model of
lung cancer
Disrupting Siah2 function in lung cancer
induced apoptosis and prevented tumor
growth (Ahmed, Schmidt et al. 2008)
Pan
crea
tic
Can
cer Xenograft
mouse model of
pancreatic
cancer
Siah mediates Ras signaling in pancreatic
tumor progression (Schmidt, Park et al. 2007)
Liv
er C
an
cer Human HCC
tissue and cell.
Nuclear accumulation of Siah2 enhances
motility and proliferation of liver cancer cells
(Malz, Aulmann et al. 2012). Inhibition of
Siah2 expression also directly sensitizes
human hepatocellular carcinoma (HCC) cells
to treatment with cytostatic drugs
13
1999). However under a hypoxic condition HIF-1α triggers a response that
includes the expression of genes like VEGF necessary to recruit new blood
vessels thus playing an important role in promoting tumor cell survival. A
cancer response to hypoxia not only sustains tumor growth and survival, but
through angiogenesis it fosters invasion and metastasis (Brizel, Scully et al.
1996). This pathway is well reviewed in (Semenza 2007).
Studies provide a link between Siah2 and HIF in tumor development
and progression (Qi, Nakayama et al. 2008; Qi, Nakayama et al. 2010). Siah2
is subjected to phosphorylation, which increases its ubiquitin targeted
degradation activity under hypoxia conditions through mitogen-activated
protein kinase (MAPK) p38 signaling cascade (Khurana, Nakayama et al.
2006) . Essentially, Siah2 is induced under hypoxic conditions, that control
the stability of prolyl hydroxylases, PHD3 and PHD1 (Nakayama, Frew et al.
2004), resulting in proteasomal degradation of hydroxylases and impairment
of HIF-1α (Qi, Nakayama et al. 2008), which is essential for HIF-1a’s
association with and ubiquitination by pVHL (Ivan, Kondo et al. 2001).
Another study reports that Siah2 controls the levels and activity of
HIF-1α, which cooperates transcriptionally with FoxA2 to promote NE tumor
development or formation of NED of human prostate cancers. However, in
this study the signaling pathway of how Siah2 regulates HIF-1α is not
described (Qi, Nakayama et al. 2010).
Thus the studies in various mouse models show that Siah2 indirectly
controls HIF-1α abundance, and is known to have a potential role in tumor
development. Furthermore results showed that Siah2 seems to promote
vasculogenesis, cell growth and metastasis (Fig. 4) (Qi, Nakayama et al.
2008).
Siah2 knockout mice have a delayed and abrogated response to
hypoxic conditions. At a cellular and tissue level, exposure of Siah2 mutants
to hypoxia leads to significantly lower protein levels of HIF-1α, resulting in
reduced hypoxia-induced gene expression (Nakayama, Frew et al. 2004).
Consistent with this, a number of studies have shown that inhibition of Siah2
activity reduces HIF-1α levels under hypoxic conditions that subsequently
blocks the formation of tumors and reduced metastasis (Qi, Nakayama et al.
14
2008; Moller, House et al. 2009; Shah, Stebbins et al. 2009; Qi, Nakayama et
al. 2010).
In addition, Siah2 has also been shown to regulate the stability of
HIPK2 (Calzado, de la Vega et al. 2009), a transcriptional repressor of the
HIF-1α gene (Nardinocchi, Puca et al. 2009). HIPK2 directly phosphorylates
Siah2 resulting in a disruption of the HIPK2/Siah2 interaction, thus stabilizing
HIPK2 and promoting apoptosis.
Figure 4. Hypoxic signaling connected to Siah2. Under the hypoxia
condition, hypoxia-activated p38 and Akt pathways positively regulate Siah2
activity through phosphorylation and induction, respectively. Siah2 is
phosphorylated by p38 on its serine and threonine residues, which will
enhance its activity. Akt pathway increases the abundance of Siah2 by
induction of its mRNA through signaling pathways yet to be clarified. Induced
and activated Siah2 modulates the hypoxic signaling through PHD3
degradation and HIF-1α stabilization on one hand. Although, it is not yet
identified, other Siah2 substrates would also play roles on hypoxic signaling
by both HIF-1α-dependent and -independent mechanism. Adapted from
(Nakayama, Qi et al. 2009).
1.1.8.2 RAS signaling connected to Siah2 in cancer
Siah2 enhances the MAPK-ERK signaling pathway, primarily through
direct ubiquitination-dependent modulation of Sprouty2 (SPRY2), a Ras
15
inhibitory protein that negatively regulates the MAPK-ERK signaling
(Nadeau, Toher et al. 2007; Shaw, Meissner et al. 2007). Inhibition of Siah2
reduced Erk signaling, cell proliferation and increased apoptosis in various
cancers (Schmidt, Park et al. 2007; Ahmed, Schmidt et al. 2008; Qi,
Nakayama et al. 2008). In addition, attenuation of Sprouty2 expression
effectively restores phospho-ERK levels in Siah2-inhibited melanoma cells
and partially rescues tumor formation in vivo (Nadeau, Toher et al. 2007).
Inhibition of Siah2 activity by a dominant negative Siah2 RING
mutant in SW1 melanoma cells resulted in reduced tumor growth and
metastases by robustly upregulating the levels of SPRY2 a specific inhibitor
of Ras/ERK signaling. In contrast, the RING mutant inhibition of Siah1 has
only little effect on levels of PHD3 and HIF-1α in SW1 cells (Shaw, Meissner
et al. 2007). However inhibition of Siah2 substrate binding through PHYL (a
peptide inhibitor for Siah2) reduced only metastatic spread and not tumor
growth in melanoma cells growth via the hypoxic response pathway (Qi,
Nakayama et al. 2008; Moller, House et al. 2009).
These studies suggest that Siah2 may act through both pathways
simultaneously or through different pathways at different stages of tumor
progression. Consistent with the important role of Siah2 in HIF and ERK
signaling, vitamin K3, a Siah2 inhibitor, reduces HIF and phospho-ERK levels
in a human melanoma cell line and inhibits tumor formation in the mouse
xenograft model (Shah, Stebbins et al. 2009). Taken together, these
observations provide a foundation for the development of Siah2-targeted
therapeutic agents for cancer therapy.
1.1.9 E3 ubiquitin ligases as a cancer target
E3 ubiquitin ligases regulate a variety of biological processes,
including cell growth and apoptosis, through the timely ubiquitination and
degradation of many cell cycle- and apoptosis-regulatory proteins. Abnormal
regulation of E3 ligases has been convincingly shown to contribute to cancer
development (Nakayama and Nakayama 2006). Thus, targeting E3 ubiquitin
ligases for cancer therapy has gained increasing attention, which is further
stimulated by the recent approval of a general proteasomal inhibitor,
bortezomib (Velcade, Millennium, MA), for the treatment of relapsed and
16
refractory multiple myeloma (Richardson, Mitsiades et al. 2006), as well as for
the discovery of a new class of proteasome inhibitors (Chauhan, Catley et al.
2005). Targeting a particular E3 ubiquitin ligase rather than the total
proteasome system is expected to increase specificity and limit toxicity. This
became even more blossomed after the approval of SMAC mimetics, a small
inhibitor for E3 IAP family (Feltham, Bettjeman et al. 2011).
1.1.10 Protein-Protein Interactions as drug targets
Protein-Protein interactions (PPIs) are essential for many biological
functions. Numerous investigations in genomics, proteomics and
bioinformatics have provided convincing experimental evidence that PPIs
participate in networks of interactions that may also involve other biological
macromolecules (Schwikowski, Uetz et al. 2000; Schächter 2002). PPIs are
increasingly attracting attention as a new frontier in drug development,
particularly where they are involved in the regulation of cellular function and
disease states. The advantage of targeting PPIs is mainly the biological
specificity of inhibition (Valkov, Sharpe et al. 2012). It might, therefore, be
possible to limit the adverse effects of a drug by selectively targeting one of
the many functions the protein might have.
17
1.2 Drug Development
1.2.1 Introduction to drug discovery
The search for new, effective and safe drugs has become increasingly
sophisticated. Two pronounced characteriztics marked the modern age of the
pharmaceutical industry: “competitiveness” and “high cost”. Driven by the
high exclusive marketing profit, competition between pharmaceutical
companies is much more intensive than before (Drews and Ryser 1997).
“Innovate or die” is the first rule of international industrial
competition. The cost of discovering a new drug is becoming very high. The
initial statistics of drug discovery usually shows that it would take 12-15 years
and more than $1 billion to discover a new drug and this cost has been
growing at a rate of 20% per year (Heilman 1995; Yevich 1996). To alleviate
this problem, efforts have been directed to reduce the cost and the time span
needed for the discovery of a new drug. In consideration of the current patent
protection period of 20 years for new drugs, any advance in marketing a drug
more quickly is desirable. In addition to its great contribution to the
improvement of our life qualities, earlier marketing is also enormously
profitable.
1.2.2 Stages in drug development and the role of in silico approaches
The stages of drug development are simplified as follows,
1. Pre-Clinical trials (also known as Phase 0). This phase mainly
consists
Discovery phase: identification of target
Lead identification and optimization
Toxicology
2. Clinical development: Phase1, 2 & 3
3. Marketing
The hierarchical process of traditional drug discovery after the target is
identified is outlined in Fig. 8. As the preclinical stage normally takes about 6
years, and if it were to be shortened to 2 or 3 years, not only a big amount of
research and development funding could be saved, but also the more important
and exclusive clinical and marketing periods would be benefited. More and
18
more computer approaches are now being developed to reduce the cost and
cycle time for discovering a new drug. In silico approaches in drug discovery
and development are discussed in detail Section 1.14.
Figure 5. Sequential procedure and steps involved in traditional drug
development
Target Identification
The first step is to identify suitable biological targets, such as hormone,
receptor, enzyme, peptide and nucleic acid based on a thorough understanding
of regulatory networks and metabolic pathways. The second step is to design a
highly specific drug based on the known three-dimensional (3D) structure of
that target.
X-ray crystallography is the most powerful technique for determining
the three-dimensional structure of a protein but this can be a time-consuming
process, and it will succeed only if it is possible to find suitable conditions for
growing crystals. This can therefore easily become a bottleneck in drug
designing projects. Also, some proteins have highly flexible domains making
it difficult to crystallize them. Since the number of protein folds used by
Nature is limited, homologous proteins can be expected to have a similar fold
(Govindarajan, Recabarren et al. 1999). Hence homology based modeling,
19
which is discussed in section 1.14.1 is a practical and reasonable alternative to
crystallography.
Lead identification and optimization
The step of lead discovery is considered a bottle-neck of the drug
discovery process (Kenny, Bushfield et al. 1998; Langer and Hoffmann 2001).
In the past, leads were mainly discovered by random screening of a large
chemical library. The sources of chemicals can be diverse such as active
ingredients of natural products, derivatives of existing drugs, or even
randomly synthesized chemicals. Most large pharmaceutical companies have
their own libraries, which contain the chemicals accumulated from years of
efforts. It was reported that only one potential lead can be identified from
random screening of 5-10 thousand chemicals (Yevich 1996). Therefore, the
efficiency of mere screening is very low. The increasingly better
understanding of a drug-target interaction mechanism and rapid advances in
biochemistry and organic chemistry lead to the advent of computer aided drug
design (CADD), which aims to help the rapid and efficient discovery of drug
leads (Marshall 1987; Vedani 1991; Ooms 2000; Veselovsky and Ivanov
2003; Bienstock 2012; Chen 2013). The role of CADD in lead identification
and optimization has been discussed in Section 1.14
Toxicology
In pre-clinical treatment before administering to human, in vitro and in
vivo tests of a lead molecule are conducted. Acute and short term toxicity of
lead molecules are evaluated on animals. The physiological and biological
effects of lead molecules are observed and a molecule is absorbed, distributed,
metabolized and excreted in animals need to be addressed. The lethal dose
limits of a lead molecule is determined in this phase. Only one out of
approximately 5000-10000 molecules facing pre-clinical tests is usually
approved for marketing (Klees and Joines 1997).
Clinical trials involving new drugs are commonly classified into four
phases. Each phase of the drug approval process is treated as a separate
clinical trial.
20
Phase 1: Screening for safety. (human pharmacology)
Phase I studies are used to evaluate pharmacokinetic parameters and
tolerance, generally in healthy volunteers, normally about 20-80 individuals. It
is the first stage to test a drug on human subjects. The test usually starts with
very small doses and subsequently increased. Escalating doses of a drug are
administered to determine the maximum tolerance dose (MTD), which can
induce the first symptom of toxicity.
Phase 2: Establishing a testing protocol. (therapeutic exploratory)
After the Phase I trials, when the initial safety levels of a drug have
been confirmed, Phase II clinical studies are conducted on about 100 to 300
patients to assess the efficacy of a lead molecule and to determine how well
the drug works. Additional safety, clinical and pharmacological studies are
also included in this study. During this phase, effective dose, method of
delivery, safety and dose intervals of a lead molecule are established.
Phase 3: Final testing. (therapeutic confirmatory)
In Phase 3 trials, treatment is given to a large group of people (1,000-
3,000) to confirm the effectiveness of a drug, monitor side effects, compare it
to commonly used treatments, and collect information that will allow a drug to
be used safely.
The drug-development process will normally proceed through all four
phases over many years. If the drug successfully passes through Phases 1, 2,
and 3, it will usually be approved by the national regulatory authority of a
country for use in the general population.
Phase 4: Post approval studies. I
Phase 4 trials, post-marketing studies delineate additional information,
including the risk of treatment, benefits, and optimal use.
1.2.3 Computer-aided drug design (CADD)
Computer-aided drug design is typically carried out by
computationally docking sets of small molecular compounds and peptides into
a rigid model of the active site of a protein. In the past, docking methods were
21
primarily used to identify a small set (10–100), of molecules, which would be
active against a single target. More recently, docking methods have been used
to prioritize combinatorial libraries and create screening libraries focused on
specific gene families/macromolecular targets. Following are the approaches
involved in CADD.
1.2.3.1 Homology modeling
Homology modeling aims to build three-dimensional protein structure
models using experimentally determined structures of related family members
as templates based on the observation that the structures are more conserved
than the corresponding sequences (Lesk and Chothia 1980; Chothia and Lesk
1986). The active site can have very similar geometries, even for distantly
related proteins (Zvelebil, Barton et al. 1987; Raha, Wollacott et al. 2000). The
methodology involves four steps: template selection, sequence alignment,
model building and model validation (Brenner, Chothia et al. 1998; Al-
Lazikani, Jung et al. 2001).
Template identification and selection
Template selection starts with identifying one (or more) experimentally
determined structures (the template), which is likely to have structure
homology with the sequence of interest (the target). Heuristic search methods
such as BLAST (Altschul, Gish et al. 1990) and FASTA (Pearson and Lipman
1988), are often used for this as they are fast and accurate. In difficult cases
more sensitive fold recognition methods, which utilize techniques such as
hidden Markov methods, neural networks, iterated searches (e.g. PSI-BLAST
(Pearl, Martin et al. 2001)), and evolutionary information that offer high
relaiablility for the model and can be used to scan a structural database for
suitable templates (Edwards and Cottage 2003).
Target and template sequence alignment
Once the best template for modeling is identified, an optimal alignment
of the target and template must be made. The alignment created by a search
method or a fold recognition method may be sub-optimal with respect to
modeling. Different score matrices are needed in order to get an optimal
22
alignment for homology modeling as compared to fold recognition, possibly
because fold recognition needs to focus on conserved regions whereas
homology modeling needs to take all regions into account. The Smith-
Waterman algorithm uses dynamic programming to find an optimal alignment
between two sequences, given a scoring matrix and a gap model (Smith and
Waterman 1981).
If multiple templates were found with significant sequence similarity,
then the use of alignments based on multiple sequences is implied, as it
highlights evolutionary relationships, and increase the alignment accuracy
(Jaroszewski, Rychlewski et al. 2000). Several tools like ClustalX (Thompson,
Gibson et al. 1997), and T-Coffee (Notredame, Higgins et al. 2000) serve this
purpose. Other interesting methods include machine learning fast Fourier
transform, improved score matrices and new scoring functions have also been
developed to give a quantitative measure of alignment accuracy (Cristobal,
Zemla et al. 2001; Karwath and King 2002). If sequence similarity is low, then
structurally aligned templates are a good starting point to improve the
alignment quality (Yang 2002). DALI (Holm and Sander 1993) is an example
of the multiple structural and templates alignment templates.
Model building
Model building consists of three main steps which involves main-chain
building, loop modeling, basically de novo model building and side-chain
modeling, an optimization step. Building the core is based on the approaches
like rigid body superimposition that constructs the model from a few core
sections defined by the average of Cα atoms in conserved regions. Distance
geometry uses spatial restraints obtained from alignment. Segment matching
uses a database of short segments of a protein structure, with a combination of
energy and geometry rules (Deane, Kaas et al. 2001). Several programs are
available for homology modeling. SwissModel (Guex and Peitsch 1997) a
popular implementation of the rigid body approach is a commonly used online
tool. The backbone is rebuilt based on the positions of Cα atoms, using a
library of backbone elements derived from high quality Xray structures.
Homology modeling in MODELLER (Sali and Blundell 1993) is very
reliable and is performed based on satisfying spatial restraints. Distance and
23
dihedral angle restraints on the target structure are also generated, based on the
alignment to the template structure. Corresponding distances and angles
between aligned residues in the template and target structures are assumed to
be similar. Restraints on bond lengths, bond angles, dihedral angles and non-
bonded atom-atom contacts are also derived from statistical analysis of the
relationships between Cα atoms, solvent accessibilities and side-chain torsion
angles in known protein structures. The restraints are expressed as probability
density functions (pdfs). These pdfs are combined to give a molecular
function, which is optimized using a combination of energy minimization with
molecular dynamics and simulated annealing.
1.2.3.2 Molecular dynamics simulation
Molecular dynamics (MD) simulation is a computer simulation of the
physical movements of atoms and molecules which are allowed to interact for
a period of time. Computer simulations are also an alternative to complex
experiments to predict the thermodynamic and kinetic roles of amino acids in
proteins (Dokholyan, Buldyrev et al. 1998; Munoz and Eaton 1999).
Molecular dynamics investigates the motion of discrete particles described by
a potential energy function under external forces and quantifies them (Kandt,
Ash et al. 2007). With the knowledge of these forces it is possible to calculate
the dynamic behavior of the system using classical equations of motion. This
potential energy function that consists of a set of equations that empirically
describe bonded and non-bonded interactions between atoms is referred to as
“force field” (Ponder and Case 2003). The bonded interactions between atoms
are via covalent bonds, which typically includes bonds, bond angles, and
dihedrals. Non-bonded interactions are electrostatic interactions between the
partial charges on each atom and a Lennard-Jones potential to model
dispersive van der Waals interactions. Each molecule in the simulation is
described by its ‘topology’, the combination of the set of all atoms with their
non-bonded and bonded interaction parameters and the connectivity of those
atoms in the molecule's in silico MD simulations and provides a means to
facilitate the interpretation of experimental data.
On the other hand, the complexity and vast dimensionality of the
protein conformational space makes the folding time too long to be reachable
24
by computational studies at the atomic detail (Taketomi, Ueda et al. 1975;
Shakhnovich 1997). Simplified models became popular due to their ability to
reach reasonable time scales and to reproduce the basic thermodynamic and
kinetic properties of proteins (Privalov 1989). They are (i) unique native state
(NS), i.e. there should exist a single conformation with the lowest potential
energy (ii) cooperative folding transition (iii) thermodynamical stability of the
NS (iv) kinetic accessibility, i.e. the NS should be reachable in a biologically
reasonable time.
Simulation of complex systems of hundreds and millions of atoms is
achieved by GROMACS (see www.GROMACS.org), one of the fastest and
most popular molecular dynamics software packages. GROMACS was
designed primarily for simulations of nucleic acids, proteins, and lipids based
on non-bonding interactions. The software is not capable of simulations where
bonds are broken and reformed, such as chemical/enzymatic reactions. But for
situations where non-bonding interactions are expected to predominate,
GROMACS can provide information about the behavior of solute and solvent
molecules, and potentially support and explain experimental data.
1.2.3.3 Structure based drug design (SBDD)
A drug is most commonly a small organic molecule that activates or
inhibits the function of a biomolecule such as a protein which in turn results in
a therapeutic benefit to a patient. In most cases, drug designing involves the
development of small molecules that are complementary in shape and charge
to a biomolecular target with which they interact. (Cohen 1996). Structure-
based drug design relies on the knowledge of the three dimensional structure
of a biological target obtained through methods such as X-ray crystallography
or nuclear magnetic resonance (NMR) spectroscopy (Leach and Jhoti. 2007).
If an experimental structure of a target is not available, it may be possible to
create a homology model of the target, based on the experimental structure of
a related protein as discussed earlier in Section 1.14. In such cases, SBDD
frequently relies on computational techniques and involves iterative steps of
identifying new therapeutics based on a particular biological target (Madsen,
Krogsgaard-Larsen. et al. 2002). Using the structure of a biological target,
candidate drugs that are predicted to bind with high affinity and selectivity
25
may be designed using interactive graphics and the intuition of medicinal
chemistry. Alternatively various automated computational procedures may be
used to suggest new drug candidates. The complete iterative process of this is
illustrated in Fig. 6
Figure 6. Integration of structural and informatics methods. The diagram
illustrates how different computational approaches complement each other in
the context of structure-based drug design (Tang and Marshall 2011).
SBDD can potentially reduce the numbers of compounds that need to
be evaluated, and lead to new directions for synthetic optimization (Scapin
2006). Current methods for structure-based drug designing can be divided
roughly into two categories. The first category is about “finding” ligands for a
given target which is usually referred as database searching. In this case, a
large number of potential ligand molecules are screened to find those fitting
the binding pocket of the target. This method is usually referred as ligand-
based drug design. The key advantage of database searching is that it saves
synthetic effort to obtain new lead compounds. Another category of structure-
based drug design methods is about “building” ligands, which is usually
Experimental structure determination (X-ray crystallography, NMR)
3D protein structures (templates for model building)
Comparative modellingSequences of novel therapeutic targets
Virtual libraries (diverse compounds, chemical inventory)
3D structures of therapeutic targets
High-throughput docking (in silico of compounds on 3D structures of targets)
Structure-based library design (design of combinatorial libraries
taking 3D constraints of binding sites into account)
Hits
Structure-based analog design (design of modified hits using 3D protein-ligand complexes and computer simulations)
Leads
26
referred as receptor-based drug design. In this case, ligand molecules are built
up within the constraints of the binding pocket by assembling small pieces in a
stepwise manner. These pieces can be either individual atoms or molecular
fragments. The key advantage of such a method is that novel structures, not
contained in any database, can be suggested (R., Y. et al. 2000; Jorgensen
2004; Schneider and Fechner 2005).
1.2.3.4 Active site identification
Active site identification is the first step in this program. It analyses a
protein to assign a suitable drug binding pocket, derives key interaction sites
within the binding pocket, and then prepares necessary data for ligand-
fragment link. The basic inputs for this step are the 3D-structure of the protein
and a pre-docked ligand, as well as their atomic properties. Both ligand and
protein atoms need to be classified and their atomic properties should be
defined, basically, into four types: hydrophobic atoms (all carbons); H-bond
donor (oxygen and nitrogen atoms bonded to hydrogen atoms); H-bond
acceptor (oxygen and sp2 or sp hybridized nitrogen atoms with lone electron
pairs); polar atom (oxygen and nitrogen atoms that are neither H-bond donor
nor H-bond acceptor, sulphur, phosphorus, halogen, metal, and carbon atoms
bonded to hetero-atoms).
The space inside the ligand binding region would be studied with
virtual probe atoms of the above four types so that the chemical environment
of all spots in the ligand binding region can be known. This will suggest what
kind of chemical fragments can be put into their corresponding spots in the
ligand binding region of the target.
A typical in silico drug design cycle consists of docking, scoring and
ranking initial hits on the basis of their steric and electrostatic interactions with
the target site, which is commonly referred to as virtual screening.
1.2.3.5 Virtual screening
High-throughput screening is the most common experimental method
used to identify lead compounds. Because of the cost, time, and resources
required for performing high-throughput screening for compound libraries, the
use of alternative strategies is necessary for facilitating lead discovery. Virtual
27
screening has been successful in prioritizing large chemical libraries to
identify experimentally active compounds, serving as a practical and effective
alternative to high-throughput screening.
Virtual screening is a computational method for identifying lead
compounds from large and chemically diverse compound libraries from public
or specifically compiled databases of hundreds of thousands of organic or
synthetic compounds (Oprea and Matter 2004; Shoichet 2004)
It is always advantageous to begin the screening with established drug
compounds. This will enable a developer to rapidly prototype a candidate
ligand whose chemistry is well known and within the intellectual property of
the developer. The initial successful hits are called lead compounds. The
interaction of a lead compound with its target can be verified by X-ray
crystallography, if possible. From this point onward, a cycle of iterative
chemical refinement and testing continues until a drug is developed that
undergoes clinical trials.
As a consequence of lack of knowledge about function-determining
features of desired ligand molecules in the early stages of the discovery
process, virtual screening had become a complementary strategy to high
throughput screening (HTS) as follows.
1. By suggesting new chemical entities that are designed by taking into
consideration of several aspects of lead and drug-likeness and synthetic
accessibility; by making full use of pre-existing knowledge, such as reference
ligands or receptor models.
2. By facilitating the designing of, activity enriched screening sets with
increased hit rates. This computational method is valuable for discovering lead
compounds in a faster, more cost-efficient, and less resources-intensive
manner compared to experimental methods such as high-throughput screening.
The virtual screening could be separated into two steps: docking and scoring
(Tang and Marshall 2011) .
Methodologies used in virtual screening, such as molecular docking
and scoring, have advanced to a point where they can rapidly and accurately
identify lead compounds in addition to predicting native binding
conformation.
28
1.2.3.6 Docking
The docking process involves the prediction of ligand conformation
and orientation (called posing) within the binding site of a target (Fig. 7). In
general, there are two aims of docking studies: accurate structural modeling
and correct prediction of activity. However, the identification of the molecular
features that are responsible for specific biological recognition, or the
prediction of compound modifications that improve potency, are complex
issues that are often difficult to understand and even more so to simulate on a
computer.
In view of these challenges, docking is generally devised as a multi-
step process in which each step introduces one or more additional degree of
complexity (Brooijmans and Kuntz 2003). The process begins with the
application of docking algorithms that place small molecules in the active site.
The outcome of this process determines whether a given conformation and
orientation of a ligant fits the active site. This in itself is challenging, as even
relatively a simple organic molecule can contain many conformational degrees
of freedom.
Sampling these degrees of freedom must be performed with sufficient
accuracy to identify the conformation that best matches a receptor structure,
and must be fast enough to permit the evaluation of thousands of compounds
in a given docking run.
29
Figure 7. A docked pose of a ligand and protein receptor in its active site.
(a) The compound is displayed in ball and sticks and the macromolecule is
displayed as surface representation by use of PyMOL. (b) A diagram
illustrating the docking of a small molecule ligand (brown) to a protein
receptor (green) to produce a complex.
Algorithms are complemented by scoring functions (both posing and
ranking involve scoring). The pose score is often a rough measure of the fit of
a ligand into the active site. The rank score is generally more complex and
attempt to estimate binding energies. The scores are designed to predict the
biological activity through the evaluation of interactions between compounds
and potential targets. Early scoring functions evaluated compound fits on the
basis of calculations of approximate shape and electrostatic complementarities.
Relatively simple scoring functions continue to be heavily used, at least during
the early stages of docking simulations. Pre-selected conformers are often
further evaluated using more complex scoring schemes with a more detailed
treatment of electrostatic and Van der Waals interactions, and inclusion of at
least some solvation or entropic effects (Gohlke and Klebe 2002). It should
also be noted that ligand binding events are driven by a combination of
enthalpic and entropic effects, and that either entropy or enthalpy can
dominate specific interactions. This often presents a conceptual problem for
contemporary scoring functions (discussed below), because most of them are
much more focused on capturing energetic than entropic effects.
In addition to the problems associated with scoring of compound
conformations, other complications exist that make it challenging to accurately
b a
30
predict binding conformations and compound activity. These include, among
others, limited resolution of crystallographic targets, inherent flexibility,
induced fit or other conformational changes that occur on binding, and the
participation of water molecules in protein–ligand interactions.
1.2.3.7 Scoring methods
Structure-based drug designing attempts to use the structure of proteins
as a basis for designing new ligands by applying accepted principles of
molecular recognition. The basic assumption underlying structure-based drug
designing is that a good ligand molecule should bind tightly to its target. Thus,
one of the most important principles for designing or obtaining potential new
ligands is to predict the binding affinity of a ligand to its target and use it as a
criterion for selection. It gives the best possible conformation of the ligand to
the target with least binding energy (Fig. 8).
Figure 8. Prediction of best-fit ligand to the binding site of the target by
scoring function.
31
A method was developed by Bohm (Bohm 1994) to develop a general-purpose
empirical scoring function in order to describe binding energy.
The following equation (Eq.1) was derived:
Gbind Kd
Kd
Gbind = Gdesolvation + Gmotion + Gconfiguration + Ginteraction Eq. 1
where: desolvation is the enthalpic penalty for removing the ligand
from solvent; motion is the entropic penalty for reducing the degrees of
freedom when a ligand binds to its receptor; configuration is the
conformational strain energy required to put the ligand in its "active"
conformation; interaction is the enthalpic gain for resolvating the ligand with
its receptor.
The basic idea is that the overall binding free energy can be distributed
into the independent components that are known to be important for the
binding process. Each component reflects a certain kind of free energy
alteration during the binding process between a ligand and its target. The
master equation is the linear combination of these components. According to
the Gibbs free energy equation, a relationship between the dissociation
equilibrium constant, Kd, and the components of free energy can be built.
Various computational methods are used to estimate each of the
components of the equation. For example, the change in polar surface area
upon ligand binding can be used to estimate the desolvation energy. The
number of rotatable bonds, frozen upon ligand binding, is proportional to the
motion term. The configuration or strain energy can be estimated using
molecular mechanics calculations. Finally the interaction energy can be
estimated using methods such as the change in non-polar surface, statistically
derived potentials of mean force and the number of hydrogen bonds formed. In
practice, the components of the equation are fit to experimental data using
multiple linear regression. This can be achieved with a diverse training set,
including many types of ligands and targets to produce a less accurate but a
more general global model or a more restricted set of ligands and targets to
produce a more accurate but less general local model (Gohlke, Hendlich et al.
2000; Clark, Strizhev et al. 2002; Wang, Lai et al. 2002).
32
Drug discovery pipelines in pharmaceutical companies are fuelled by
HTS as one of the major sources of new hit-to-lead candidates. As
experimental methods, such as X-ray crystallography and NMR, develop, the
amount of information concerning 3D structures of biomolecular targets has
increased dramatically. In parallel, the information about structural dynamics
and electronic properties of ligands has also increased. This has encouraged
rapid development of the structure-based drug designing.
1.2.3.8 Adsorption, distribution, metabolism and excretion (ADME)
While virtual screening was expected to generate new drugs easily, just
by virtue of the sheer size of available libraries, in fact, things were not that
straight forward (Lahana 1999). In the early days, many large sets of
biologically inactive molecules were tested, most often as ill-defined mixtures.
Library composition has subsequently been significantly influenced by Chris
Lipinski, who formulated simple rules for predicting which compounds would
support useful bioavailability (Lipinski, Lombardo et al. 2001). The
bioavailability of drug molecules could be analyzed by its physicochemical
properties using Lipinski’s rule of five.
According to Lipinksi's 'rule of five' (Lipinski, Lombardo et al. 2012),
drug candidates should have a molecular mass below 500 Daltons, a
lipophilicity below Log P = 5, and contain no more than 5 hydrogen bond
donors and no more than 10 oxygen and nitrogen atoms. Furthermore, poor
passive absorption is to be expected if two or more of these conditions are
violated. As an alternative to the Lipinski rules, polar surface area (Ertl, Rohde
et al. 2000) or VolSurf parameters (Cruciani, Pastor et al. 2000) can be used to
predict oral absorption (Jorgensen 2004; Shoichet 2004; Scapin 2006; Leach
and Jhoti. 2007) and blood–brain barrier penetration (Kelder, Grootenhuis et
al. 1999; Cruciani, Pastor et al. 2000). The flexibility of molecules has been
recognized as another factor influencing bioavailability (Veber, Johnson et al.
2002).
Violation of the Lipinski rules was indeed the main reason for failure
of early combinatorial libraries, and the application of the rules significantly
aided improvements. In this context, two investigations are most often cited
(Bohm 1994), which correlated about 40% of failures in clinical development
33
with inappropriate pharmacokinetics, that is, a lack of sufficient oral efficacy.
Correspondingly, absorption, distribution, metabolism and excretion (ADME)
parameters are now considered to be key factors in drug development. The
sequential analysis of virtual screening is presented below in Fig. 9.
Figure 9. Schematic flow of virtual screening process.
1.2.4 Successes and limitations of SBDD
Successes
SBDD has proven to be successful in the design of the following drugs
which are already in clinical trials, of medically relevant proteins in complex
with compounds developed during the drug discovery process.
1. Dorzolamide (Ki = 0.37nM )(Glaucoma treatment) (Baldwin,
Ponticello et al. 1989)
2. Saquinavir, Indinavir, Ritonavir, Nelfinavir (HIV therapy) (Wlodawer
and Vondrasek 1998; Edwards 2001)
Agouron Pharmaceuticals and Vertex Pharmaceuticals, were both
successful in designing the HIV protease inhibitors nelfinavir 3 (Ki =
2.0nM) and amprenavir 4 (Ki = 0.6nM)
3. Zamnavir: Neuroaminidase inhibitors (anti-influenza) (Fig. 10) (von
Itzstein, Wu et al. 1993)
100000 • Available from public database
1000
• Screened and narrowed down to ‘n’ number of molecules based on pharmacokinetics properties (Lipinskis rule of five) using computational techniques
25
• Compounds that have better binding energy than known
5
• Analysed based on their specificity towards binding site residues
34
4. COX-2 inhibitors (anti-inflammatory)
5. Phospholipase A(2) inhibitors (anti-inflammatory)
Figure 10. Docking pose of zanamivir (Relenza) bound to influenza
neuraminidase. An example for structure based drug design showing ligand
in virtual space docked with the crystal structure of the target protein.
Residues involved in ligand binding are highlighted in three letter codes
(Blundell, Jhoti et al. 2002).
The design of estrogen receptor subtype-selective (ERα and ERβ) ligands is an
exciting success story of homology modeling and structure-based design
(Hillisch, Pineda et al. 2004).
These compounds go through numerous iterations as they progress
towards clinical trials. In an industrial environment, where multiple projects
are being worked simultaneously, with each of them likely having multiple
compound series, the number of required total complex structures can be very
high (hundreds per year). Also, the success ratio of crystals with the ligand to
harvested crystals will be very small. The efficiency of the process can be
improved only by using bioinformatics tools for sample tracking and
automation for sample handling (Gyorgy 2012).
35
Limitations
Although docking is a relatively well-developed technique, it still
suffers from some limitations. One limitation is that docking studies are
typically carried out using a rigid target model. Although a few docking
methods allow a certain degree of side-chain flexibility, these programs are
currently unable to model loop movements or induced-fit effects, which are
found in many systems (Ajay and Murcko 1995; Charifson, Corkery et al.
1999; Muegge and Rarey 2001). Combinatorial chemistry and structure based
validation assist the refinement process significantly. This is a very powerful
technique that chemists employ to aid in the refinement of a lead compound. It
is a synthetic tool that enables chemists to rapidly generate thousands of lead
compound derivatives for testing. A scaffold is employed that contains a
portion of the ligand that remains constant. Subsite groups are potential sites
for derivatization. These subsites are then reacted with combinatorial libraries
to generate a multitude of derivative structures, each with different substituent
groups. If a scaffold contains three derivatization sites and the library contains
ten groups per site, theoretically 1000 different combinations are possible. By
carefully selecting libraries, based upon the study of the active site, we can
target the derivatization process towards optimizing ligand receptor
interaction.
1.3 Protein crystallization and data collection
Protein crystals can be interpreted as an infinite array in which a
minimum volume (known as a unit-cell) is repeated in three-dimensional
space and within a unit-cell molecules are arranged following well-defined
symmetry rules. Growth of a single and well defined diffracting crystal forms
the basic and essential prerequisite for X-ray crystallographic protein structure
determination (Blow 2002). Producing high quality crystals has always been
the bottleneck in structure determination and it is still not understood why
some proteins crystallize with ease while others stubbornly refuse to produce
suitable crystals (Chayen 2004). Crystallization requires that a protein is first
be purified to homogeneity and then concentrated to a supersaturated state.
36
Crystal growth has three steps: nucleation, growth and cessation of
growth. However, identifying a successful crystallization condition for a new
protein is like looking for a needle in a haystack (McPherson 2009). Also,
truncation of a protein at the flexible N or C terminus may lead to proper
crystallization and diffraction. In addition, tagging a partially soluble or
partially homogeneous protein with maltose binding protein (MBP) (Gruswitz,
Frishman et al. 2005) or other tags (Cherezov, Rosenbaum et al. 2007) at the N
or C terminus has given a positive effect in crystal formation (nucleation site
for crystal formation) as well as in enhancing solubility. Different techniques
are employed for setting up crystallization trials, which include sitting drop
vapor diffusion, hanging drop vapor diffusion, sandwich drop, batch, micro
batch under oil, micro dialysis and free interface diffusion. While vapor
diffusion has been very popular and successful for the past 40 years,
microbatch, which is the simplest method, is a relatively new technique.
Sitting and hanging drop methods are easy to manipulate, require a
small amount of sample, and allow large amount of flexibility during
screening and optimization. Once a lead is obtained as to which conditions
may be suitable for crystal growth, the conditions can generally be fine-tuned
by making variations to the parameters including precipitant, pH, salt,
temperature, etc. Cryo-preservation of good crystals is also a defining factor in
protein crystallography. This is helpful in protecting a protein crystal from the
naturally damaging effects of X-rays during data collection. Usually, glycerol,
sucrose, propanediol, glycol, etc. are used for cryo-preservation. No additional
cryo protection agent is needed if a crystal is already grown in some natural
cryo buffers or glutaraldehyde.
Objectives
1. To identify specific drug targets towards the substrate binding domain
(SBD) of Siah2 using in silica high throughput virtual screening (HTVS)
2. To validate the potential drug targets using in vitro and in vivo binding
assays, and
3. To identify the critical amino acid residues involved in the
binding/interaction between Siah2 and the target molecules.
37
CHAPTER 2. MATERIALS AND METHODS
2.1 Homology modeling
Homology modeling of the SBD of Siah2 was built using Modeller 9v7
software (Sali and Blundell 1993). The amino acid sequence of human Siah2
was retrieved from GenBank (accession number: NM_005067) in NCBI
(McGinnis and Madden 2004). It consists of 324 amino acids of which,
residues from 130-322 belongs to SBD. The SBD was then subjected to PSI-
BLAST search (Altschul, Madden et al. 1997) in order to identify the
homologous proteins from the Protein Data bank (PDB) (Berman, Henrick et
al. 2003). An appropriate template for SBD was identified based on the e-
value and sequence identity. The template and the target sequences was then
aligned using ClustalW (Thompson, Gibson et al. 2002). Subsequently,
homology modeling for SBD of Siah2 against the chosen template was carried
out using Modeller 9v7. The outcome of the modeled structures were ranked
on the basis of an internal scoring function and those with the least internal
scores were identified and utilized for model validation.
2.2 Validation of the modeled structure
In order to assess the reliability of the modeled structure of SBD of
Siah2, we calculated the root mean square deviation (RMSD) by
superimposing with template structure using 3-dimensional structural
superposition (3D-SS) tool (Sumathi, Ananthalakshmi et al. 2006). The
backbone conformation of the modeled structure was calculated by analyzing
the phi (Φ) and psi (ψ) torsion angles using PROCHECK (Laskowski,
MacArthur et al. 1993) determined by the Ramachandran plot statistics.
Finally, the quality of consistency between the template and the modeled
Siah2 were evaluated using ProSA (Wiederstein and Sippl 2007), in which the
energy criteria for the modeled structure was compared with the potential
mean force obtained from a large set of known protein structures.
38
2.3 Protein simulation
The stability of the modeled Siah2 was checked by molecular
dynamics (MD) simulation using Gromacs software (Hess, Kutzner et al.
2008). The modeled SBD was energy minimized using optimized potentials
for liquid simulations all-atom (OPLS) force field (McDonald and Jorgensen
1998). This preliminary energy minimization was performed to discard the
high energy intramolecular interactions. The overall geometry and atomic
charges were optimized to avoid steric clashes. The modeled SBD was
solvated in a rectangular box of about 16,614 water molecules using TIP3P
model system in order to mimic the physiological behavior of the
biomolecules (Jorgensen, Chandrasekhar et al. 1983).
The energy of the modeled Siah2 was then minimized without
restraints for 2000 steps using the above mentioned algorithms. The system
was then gradually heated from 10 to 300 Kelvin over 5 nano seconds (ns)
using the NVT ensemble. Finally MD simulation was carried out to examine
the quality of the model structures by checking their stability and by
performing a 5 ns simulation and the lowest energy structure during simulation
was calculated. Similar protocol was followed for the template also in order to
compare the stability and reliability between the modeled and experimentally
solved proteins.
2.4 Docking
SBD of Siah2 was modeled computationally and its active sites were
predicted using SiteMap (version 2.3, Schrödinger, LLC, New York, NY,
2009) followed by docking studies. The preparation of the modeled Siah2 was
performed by protein preparation wizard and menadione was prepared using
the ‘LigPrep’ program and the docking procedure was performed using Glide
extra precision (XP version 5.5), which produces the least number of
inaccurate poses and calculates the accurate binding energy of 3D structures of
a known protein with ligands (Kontoyianni, McClellan et al. 2003; Friesner,
Banks et al. 2004). Docking studies were carried out using menadione as an
inhibitor against the modeled SBD of Siah2. The Siah2-menadione ligand
39
complex was used as a reference point in order to identify better drug-like
molecules that could interact against SBD of Siah2.
2.5 Virtual screening
A total of 16,908 molecules derived from public databases namely
Maybridge (14,400, www.maybridge.com), PubChem (Wang, Xiao et al.
2009) (2438, obtained from Shanghai Institute of Organic Chemistry) and
Binding (70, www.bindingdb.org), were selected for virtual screening against
SBD. The in silico structure-based high-throughput virtual screening (HTVS)
method of Glide, version 5.5 (Schrödinger, LLC, New York, 2009) (Friesner,
Banks et al. 2004) was used for screening. Before performing HTVS, the
ligand molecules collected from the databases were prepared using the
‘LigPrep’ module and were subsequently subjected to Glide ‘Ligand docking’
protocol with HTVS mode.
A grid file was generated using the Receptor Grid Generation protocol
with centroid at the predicted binding site of the modeled structure. A scaling
factor of 1.0 was set to van der Waals (VDW) radii for the atoms of residues
that presumably interact with ligands and the partial atomic charge was set to
less than 0.25. Ligands were then allowed to dock with the high throughput
screening (HTVS) mode and all the obtained molecules were subjected to the
Glide extra precision (XP) mode of docking, which performs extensive
sampling and provides reasonable binding poses(Friesner, Banks et al. 2004).
The lead molecules from screening were then tested in silico for their
pharmacokinetic properties and percent human oral absorption using QikProp
programme (Jorgensen 2006).
2.6 Cell lines and cell culture
Human embryonic kidney 293T (HEK 293T) were grown at 37°C and
5% CO2 in Dulbecco's modified Eagle's medium (Invitrogen) supplemented
with 10% fetal bovine serum (FBS) (HyClone), L-glutamine (Invitrogen) and
pencillin/streptomycin (Invitrogen).
T47D, MCF 7 breast cancer cell lines, were kindly provided by Dr.
Yong Eu Leong (NUS) and maintained in Minimum Essential Medium Eagle
40
(MEM) (Sigma Aldrich) with 1% L-glutamine, 1% penicillin/streptomycin
and 10% FBS.
MDA-MB-231, BT 549, MCF 10A breast cancer cell lines, were
kindly provided by Dr. Alan PremKumar (NUS) and maintained in Roswell
Park Memorial Institute 1640 medium (Sigma Aldrich) supplemented with 1%
L-glutamine, 1% penicillin/streptomycin and 10% FBS. These cell lines were
obtained ethically from ATCC (Singapore). MCF 10a was cultured in
Mammary Epithelial Growth Medium (MEGM, Lonza) supplemented with
growth factors, 10% FBS, 1% L-glutamine and 1% penicillin/streptomycin.
All cell lines were maintained in a humidified incubator at 37oC with 5% CO2
and sub-cultured according to suppliers recommendations.
2.7 Molecular cloning
2.7.1 Human plasmid constructs
The pcDNA3.1 FLAG-SBD of human Siah2(130-392) and Full-
length(1-394) were constructed by PCR amplification of Siah2 cDNA
fragments separately from the pCMV-SPORT6 plasmid (Thermo Scientific
OpenBiosystems) with a HindIII-containing forward primer and an XbaI-
containing reverse primer. The HindIII-Siah2-XbaI fragments was then
subcloned into the FLAG-pcDNA3.1 plasmid between the HindIII and XbaI
restriction sites. Similarly FLAG-SBD of Siah1 (90-292) and full-length (1-
292) were constructed as described for Siah2 using the same restriction sites.
HA-PHD3, HA-Nrf2 and HA-βcatenin plasmids were provided by our
collaborator Dr. Thilo Hagen.
2.7.2 Bacterial expression plasmid constructs
As described above, the gene fragment encoding SBD residues (130-
322) of Siah2, was PCR amplified using the Pfu DNA polymerase (Fermentas)
with an EcoRI containing forward primer and NotI containing reverse primer
(1st Base). The amplified gene was ligated into the modified pET-Duet vector,
next to a pre-inserted bacterial maltose binding protein (MBP), between the
EcoRI and NotI restriction sites. The second molecular cloning site of the
vector contained a preinserted bacterial disulfide bond isomerase (DSBC) gene
41
(Pioszak and Xu 2008). This modified vector was a generous gift from one of
our collaborator Dr. Eric Xu of Van Andel Institute, USA. Full length PHD3
was PCR amplified using the pcDNA 3.1 PHD3 plasmid as a template and
then cloned into the modifed pET-Duet vector as described for Siah2. SBD of
Siah2 was also cloned in pGEX6P-1 vector with an N-terminal GST tag using
a BamHI containing forward primer and a XhoI containing reverse primer.
The list of constructs and primers are given in Table 7 (Appendix 1).
2.8 Transient transfection
2.8.1 Plasmid transfection
DNA plasmids were transiently co-transfected in subconfluent HEK
293T cells platted in a 60mm plate with GeneJuice Transfection Reagent
(Novagen). Empty pcDNA3.1 vector was also co-transfected as a control. To
this end, GeneJuice transfection reagent was first added to serum free DMEM
media and incubated for 5 min. 1µg of each DNA plasmid was then added to
the mixture and further incubated for 15 min. The mixture was then added
dropwise to the cells and returned to the 5% CO2. Cells were lysed 48 hours
after transfection.
2.8.2 Small-interfering (siRNA) transfection
HEK 293T cells were plated at 2 X 105
cells per 6-well plate for siRNA
transfections. RNAi Max Lipofectamine (Invitrogen) was used as a
transfection agent according to the manufacturer’s instructions. The following
predesigned siRNA duplexes (IDT) at final concentration of 20nM were used:
HSC.RNAI.N005067.12.3, 3'UTR/2 (Siah2 siRNA oligo #1) and
HSC.RNAI.N005067.12.7, CDS/2 (Siah2 siRNA oligo #2). Lipofectamine
RNAiMax and respective siRNAs were separately diluted in serum free
DMEM media for 5 min before mixing together for another 20min of
incubation at room temperature. The mixture was then added dropwise to the
cells and returned to the 5% CO2. Cells were lysed three days after siRNA
transfection.
For MCF 7 cells reverse transfection was carried out. siRNA-
Lipofectamine RNAiMAX complexes as described above, were prepared in
42
opti-MEM reduced serum free media (Invitrogen). This mixture was first
added to the well and then 1.5 X 105
and 3 X 103
cells per 6-well and 96-well
were seeded. Plates were incubated for 72 hours at 37°C in a CO2 incubator.
2.9 Protein isolation and concentration determination
At the time of harvest, cells were washed with ice-cold 1X PBS and
then lysed in 1X MPHER mammalian protein extraction reagent (Thermo
Scientific). Cell lysates were stored at -80 °C freezer. Protein concentration
determination of cell lysates was carried out using BSA standard curve assay
in a 96-well format. Frozen cells were thawed and spun down at 4 °C for 10
min at 13000 rpm in a microfuge. 1 µl of supernatant was added to 100 µl of
solution containing 1X Bradford reagent and the absorbance at 595nm was
read using a Tecan GENios microplate reader (Tecan Trading, Austria).
2.10 SDS-PAGE
A 12% resolving gel and 4% stacking gel were casted using 30%
acrylamide-Bis solution 37.5:1 (2.6% C). Protein lysate was mixed with 1X
loading dye or Laemmli sample buffer and heated at 95 °C for 5 min. Protein
samples were spun at 12,000 rpm for 1 min and loaded into wells of SDS-
PAGE gel. Gel containing protein lysate samples were electrophorezed using
1X running buffer (3.03g Tris base, 14.4g glycine, 10ml of 10% SDS/L) at
80V for fifteen minutes, followed by 95V for two hours until the dye front
reached the bottom of the gel. After electrophoresis, the gel was removed and
proteins were transferred onto a nitrocellulose membrane (AmershamHybond
ECL, GE Healthcare) in 1X cold transfer buffer (3.1 g Tris base, 14.4 g
glycine, 20% methanol/L) for overnight at 25 V.
2.11 Immunoblotting and detection
The nitrocellulose membrane with proteins was blocked with 5% w/v
non-fat milk in 1X Tris-buffered saline [0.1% (v/v], Tween-20 (TBS-T) for 1
hour at room temperature. The membrane was incubated with diluted primary
antibody in TBST for 1 hour at room temperature or overnight at 4 °C.
Unbound primary antibody was removed by washing thrice with 1X TBS-T
43
for 5 min. Following this, the membrane was incubated with IgG-HRP-
conjugated secondary antibodies at 1:10,000 dilution for 1 hour at room
temperature. The following antibodies were used to probe the membranes:
mouse monoclonal anti-FLAG (Sigma) at 1:5000 dilution, rat or mouse
monoclonal anti-HA (clone 3F10, Roche) at 1:5000 dilution, mouse
monoclonal anti-β-actin (Sigma) at 1:10,000 dilution, anti-p27 at 1:2000
dilution. The following primary antibodies required overnight incubation at
4°C: goat polyclonal anti-Siah2 (Santa Cruz Biotechnology) at 1:500 dilution,
mouse monoclonal anti-Siah2 (Sigma) at 1:500 dilution. The bound antibodies
were detected using the detection reagent (Amersham ECL Western Blotting
System, GE Healthcare. The blot was exposed to X-ray film (Thermo
Scientific) for different exposure times.
2.12 Co-immunoprecipitaction
For co-immunoprecipitation, cells were washed with cold PBS and
lysed 2 days post-transfection with lysis buffer containing 25mM Tris-
HCL(pH 7.5), 3mM EDTA, 2.5mM EGTA, 20mM NaF, 1mM Na3VO4, 20mM
sodium β-glycerophosphate, 10mM sodium pyrophosphate, 0.5% Triton X-
100, 0.1% β-mercaptoethanol and Roche protease inhibitor cocktail. lysates
from transfected cells grown in 60mm plates were precleared by centrifugation
and were added to beads. Anti-FLAG or anti-HA M2 agarose beads coupled to
anti-FLAG or anti-HA monoclonal antibody was used to immunoprecipitate
the FLAG-Siah2 or HA-PHD3. Samples were tumbled at 4 °C for 1 hour and
washed four times with NP40 lysis buffer containing 20mM Tris, pH7.5,
50mM NaCl, 0.5mM EDTA, 5% glycerol, 0.5% NP40; and once with buffer
containing 50 mM Tris, pH 7.5.
2.13 GST pull down
For GST pull down assay, GST-fused Siah2 SBD was allowed to bind
to glutathione sepharose beads (GE Healthcare) for 30 min at 4 °C in binding
buffer containing 50 mM Tris-HCl buffer, 150 mM NaCl, 1 mM DTT, 5%
glycerol, 0.1% Triton X-100, pH 8.0. The PHD3 lysate from HEK293T cells
were allowed to incubate with GST-Siah2 fusion proteins, as indicated,
44
immobilized on glutathione sepharose beads for 1 hour at 4 °C. Controls
included GST alone, tested for binding to PHD3. After binding, the resin was
washed three times in binding buffer, and then heated in Laemmli sample
buffer for 5min at 95 °C. Samples were separately resolved by 12% PAGE and
Western blotted using anti-HA antibody.
2.14 RNA isolation
RNA extraction was carried out using the TRIzol reagent ( Invitrogen).
Cells were scrapped in 1ml of TRIzol and transferred to 1.7 ml eppendoff
tubes. The homogenates were then stored for 5 min at room temperature to
permit complete dissociation of nucleoprotein complexes. They were then
supplemented with 0.6ml chloroform, vortexed and microcentrifuged at
13,000 rpm for 15 min. Following centrifugation, the mixture separated into
middle red phenol-chloroform phase, interphase and organic bottom phase
respectively. The top colourless aqueous layer was then transferred into a fresh
tube and equal volume of isopropanal was slowly added to precipitate RNA
and incubated on ice for 15 min. The samples were then spun down for 10 min
and the supernatant was discarded. The pellet was washed with 1ml of cold
75% ethanol. The samples were then centrifuged at 13000 rpm for another 10
min and the supernatant was removed. The RNA pellet was air-dried and
dissolved in ultra-pure RNase free water and its concentration was measured
using NanoDrop ND-1000 spectrophotometer (NanoDrop technologies,
Wilmington, DE).
2.15 Real-time polymerase chain reaction using SYBR green
Real time quantitative RT-PCR was carried out using the one-step RT-
PCR kit with SYBR GREEN (Bio-Rad). PCR was carried out in triplicates for
each sample being validated. The β-actin gene was used as a control. Primers
(1st Base) used for Siah2 are as follows and can be found in (Frasor, Danes et
al. 2005)
Forward primer: 5-CTATGGAGAAGGTGGCCTCG-3
Reverse primer: 5-CGTATGGTGCAGGGTCAGG-3
45
2.16 Domain swapping using fusion PCR and mutants
A three step fusion PCR procedure was employed to create the fusion
proteins (Hobert 2002), SBD-Siah1-2 and SBD-Siah2-1 from the wild type
pcDNA Siah1 and Siah2 constructs described previously. In this procedure for
generating SBD-Siah1-2, the first round of PCR involved amplifying the first
100 bp of SBD (1-193 residues) using pcDNA forward primer and Siah1-2
reverse primer. In the second round of PCR involved amplifying the C
terminal region of SBD (the remaining 93 bp) using Siah1-2 forward primer
and pcDNA reverse primer. The final round of PCR was performed using both
the PCR products that were amplified in the previous rounds as templates and
with forward and reverse primer of pcDNA. The final PCR product was gel
purified and digested with XbaI and HindIII restriction enzymes (New
England Biolabs). Similarly SBD-Siah2-1 was also prepared as mentioned in
Table 2. Predigested products were individually ligated with pcDNA, which is
also digested with XbaI and HindIII restriction enzymes and transformed into
E. coli DH5α cells. Positive colonies were screened and verified by DNA
sequencing.
SBD-Siah1-2 mutant (Mut) and SBD-Siah2-1 mutant (Mut) were
custom synthesized (Shanghai ShineGene Molecular Biotech,Inc).
46
Table 2. List of primers and corresponding template used for fusion PCR
Gene Forward Primers Reverse Primers Template
Name Sequence Name Sequence
Siah1-2 PCR1 pcDNA F 5AGCAGAGCTCT
CTGG3
Siah1-2 R 3TCCAGCACCAGC
ATGAAGTGAAAGC
CAA5
pcDNA-Siah1
PCR2 Siah1-2 F 5TTGGCTTTCACT
TCATGCTGGTGCT
GGA 3
pcDNA R 3AGGCACAGTCGA
GGC5
pcDNA-Siah2
PCR3 pcDNA F 5AGCAGAGCTCT
CTGG 3
pcDNA R 3AGGCACAGTCGA
GGC5
Gel purified
product of PCR1
and PCR2
Siah2-1 PCR1 pcDNA F 5AGCAGAGCTCT
CTGG 3
Siah2-1 R 3TCTAAGACTAAC
ATGAAGTGATGGC
CAA5
pcDNA-Siah2
PCR2 Siah2-1 F 5TTGGCCATCAC
TTCATGTTAGTCT
TA GA 3
pcDNA R 3AGGCACAGTCGA
GGC5
pcDNA-Siah1
PCR3 pcDNA F 5AGCAGAGCTCT
CTGG 3
pcDNA R 3AGGCACAGTCGA
GGC5
Gel purified
product of PCR1
and PCR2
47
2.17 Drug treatments and preparation
The Five compounds, Compound 1 (ACR#HTS09269SC), Compound
2 (ACR#SCR01006SC), Compound 3 (ACR#SCR00073SC), Compound 4
(ACR#DSHS00977SC), and Compound 5 (ACR#DP01471SC) were
purchased from Fisher Scientific Pte Ltd, Singapore. Compounds were
dissolved in DMSO to prepare 200mM Stock. For all compounds, final
working concentrations were attained by freshly diluting stock solutions with
complete medium before use.
2.18 MTS assay
The [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-
sulfophenyl)-2H-tetrazolium, inner salt] (MTS) assay is a colorimetric method
for determining the number of viable cells in proliferation or cytotoxicity
assays. Growth inhibition was measured using the CellTiter 96 Aqueous One
Solution Assay (Promega, Madison, WI) as recommended by the
manufacturer. The solution contains MTS compound and an electron coupling
reagent PES [phenazine ethosulfate]. The MTS compound is bio-reduced by
mitochondrial enzymes of cells into a colored formazan product that is soluble
in tissue culture medium. The quantity of formazan product as measured by
the amount of 490 nm absorbance is directly proportional to the number of
living cells in culture. Phenol red free culture medium was used. The
experiment was performed in triplicates. After 4 hours of incubation under
standard conditions of 5% CO2 and 37°C, the colour change due to the
reduction of formazan product (indicative of reduction of MTS) was visible.
MCF7 cancer cells (4x103 per well) were seeded on 96-well plates in triplicate
at each dose level. The cells were grown for 24 h prior to treatment.
Compounds were diluted in DMSO at 200 mM before being further diluted in
culture medium prior to each experiment. The compounds were added on day
1 at 0.06 µM, 0.032 µM, 1.6 µM, 8 µM, 40 µM, 200 µM and 1000 µM and
kept in the media for 72 hours. Absorbance was read in a Tecan GENios micro
plate reader (Tecan trading, Austria). The signal generated (color intensity) is
directly proportional to the number of viable (metabolically active) cells in the
wells. Relative cell numbers can therefore be determined based on the optical
48
absorbance (optical density, OD) of the sample. The blank values (media only)
were subtracted from each well of the treated cells and controls.
2.19 Protein expression and purification of MBP-SBD-Siah2 and
MBP-PHD3
The pET-Duet construct carrying MBP-SBD-Siah2-6xHis gene at the
first multiple cloning site (MCS) and DSBC at the second MCS was
transformed in the E. coli BL21 (DE3) (Stratagene) for protein expression. A
single colony was picked and inoculated into 100 ml of LB medium
supplemented with 100 μg/ml ampicillin (Gold Biotech) and allowed to grow
overnight at 37 °C with continuous shaking. Subsequently, it was transferred
into 1 L LB medium supplemented with 100 μg/ml ampicillin and allowed to
grow at 37 °C until the OD600 was 0.6-0.8 AU. Subsequently, to induce protein
expression, 0.1 mM isopropyl-1-thio-β-D (-) thiogalactopyranoside (IPTG)
(Gold Biotech) was added to the culture and grown at 16°C for 16 hrs with
continuous shaking. Cells from 1 L of culture were harvested by centrifugation
at 7,000 g for 30 minutes and the pellet was stored at -80 °C. Cell pellet
obtained was resuspended in 50 ml of lysis buffer containg 50 mM Tris-HCl
buffer, 150 mM NaCl, 2 mM DTT, 5% Glycerol, 0.1% Triton X-100 with the
final pH adjusted to 8.0. and 1 ml of Sigma protease inhibitor cocktail. Cell
suspension was sonicated for 9 min with 1 sec ON and 1 sec OFF pulse. Cell
lysate obtained was centrifuged at 39,000 g for 30 min to sediment cell debris
and insoluble proteins; clear supernatant was mixed with 5 ml of Ni-NTA
(Qiagen) resin pre-equilibrated with lysis buffer and allowed to bind for 1 hr.
Weakly bound proteins were removed by extensive washing using lysis buffer
with 10 mM imidazole and later eluted with 250 mM imidazole in lysis buffer.
To remove the excess imidazole and further purification, amylose affinity
purification with 3 mL of 50% slurry of amylose resin (New England Biolabs)
was allowed to bind for 1 hour with Ni-NTA purified MBP-SBD-6xHis with
gentle shaking using MBP affinity. The wash buffer composition for the
amylose column is 50 mM Tris-HCl (pH 8.0), 150 mM NaCl, and 5% glycerol
while 10 mM maltose was added for elution. After affinity chromatography,
the eluted protein was loaded to a Superdex 200 column (GE Healthcare)
49
equilibrated with 20 mM Tris, 100 mM NaCl, pH 8.0. The purified protein
was then concentrated using ultra 30 kDa Mol. Wt. cut off Centricon (Merck
Millipore).
Expression of MBP-PHD3 followed almost the same above conditions,
but during purification in addition to affinity chromatography, ion exchange
chromatography was carried out prior to gel filtration. In short, the construct
was transformed into E. coli BL21 (DE3) competent cells and the protein was
over expressed and first affinity purified using Amylose resin. The protein was
further purified using an anion exchange Hi-Trap Q-HP (GE Healthcare)
column, with buffer A: 50 mM Tris and buffer B: 50 mM Tris, 300mM NaCl
pH adjusted to 8.0. MBP-PHD3 was again purified using a Superdex 200
column (GE Healthcare) equilibrated with 20 mM Tris, 100 mM NaCl pH
8.0. The purified MBP-PHD3 protein was concentrated using ultra filtration
membrane (Merck Millipore) with 30 kDa molecular weight cut off. All the
protein purification steps were carried out at 4°C.
2.20 Statistical analysis
Siah2 gene expression levels by real time PCR data were analyzed by
one-way ANOVA, Dunnett's multiple comparison test using the Graphpad
Prism software. For comparisons, probability values <5% (P < 0.05) were
considered statistically significant.
2.21 Isothermal titration calorimetry (ITC)
The binding affinities and thermodynamic parameters between SBD of
Siah2 and PHD3 or compounds were studied using isothermal titration
calorimetry (ITC). The interaction of Siah2 SBD with PHD3 was verified
using a VP-ITC microcalorimeter (MicroCal) at 25°C in 50 mM Tris buffer,
pH 8. The cell contained 10 μM SBD. The syringe contained 150 μM of PHD3
and titrated against SBD of Siah2, which was prepared in the same buffer.
Samples were first degassed for 15 min and titration was performed using a
stirring speed of 390 rpm. The initial injection for ligand was 2 μl for 10 s and
subsequent injections were 10 μl for 10 s with 240 s spacing. Data points were
fitted using the integrated program ORIGIN (MicroCal) for one-site binding.
50
2.22 Crystallization
Crystallization trials were carried for the purified MBP-SBD protein at
room temperature by hanging-drop vapor-diffusion method using
crystallization screens from Hampton Research and Jena Bioscience screens.
51
CHAPTER 3. IDENTIFICATION OF DRUG LIKE
MOLECULES AGAINST SBD OF SIAH2 BY AN IN SILICO
APPROACH
3.1 Introduction
Given its involvement in various cancer and its related signaling
pathways (discussed in Section 1.8), Siah2 has been suggested as a target for
developing inhibitors for blocking its activity or abundance (Nakayama, Qi et
al. 2009). Siah2 regulates HIF-1α by hypoxia response and SPRY2 by RAS
signaling leading to tumor growth and metastasis. The SBD is of importance
in the induction of HIF-1α dependent pathway. However, to date no structural
or drug targeting information against SBD of human Siah2 is available.
Homology modeling provides a way of constructing the structure of a protein
with a "target" from its amino acid sequence and an experimental 3D structure
of a related homologous protein (the "template"). Herein, we applied
homology modeling approach to construct the structure of SBD of Siah2
The inhibition of the Siah2 expression could effectively inhibit
angiogenesis in tumors. In this regard, menadione (vitamin K3) has been
identified as an inhibitor for Siah2 which effectively attenuates hypoxia and
the MAPK signaling pathway and inhibits melanoma growth in the mouse
xenograft model (Shah, Stebbins et al. 2009). Targeting a non-catalytic
substrate has been deemed among the more difficult tasks, although more
advanced structure-based design holds a better promise.
In this study, we present structural information of SBD for Siah2 using
a homology modeling approach followed by a molecular dynamics simulation
in order to analyze the stability of this domain. In addition, we predicted the
binding site of the SBD in order to eventually identify drug-like molecules
which possessed better binding energies and pharmacokinetic properties for
this SBD using in silico high throughput virtual screening with menadione as a
reference ligand, thereby investigating the structural features of SBD of Siah2,
which may be a novel drug target for inhibitor development studies for cancers
during hypoxic conditions. The potential specific drug-like molecules obtained
52
from such a screening procedure could serve as inhibitors against SBD of
Siah2.
3.2 Results
3.2.1 Generation of SBD of Siah2 by homology modeling
The main criteria in homology modeling are the template selection and
sequence alignment between the target and the template. The PDB ID’s for the
three template hits that were found for the SBD of Siah2 after performing PSI-
Blast were 1K2F, 2AN6 and 2A25. The 3D-structure of 2AN6 has low
resolution of 3 Å and there are 13 missing residues in its X-ray structure.
Although 2A25 seems to be a good template with a resolution of 2.2Å, its 15
missing residue regions appears as a string in the modeled protein. Therefore,
1K2F was identified as a promising template having a sequence similarity of
86% to the SBD with a resolution of 2.4Å and contained no missing residues.
Hence, the structure 1K2F was selected as the appropriate template for SBD
modeling. During modeling the original amino acids positions of SBD i.e. 130
– 322 were assigned as 1- 193 residues respectively by Modeller 9v7. Hence
the later positions are assigned for further data analysis. Twenty five models
were generated and ranked during modeling of SBD against template 1K2F.
The best ranked model (Fig. 11) was then subjected to evaluation to check the
quality of the generated model.
53
Figure 11. Modeled structure for SBD of Siah2 obtained from
Modeller9v7. The structure is represented in secondary structure mode using
pymol; the α-helixes, β-sheets and loops are colored in red, yellow and green
respectively.
3.2.2 Evaluation of the modeled structure
The modeled SBD of Siah2 was taken for further optimization and
validation. The calculated root mean square deviations between the target and
template structure was found to be 0.324 Å (Fig. 12a). Furthermore, the
quality of the structure derived from homology modeling was validated by
calculating the ProSA. z-score which gives the overall model quality by Cα
positions. The z-score of the target and template were -4.7 and -5.2. Hence the
quality of the model obtained was validated using ProSA score. Both the target
and the template models show the similar profiles given in the Fig. 12b and
12c.
The geometric evaluations of the modeled 3D-structure of SBD were
performed using PROCHECK by calculating the Ramachandran plot (Fig.
12d) (Table 3). This plot represents the distribution of the Φ and ψ angles in
each residual of amino acid. The percentage of phi and psi angles in the
allowed regions is 90% for residues in the core region whereas 0.9% of
residues were located in the disallowed regions.
54
Figure 12. Model validation. (a) Superimposition of modeled Siah2 (target)
and 1K2F (template) using the 3d-SS tool. In this wireframe diagram, yellow
color represents the target and green represents the template. (b) z-score of -
4.7 from this graph represents the overall quality of the modeled 3D-structure
to Siah2. It can be used to check whether the z-score of the input structure is
within the range of scores typically found for native proteins of similar size.
Its value is displayed in this plot that contains the z-scores of all
experimentally determined protein chains in current PDB solved by either X-
ray diffraction or NMR. (c) z-score of -5.1 from this graph represents the
overall quality of the template 3D-structure (PDB ID 1K2F). When compared
to Fig. 2b, it was found that the overall quality of the 3D-structures of the
template and target is highly similar in terms of their corresponding z-scores.
(d) Ramachandran Plot for the modeled SBD of Siah2. The most favored
regions are colored red, additional allowed, generously allowed and
disallowed regions are indicated as yellow, light yellow and white fields
respectively.
55
Table 3. Ramachandran Plot statistics on 3D model of SBD of Siah2
calculated using PROCHECK
3.2.3 Molecular dynamics simulation of stability
The stability of the modeled SBD of Siah2 was assessed by MD
simulation for 5ns and was used for subsequent docking studies. In addition,
parameters such as pressure, temperature and total energy were calculated to
check the stability of the modeled protein with improved steric parameters.
The analysis of the simulation parameters revealed that the total energy for the
modeled SBD is -6.74E+5 kJ mol-1
and for the template 1K2F is -7.22E+5 kJ
mol-1
(Fig. 13a, 13b). This shows that the target and template does not show
much variation in terms of their total energy and hence the modeled structure
was considered to be stable as of the experimentally solved structure of the
template (1K2F). A pressure bar of 1.0 and temperature of 300K applied to
both the proteins did not exhibit any significant difference until 5ns. Based on
these simulation parameters it is evident that the modeled SBD adopted good
conformational stability that could used for further SBDD.
Ramachandran plot statistics for the modeled
protein
core% 90.3
allowed% 0.8
general% 0.6
Disallowed% 0.9
Bad contacts 12
G factor -0.02
Main chain bond lengths -0.17
Main chain bond angles -0.23
56
Figure 13. Total energy calculated by MD Simulation. The total energies of
the modeled SBD of Siah2 (a) and the template 1K2F (b) were compared
using gromacs. This comparison shows that the overall energy of the target
and template are very similar, so the modeled SBD was considered to be stable
as the experimentally solved structure of the template (1K2F).
3.2.4 Docking and HTVS screening of compounds
The validated and refined model was then used for docking using a
known functional inhibitor menadione and the complexes were analyzed for
the putative functional amino acid residues that are involved in hydrogen
bonding. Prediction of the binding site of SBD was performed using the
Sitemap in Schrodinger, which yields 13 amino acid residues in the binding
pocket of SBD for Siah2 (Fig. 14). The predicted binding pocket was validated
by both blind docking (to examine whether the ligand binds the protein away
from the predicted binding site) and normal docking (allowing the ligand to
bind to the predicted binding site) using menadione as an inhibitor against
Siah2 using Glide (XP). It was observed that 8 of 13 predicted binding site
residue atoms are within 5 Å of the ligand in both docking procedures.
57
Figure 14. Diagrammatic representation of the 13 predicted active site
residues of SBD. The amino acids in SBD were numbered from 1-193 during
modeling, whereas the corresponding amino acid residue position for the full
length protein is also shown which contains the amino acid residues 1-324.
Yellow color represents the amino acid which is consistent in hydrogen
bonding formation for all the 5 identified lead molecules, whereas orange
color represents the other amino acids involved in hydrogen bonding.
In addition we observed that the amino acid Ser39 (corresponding to
Ser167 for a full length protein) is involved in hydrogen bond formation with
menadione during both the docking procedures. This particular binding site
was utilized for HTVS of compounds from a Maybridge database as one of the
selection criteria. We next aimed to identify and characterize ligands
interacting with predicted binding sites of SBD of Siah2. The in silico
structure-based high-throughput virtual screening (HTVS) method of Glide,
version 5.5 (Schrödinger, LLC, New York, 2009) (Friesner, Banks et al.
2004), was used to identify potential ligand molecules that interacted with at
least one of the residues identified. The binding of ligands to these residues is
postulated to render competitive inhibition with substrates of Siah2 that
interact through SBD.
3.2.5 Selection of inhibitors
Out of 16,908 molecules from the three public ligand databases, Hits
were obtained only for Maybridge database. As a result 1000 potential ligand
hits were identified based on their pharmacokinetic properties (ADMET).
Subsequently 156 drug-like molecules were identified based on their glide
scores better than menadione and compared with its binding conformation;
eventually 20 lead molecules were selected. Five of 20 drug-like molecules
were identified based on their hydrogen bond interaction with the putative
58
functional amino acid residue Ser39 with a binding energy ranged from ~ -6
kcal mol-1
to ~ -8 kcal mol-1
against Siah2 and their chemical structures are
presented along with their IUPAC names (Fig. 15), whereas the binding
energy for menadione is ~ -5 kcal mol-1
.
Figure 15. Chemical structure of the five lead molecules. Compound
1(7599): 2',3',4',9'-tetrahydrospiro[piperidine-4,1'-pyrido[3,4-b]indole];
Compound 2 (724): (R)-4-methyl-N-(2-oxoazocan-3-yl)-3,4-dihydro-2H-
benzo[b][1,4]oxazine-6-sulfonamide; Compound 3 (4035): 4-methyl-N-((5-
methyl-3-phenylisoxazol-4-yl)methyl)-3,4-dihydro-2H-benzo[b][1,4]oxazine-
6-sulfonamide;Compound 4(10746): 2-(2-hydroxyphenyl) quinazoline-4(1H)-
one; Compound 5(7757): 4-oxo-1,4-dihydroquinazoline-2-carbohydrazide.
Corresponding Maybridge ID's are given in parentheses.
The binding conformations of these lead compounds towards the
modeled Siah2 were also analyzed to determine the hydrogen bond
interactions (Fig. 16). All protein-ligand complexes for these five lead
molecules possess multiple hydrogen bonds when compared with menadione.
The short hydrogen bond distance, ranging from ~1.5 to ~2.4 Å, and the
favourable binding G-scores (-8 to -6 kcal/mol) suggested strong enzyme-
ligand interactions. It is noteworthy that all five lead molecules interacted with
residue Ser39 in common and their respective glide scores are comparable to
menadione. The docking results of the final lead molecules against Siah2 are
given in Table 4.
59
Figure 16. Binding poses of the five lead molecules and menadione to SBD
of Siah2. Panels (a-e) represents the binding pose of lead molecules and (f)
represents the binding pose of Menadione. The proposed binding mode of the
lead molecules are shown in ball and stick display and non-carbon atoms are
colored by atom types. Critical residues of binding are shown as tubes colored
by atom types. Hydrogen bonds are shown as dotted yellow lines with the
distance between donor and acceptor atoms indicated. Atom type color code:
red for oxygen, blue for nitrogen, grey for carbon and yellow for sulfur atoms
respectively. Ser39 amino acid residue that interacted with the lead molecules
is indicated by circles.
60
Table 4. Glide extra-precision (XP) results for the five lead molecules and
menadione using Schrodinger 9.0.
a Ligand IDs are of the Maybridge database. b Glide score. c Number of hydrogen bonds formed.
3.2.6 Bioavailability of the lead molecules
The drug-like properties of the lead compounds was assessed by
evaluating their physicochemical properties using QikProp(Lipinski,
Lombardo et al. 2001). Their molecular weights were < 500 daltons with < 5
hydrogen bond donors, < 10 hydrogen bond acceptors and a log p of < 5
(Table 5). These properties are well within the acceptable range of Lipinski’s
rule of five for the five lead molecules. Further, the pharmacokinetic
parameters of the lead molecules were analyzed which includes absorption,
distribution, metabolism, excretion and toxicity (ADMET) using QikProp. For
the five lead compounds, the partition coefficient (QPlogPo/w) and water
solubility (QPlogS) are critical for estimation of absorption and distribution of
drugs within the body, ranged between ~ -0.9 to ~ 2.23 and ~ -0.05 to ~ -3.65,
while the bioavailability and toxicity were ~ -0.1 to ~ 0.3. Overall, the
percentage human oral absorption for the compounds ranged from ~ 57 to ~
72%. These pharmacokinetic parameters are well within the acceptable range
defined for human use, thereby indicating their potential as drug- like
molecules.
Lead
molecules a
Glide score
kcal/mol) b
Amino acids interacted #HB c
7599 -8.3618 SER39, ILE155 and HIS156 3
724 -7.4965 GLY44, VAL159, CYS40, SER39 and
HIS156 6
4035 -6.7967 SER39, CYS40, HIS156 and VAL159, 4
10746 -6.5975 SER39 and ILE155 3
7757 -6.5049 SER39, ILE155 and PRO7 3
Menadione -5.6476 SER39 1
61
Table 5. QikProp properties of the identified drug-like molecules and menadione using Schrodinger 9.0.
Lead
molecules a
QP log
Po/w b
QP log S c MW
d QPlogHERG
e # Metab
f HA
g HD
h
Percent human
oral
absorption i
7599
1.771 -1.251 241.33 -5.708 1 5 3 72.335
724
0.845 -2.075 339.41 -6.651 3 7 2 74.975
4035
3.008 -4.930 339.46 -5.765 4 7 1 77.055
10746
1.842 -3.058 238.24 -5.360 2 4 2 70.148
7757 -0.523 -1.966 204.188 -5.416 0 6 3 55.280
Menadione -0.967 -1.051 198.671 -6.351 4 6 4 57.45
a Ligand IDs are of the Maybridge database.
b Predicted octanol/water partition co-efficient log p (acceptable range: -2.0 to 6.5).
c Predicted aqueous solubility; S in mol/L (acceptable range: -6.5 to 0.5 ).
d Molecular weight ( < 500 daltons).
e Predicted IC50 value for blockage of HERG K+ channels (acceptable range: below -5.0).
f Number of metabolic reactions (1 – 8).
g Hydrogen bond acceptors (< 10).
h Hydrogen bond donors (< 5).
I Percentage of human oral absorption < 25% is poor and > 80% is high).
62
3.3 Discussions
This study provides the 3D structure of SBD of Siah2 by homology
modeling approach. Energy minimization and molecular dynamics simulation
were done to check the stability of the modeled structure. The analysis of the
homology modeling and MD simulation indicate that the theoretical prediction
is consistent with the known set of experimental results. This structure was
used for further insilico HTVS and we have identified five drug-like molecules
with better binding affinity, when compared to menadione. Compounds are
screened against the SBD with the aim of inhibiting the activity of Siah2 on its
substrates targeting via SBD. We mainly hypothesize a competitive binding of
these compounds to SBD of Siah2 with its substrates. Also Further analysis of
the binding conformations of the lead molecules revealed that Ser39 (ser 167
of full length) residue of SBD may be the key amino acid residue interacting
with these ligands necessary for the induction of Siah2 activity.
Yet another possibility of inhibition is also suspected. Siah2 is subjected to
phosphorylation, which increases its ubiquitin targeted degradation activity
under hypoxia conditions through MAPK p38 signaling cascade.
Phosphopeptide mapping or mutational analyses revealed S29 and T24 as
major sites of Siah2 phosphorylation which are present in the RING domain of
Siah (Emerling, Platanias et al. 2005; Khurana, Nakayama et al. 2006). The
study also highlight the possibility that Siah2 may be subjected to
phosphorylation on other MAPK acceptor sites which are below the detection
limits used in the experimental condition. We also hypothesize that Ser 39 (Ser
167 of full length), if found to be one of the phosphorylation site of Siah2,
could play a role in activity of the compounds.
So far the ubiquitin ligases have not been shown to exhibit the catalytic
activity, but merely to function as scaffolds for ubiquitin transfer, suggesting
that inhibitors will have to target protein-protein interaction. The identified
drug like molecules against the SBD of Siah2 may block the interaction
between Siah2 and its substrates by inhibiting their interaction. We observed
that these small molecules possessed greater binding affinity, when compared
to menadione by docking studies. As this study mainly aims to block protein-
protein interaction with no catalytic activity, we hypothesize that these lead
63
drug-like small molecules could serve as inhibitors against Siah2. More
studies are warranted to reinforce these findings. Since the functionally
essential role of ser39 (Ser167) is not evident from experimental method, our
further experimental validation of drug compounds are targeted to inhibit the
interaction of SBD of Siah2 and its substrates.
64
CHAPTER 4. EXPERIMENTAL VALIDATION OF DRUG
LIKE MOLECULES BY IN VITRO AND IN VIVO BINDING
ASSAYS
4.1 Introduction
Siah proteins, evolutionarily conserved E3 RING zinc finger ubiquitin
ligases, have recently been implicated in various cancers and show promise as
novel anticancer drug targets. There are various substrates of Siah that have
recently been reviewed and discussed (House, Moller et al. 2009; Nakayama,
Qi et al. 2009). Two substrates of Siah2 in hypoxia signaling and cancer have
been reported, HIPk2 and PHD3. Siah2 has been shown to regulate the
stability of HIPK2 (Calzado, de la Vega et al. 2009), a transcriptional
repressor of the HIF-1α gene (Nardinocchi, Puca et al. 2009). HIPK2 directly
phosphorylates Siah2 resulting in a disruption of the HIPK2/Siah2 interaction,
thus stabilizing HIPK2 and promoting apoptosis. It was also reported that
chronic hypoxia mainly in rat brain region decreases 2-oxoglutarate (also
known as α-ketoglutarate dehydrogenase complex (OGDC or KGDHC)
activity, and Siah2 may function in this process as it targets OGDC-E2 for
degradation although factors signaling this are not well understood and this
regulation in not reported in cancer (Habelhah, Laine et al. 2004). Among the
various substrates of Siah2, PHD3 is well characterized substrate under
hypoxia condition in cancer. Three PHD proteins have been identified: PHD1,
2, and 3(Epstein, Gleadle et al. 2001). The association of Siah2 with each of
the 3 PHDs were examined and the results revealed that PHD3 exhibited the
highest degree of binding (Nakayama, Frew et al. 2004). Such selective
targeting was due to the lack of N-terminal extension in PHD3 which are
present in PHD1 and PHD2 (Nakayama, Gazdoiu et al. 2007). The E3 ligase
Siah2 preferentially targets PHD3 for degradation under hypoxic conditions
and thus stabilizing HIF-1α which in turn promotes its transcriptional activity
(Nakayama, Frew et al. 2004). Although, all three were found to hydroxylate
HIF-1α in the in vitro experiments (Bruick and McKnight 2001), in vivo
PHD2 plays a major role in normoxic HIF-1α regulation (Berra, Benizri et al.
2003). PHD3 is known to be induced under hypoxia and believed to regulate
65
HIF-1α stability specifically under conditions of low oxygen concentrations.
Hypoxic condition and the role of PHD1 in regulating Hif -1α is not clear.
PHD3 can homo- and hetero-multimerize with other PHDs. Consequently,
PHD3 is found in high-molecularmass fractions that were enriched in hypoxia
and subsequent degradation by Siah2 (Nakayama, Gazdoiu et al. 2007).
Inhibiting Siah2 activity with a peptide inhibitor PHYL designed to
outcompete association of Siah2-interacting proteins reduced metastasis
through HIF-1α, which is implicated in tumorigenesis, and metastasis. This
selective targeting of PHD3 is achieved through the SBD. PHYL peptide binds
to SBD of Siah2 with high affinity and disrupts the PHD3 binding, thereby
elevating PHD3 levels and reducing HIF-1 α (Qi, Nakayama et al. 2008).
4.2 Results
4.2.1 SBD of Siah2 alone can independently interact with PHD3
irrespective of the N terminal Ring Domain
In light of the role of Siah2 in regulating PHD3 through SBD, we
chose this substrate for our further studies. We used coimmunoprecipitation to
analyze the interaction of Siah2 and PHD3. Two Siah2 plasmid constructs, full
length and SBD were generated. For this, cells were cotransfected with N-
terminally FLAG-tagged Siah2 full length or SBD construct and HA tagged
PHD3 or empty vector. Anti-FLAG M2 agarose beads were then used to
immunoprecipiate the Siah-PHD3 protein complex. The complex was
analyzed using Western blots with HA antibody to directly compare the
binding of PHD3 to full length and the SBD of Siah2. When comparing the
ratio of PHD3 in the FLAG-immunopreciptates to that in the lysate, a marked
enrichment of PHD3 protein was seen in the immunoprecipitates suggesting
strong binding of Siah2 towards PHD3 (Fig. 17). Furthermore, it was found
that the amounts of PHD3 that bound to full length and SBD of Siah2 were
similar. Taken together, the comparable binding of PHD3 to full length and
the SBD of Siah2 suggests that under in vivo conditions the substrate binding
domain alone is sufficient to bind to PHD3. Our results are consistent with the
data published by Nakayama et al., (Nakayama, Frew et al. 2004), who
showed that PHD3 is regulated by E3 ubiquitin ligase Siah2 for proteasome-
66
dependent degradation. Hence for further experiments, we focused on the
interaction between SBD of Siah2 and PHD3.
Figure 17. Coimmunoprecipitation of Siah2 with PHD3. HEK293T cells
were transfected in 60-mm cell culture plates for 2 days with expression
constructs for the proteins indicated at the top of panel. The cells were lysed,
and the lysates were subjected to FLAG immunoprecipitation (IP), as
described under Materials and Methods. Immunoprecipitates and aliquots of
the cell lysates were analyzed by Western blotting with the indicated
antibodies. Both the Full length and SBD binds to their substrate PHD to the
same extent.
67
4.2.2 GST pull down for SBD of Siah2 with PHD3
To confirm the interaction between PHD3 and Siah2 we also carried
out GST pull down assays. In these assays we used recombinant GST tagged
SBD of Siah2 and lysates from cells transfected with PHD3. To prepare a
recombinant GST-SBD, bacterial expression plasmid of GST-Siah2 was made
in pGEX-6P-1 vector. This expression vector pGEX-6P-1: SBD (130-322)
was transformed into E. coli BL21 and induced with 0.2 mM IPTG at 18 ˚C
overnight. The bacteria pellets were harvested, sonicated and lysed in 50mM
Tris-HCl pH 8.0, 100 mM NaCl, 2 mM dithiothreitol containing a protease
inhibitors cocktail (Sigma). Similarly, the pGEX-6P-1 expression vector alone
was expressed as a GST control. After sonication the lysates were pre-cleared
at high speed centrifugation and the supernatant was used for pull down assay.
As shown in Fig 18, the GST pull down experiment was successful and
in vitro binding of SBD of Siah2 to PHD3 was detected. Therefore, the GST
pull-down results support the coimmunoprecipitation studies.
Figure 18. GST pull down of SBD with PHD3. GST and GST-SBD proteins
were immobilized on glutathione-agarose beads and incubated with lysates
from cells trasfected with PHD3. The samples were then analyzed by western
blotting using anti HA antibody.
4.2.3 Expression and purification of MBP-SBD of Siah2
We next intended to analyze the thermodynamics parameters between
compounds/PHD3 and SBD of Siah2 by ITC experiments. For this
corresponding proteins were purified. Siah2 with an N-terminal GST was
expressed for pulldown assays. However attempts to purify and cleave the tag
leads to protein precipitation and aggregation. Since the protein yield with
MBP fusion tag was much higher than when expressed with GST tag, for
further experiments MBP-SBD-siah2 fusion protein was used.
68
The soluble SBD, (residues 130-322) of human Siah2 was expressed in
fusion with the MBP using an oxidizing atmosphere of the in E. coli BL21
(DE3) competent cells. It was cloned downstream of the MBP tag in multiple
cloning site 1 (MCS1) of the modifed pETDuet-1 vector which has a bacterial
DsbC in MCS2 to assist proper folding of the protein of interest during
expression. In summary, the final expressed protein was MBP-SBD-Siah2-
6xHis. The protein was purified with affinity chromatography using affinity
tags at both ends. This was essential to ensure the removal of prematurely
truncated proteins, which arise due to unwarranted translational stops that are
common when human proteins are expressed in E. coli. In addition, having
affinity tags at both ends also prevents co-purification of truncation products,
which could be formed during or after cell lysis. The recombinant protein was
first purified using Ni-NTA affinity purification and then followed by amylose
affinity purification (Fig 19). Secondly, the protein was purified using size
exclusion chromatography, where the protein eluted as dimer and monomer at
a molecular weight of 130 and 63 kDa in an S200 column (Fig 20).
Figure 19. SDS-PAGE affinity purification profile of MBP-SBD of Siah2.
Key for each lanes were labelled separately.
69
Figure 20. Gel filtration profile of purified MBP-SBD of Siah2. (a) After
affinity purification the elute was passed through Superdex-200 column and
eluted as three peaks. First peak corresponds to void volume. the second and
third peak is the dimeric and monomeric SBD of Siah2. (b) Samples from
three peaks were separated by 12% SDS-PAGE and visualized by Coomassie
staining. Fusion protein migrated in the gel with an apparent molecular weight
of 63 kDa. Lanes: 1. Low molecular weight ladder; 2-4. First peak of gel
filtration elution; 5-9. Second peak of gel filtration elution; 9-12. Third peak of
the gel filtration elution. SDS-PAGE shows the protein is highly pure.
AggregatesDimer
Monomer
Imidazole
a
b
70
4.2.4 Expression and purification of MBP-PHD3
Full length and truncated PHD3 with a Histag were cloned in pET-M
vector and over expressed in BL21 (DE3) cells. However the proteins were
completely insoluble. The proteins in the inclusion bodies were denatured and
the attempts to refolding yielded, a protein precipitation (Fig. 43 Appendix
2.1). Hence we moved on to the MBP tag which aids in the solubility and
purification of PHD3. Similar to the expression of MBP-Siah2, MBP-PHD3
expression was carried out (Fig 21) and purified in three steps: affinity
purification using amylose resin, anion exchange chromatography (the pI of
MPB-PHD3 is 6) (Fig 22) and size exclusion chromatography. The MBP-
PHD3 protein eluted as a monomer in the size exclusion chromatography,
corresponding to its molecular weight of 65 kDa. The eluted fractions of gel
filtration show the protein is about 95% pure, (Fig 23).
Figure 21. SDS-PAGE affinity purification profile of MBP-PHD3
71
Figure 22. The ion-exchange chromatography profile of PHD3. (a) The
blue line indicates the UV reading when samples are passed out of the column
and the brown line indicates the concentration of high-salt buffer added into
the column. (b) SDS-PAGE gel showing the FPLC fractions of the eluted
protein run on an anion exchange column. The SDS-PAGE gel verified that
PHD3 is found in Peak 3. Lanes 2 to 3 correspond to Peak 1, lanes 4 to 5
correspond to Peak 2 and lanes 6 to 10 correspond to Peak 3.
a
b
72
Figure 23. Gel filtration profile of purified MBP-PHD3. (a) After ion
exchange the fractions were pooled and passed through Superdex-200 column
for further purification. MBP-PHD3 eluted as two peaks. First peak
corresponds to void volume. The second elutes as a monomeric PHD3. (b)
Samples from two peaks were separated by 12% SDS-PAGE and visualized
by Coomassie staining. Fusion protein migrated in the gel with an apparent
molecular weight of 65 kDa. Lanes: 1. Low molecular weight ladder; 2-8. First
peak of gel filtration elution; 9-10. Second peak of gel filtration elution.
The purification of both MBP-SBD and MBP-PHD3 resulted in highly
pure proteins, which were used for ITC experiments and crystallization
attempts. The identity of the protein was verified by Mass spectrometry (MS)
(Fig 44. Appendix 2.2).
a
b
73
4.2.5 ITC analyses
Inhibitors have high affinity towards Siah2 compared to PHD3
Interactions between SBD of Siah2 and the compounds and PHD3
were studied using ITC experiments. ITC is very useful in drug development,
mainly to determine the relative affinities of a ligand that can bind to a protein
with one or more different and specific binding sites. The thermodynamic
parameters of these interactions are shown in Table 6. All the ITC experiments
were performed with the titration of ligands at 150 μM into SBD at 10 μM in
the cell under same buffer conditions. All reactions were exothermic in nature
as shown in Fig. 24 and the upper panels show the raw ITC data of each
injection. The peaks were normalized to the ligand-protein molar ratio and
were integrated as shown in the bottom panels. Solid dots indicate
experimental data, and their best fit was obtained from a non-linear least
squares method, using a one-site binding model depicted by a continuous line.
PHD3, compound 1 and compound 5 bind to SBD in 1:2 ratio (N = ~2)
at two binding sites with nearly the same thermodynamic properties (Fig. 24a,
b, f). However compound 2 and compound 3 bind in 1:1 ratio at one binding
site (Fig 24c, d). For compound 3, the inflection point in the calorimetric
isotherm occurs near the molar ratio of 0.6, suggesting only half of the binding
site is occupied by the compound (Fig 24d). Compound 4 shows a non-
stoichiometric binding profile with N = 0.05 and from the best fit values, the
standard error was large (data not shown) suggesting the initial heat changes
are unlikely to be significant and suggests no binding (Fig. 24e). Compared to
the compounds, the heat released when the substrate (PHD3) was titrated
against the SBD of Siah2 was observed to be large as it a protein-protein
interaction. The binding affinities of compound 1 and 5 were observed to be
four fold stronger than that of the binding affinity of Siah2-SBD-PHD3. As
control, MBP-SBD of Siah2 was titrated against no ligand or substrate (Fig.
24g).
In addition, all observations were further analyzed by enthalpy and
entropy changes (Table 6). The negative Gibbs free energy change for SBD-
PHD3 and SBD compound interactions also indicates that all the reactions are
thermodynamically favored.
74
a b
c d
75
Figure 24. ITC data for SBD with compounds and PHD3. (a) SBD-PHD3
titration. (b) SBD-Cpd 1 titration. (c) SBD-cpd 2 titration. (d) SBD-cpd3
titration. (e) SBD-cpd4 titration. (f) SBD-cpd5 titration. (g) SBD-Control. The
upper panels show the injection profile after baseline correction and the
bottom panels show the integration (heat release) for each injection.
g
e f
76
Table 6. Thermodynamic parameters for interaction between SBD of Siah2 and PHD3 or compounds
(“-” represents “no binding”)
Molecules N Ka (105 M-1) Kd
μM
ΔH
(kcal/mol)
TΔS
(kcal/mol)
ΔG
(kcal/mol)
PHD3 2.07 0.029 2.30 0.339 4.34 -7.74 0.177 3.15 -10.89
Cpd 1 1.805±0.050 7.02 ±0.801 1.42 -2.23±0.120 5.72 -7.95
Cpd 2 1.06 0.081 3.28 3.04 -2.11 6.62 -8.73
Cpd 3 0.649 0.073 1.28 7.8 -3.95 0.837 1.18 -5.13
Cpd 4 - - - - - -
Cpd 5 1.785 9.34 0.485 1.07 -6.14 6.02 -12.16
77
4.2.6 Compounds partially decrease SBD interaction with PHD3
We used both coimmunmoprecipitation and GST pull down assays to
study the effect of the compounds on the interaction between the SBD of
Siah2 and PHD3. We hypothesized that the identified small molecules would
bind to the binding sites of SBD and would reduce or prevent Siah2 binding to
its substrate. From the docking screen of more than 16,000 compounds in the
May bridge library the top scoring 5 chemicals that were predicted to bind to
the ligand binding pocket of SBD with high affinity were tested for their
ability to inhibit the binding interaction between the Siah2 SBD and PHD3.
4.2.6.1 Coimmunoprecipitation of SBD with PHD3 in the presence of
compounds
Coimmunoprecipitation of Siah2 SBD and PHD3 was carried out in
the presence of the identified lead compounds to test their efficacy. As
mentioned earlier, the FLAG tagged SBD and HA tagged PHD3 proteins were
co-transfected in the HEK293T cell line. After 40 hours of transfection, the
cells were treated with the inhibitors at a concentration of 100 µM for 4 hours.
The cells were then subjected to lysis and immunoprecipitated with FLAG
beads and analyzed by western blot using an anti-HA antibody. Compounds 1,
2, 3 and 5 were found to partially decrease the SBD coimmunoprecipitation
with PHD3 (Fig. 25a).
78
Figure 25. Coimmunoprecipitation of SBD with PHD3 in the presence of
the compounds. HEK293T cells were transfected in 60-mm cell culture plates
for 2 days with expression constructs for the proteins indicated at the top of
each panel. four hours before the lysis, cells were treated with 100 µM of each
compounds seperately and then coimmunoprecipitates of SBD with PHD3 was
analyzed. The cells were lysed, and the lysates were subjected to FLAG
immunoprecipitation (IP), as described under materials and methods. (a)
represents the immunoprecipitates of SBD with PHD3 in the presence of
inhibitor. Compound 1, 2, 3 and 5 found to have a subtle level of inhibition
between the SBD and PHD3 complex. (b) represents the expression levels of
proteins in the respective lysates.
a
b
79
4.2.6.2 GST pull down of SBD with PHD3 in the presence of compounds
We next performed the GST pull down assay in the presence of
compounds to test the effect of the compounds in the in vitro binding of SBD
of Siah2 with PHD3. SBD was, expressed as the GST fusion protein in
Escherichia coli. Equal volume of bacterial protein supernatants (derived by
centrifugation of 1 litre cultures depending on the relative expression level of
recombinant protein) were incubated with PHD3 in the presence of a given
compounds at 100 μM concentration. Among the compounds only compound
5 showed marked activity in suppressing Siah2 binding to PHD3 (Fig. 26a).
As pre incubation of compounds with Siah2 was not performed previously, we
intended to check whether pre incubation of the compounds with Siah2 prior
to the addition of PHD3, would result in a greater inhibitory effect. Hence, we
performed the pull-down assay with 3 different drug concentrations: 200 μM,
1 mM and 2 mM, where the compounds were allowed to pre incubate with
SBD prior to binding to PHD3. Based on the initial pulldown data,
Compounds 1, 3, 5 were chosen for pull down in a dose dependent manner.
Compound 5 was effective in inhibiting the interaction at 200 μM and 1mM
but not at 2mM. The lack of effect at 2mM may be due to low solubility.
Compound 1 was found to partially inhibit the interaction at the same level in
all the three doses. Compound 3 partially inhibited the interaction at the higher
concentrations of 1mM and 2mM (Fig 26b). From this data, among the five
compounds, compound 5 represents a candidate inhibitor of the Siah2 E3
ubiquitin ligase.
80
Figure 26. GST pull down of SBD with PHD3 in the presence of
compounds. (a) Hek293T cells transiently over expressing PHD3, were
harvested and the cell lysates were subjected to the GST-SBD or GST alone
pull-down assay (materials and methods) in the absence or presence of
Compounds at the indicated concentration. Only Cpd 5 has visible reduction in
binding of SBD of Siah2 with PHD3. (b) GST pull down of SBD with PHD3
in the presence of compounds in a dose dependent manner. Hek293T cells
transiently over expressing PHD3, were harvested and the cell lysates were
subjected to the GST-SBD or GST alone pull-down assay (as described in
materials and methods) in the absence or presence of Compound 1, 3, and 5 at
the three different concentrations. Cpd 5 shows consistent and marked
reduction in inhibition. (c) commasive blue staining to show the expression of
GST alone or GST- SBD of Siah2.
a
b
c
81
4.2.7 Basal level expression of Siah2 in breast cancer
In order to further analyze the antiproliferative and cytotoxicity of the
compounds in cell lines, we chose selected breast cancer cell lines.
Studies of Siah2 have shown that the E3 ligase functions as an
oncogene in animal models and patient cohort studies of human breast cancer
(Moller, House et al. 2009; Behling, Tang et al. 2011; Chan, Moller et al.
2011; Wong, Sceneay et al. 2012) . The expression of Siah2 in breast cancer
cell lines has not been well characterized as most studies are based on Siah2
staining in cancer tissues and the normal counterparts. Two cell line based
studies in breast cancer drew our interest in analyzing the expression levels of
Siah2 prior to test the activity of the compounds. The first study reported that,
estrogen increases Siah2 levels in estrogen receptor ER positive breast cancer
cells. This resulted in degradation of the nuclear receptor corepressor 1 (N-
CoR) and a reduction in N-CoR–mediated repressive effects on gene
expression, thus promoting cancer growth (Frasor, Danes et al. 2005). In
contrast to this, another study reported the higher level of Siah2 expression in
the ER negative cell line (MDA-MB2 31) (Sarkar, Sharan et al. 2012). In this
cell line Src and Siah2 mediated the degradation of C/EBP, a tumor
suppressor protein. Hence we chose two ER positive breast cancer cells
(MCF7 and T47D) and two ER negative breast cancer cells (MDA-MB231
and BT549) and one normal breast epithelial cell MCF 10A to compare the
expression levels of Siah2 in these cell lines and subsequently to check the
activity of the compounds in the same.
The Siah2 m-RNA expression levels in these cell lines were analyzed.
To quantify the mRNA levels, we used real time PCR and the primers were
validated (Fig. 27). Our result shows that the expression level was elevated in
ER positive MCF7 cells consistent with the finding that estrogen increases
Siah2 activity in ER positive cells. The ER positive T47D cell line also shows
significant difference compared to the normal cell line (Fig. 28). In conclusion
these results are consistent with an important role of Siah2 in breast cancer.
Given that Siah2 showed the highest expression in MCF7 cells we used this
cell line for further studies.
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Figure 27. Agarose gel showing the size of single PCR products generated
by Siah2 gene-specific primers for q-PCR. cDNA from HEK293T cells
were used as template. 100 bp DNA ladders were loaded to identify the base-
lengths of PCR products. Sizes of PCR products correspond to the sizes
obtained from primer validation data.
Figure 28. mRNA expression analysis of Siah2 in breast cancer cell lines. Total RNA extracted from all five breast cell lines was subjected to
semiquantitative RT-PCR. Experiments were repeated in triplicates. * denotes
p<0.05. one way annova, Dunnett's Multiple Comparison Test is used to test
the significance.
.
80 bp amplicon50 bp
100 bp
83
4.2.8 siRNA mediated gene silencing
We next wanted to determine the protein concentration of Siah2 in
different cell lines. Gene knock down studies were carried out for evaluating
the specificity of the compounds for Siah2 and also to validate the antibody.
The Siah2 gene was knocked down in HEK293T and MCF 7 cells using
siRNA and quantitative reverse transcription–PCR was performed to
determine the extent of knockdown of Siah2. Two SiRNA’s, one targeting the
3′ UTR region and the other targeting the coding region were used. From the
two dsiRNA oligos tested, oligo #1 and oligo #2 were found to efficiently
down-regulate Siah2 mRNA levels (Fig. 29).
The selectivity and the specificity of siRNA knockdown can be
verified by rescue experiments. Our results clearly show that over expression
of Siah2 was not rescued by siRNA 2 and thus both endogenous and over
expressed Siah2, is successfully abrogated by siRNA2. However, siRNA1
completely rescued Siah2 as it targets the 3 UTR region (Fig. 30).
Figure 29. Validation of knockdown of Siah2 in HEK293T and MCF7 cell
lines. Cells were transfected with indicated oligos for 72 hours and were
harvested for RNA extraction. Real time-PCR was performed with Siah2
primer, normalized with β-Actin and represented as fold difference.
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Figure 30. Rescue experiments using the Siah2 full length constructs. HEK293T cells were cotransfected with indicated siRNA or control siRNA
oligos, together with the expression vectors: Flag-tagged Siah2, or the empty
vehicle vector; At 48 h post-transfection, cell lysates were analyzed by
Western blotting with anti FLAG ab and actin as a loading control.
4.2.9 Endogenous Siah2 protein expression and antibody validation
Goat polyclonal anti-Siah2 (N-14) (sc-5507; Santa Cruz
Biotechnology) antibody was used to identify the endogenous Siah2 protein
expression levels in all four breast cancer and one normal breast epithelial cell
lines. However, the observed non specific binding made it difficult to identify
endogenous Siah2 expression. Also differences in protein (from the suspected
respective molecular weight band) (Fig. 31a) and mRNA expression patterns
of Siah2 were observed. In this regard, Siah2 protein was tested for its stability
using cyclohexamide, a protein synthesis inhibitor. Over time there was no
degradation of the protein as detected by the antibody (Fig. 31b). Thus we
validated the antibody to check its specificity for Siah2 and to confirm the
identity of the protein prior to any further studies.
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Figure 31. Analysis of endogeneous Siah2 protein expression and test for
its stability. (a) The cell lines were cultured, harvested, and analyzed by
Western blot analysis with Siah2 antibody from Santa Cruz and actin (as a
loading control). Rasmos lysate positive control for Ab1 was also used. (b)
MCF7 and T47D cells were treated with 40 μM cycloheximide (CHX), a
protein synthesis inhibitor, and were lysed at time points 0, 2, 5 and 8 hr. No
change in stability of Siah2 protein detected by Ab1. As a control, an unstable
protein p27 was analyzed for its stability by CHX.
In order to determine the specificity of the antibody we analyzed both
the endogenous and ectopic expression of Siah2 in the presence or absence of
siRNA oligos. There was no difference in expression levels observed in both
endogenous and ectopic Siah2 (Fig. 32). The results apparently show the
inability of the antibody to detect the Siah2 protein. We also tried validating
another antibody, Mouse monoclonal anti-Siah2 (S7945; Sigma).
Unfortunately even this antibody was also not able to detect Siah2 protein
expression. Antibody IP was carried out for both the antibodies simultaneously
along with a control antibody to check whether they could detect the Siah2
protein in immunoprecipitates (data not shown). For these reasons we could
not detect the protein expression siah2 in breast cancer cells.
b a
86
Figure 32. Antibody validation using siRNA oligos. HEK293T Cells were
transfected with negative control or Siah2 siRNA for 3 days and with FLAG-
Siah2 for the last 2 days. Cells were lysed and cells lysates were analyzed by
Western blotting with the indicated antibody. Antibody could not detect the
ectopic Siah2 expression.
87
4.2.10 Effect of Compounds on Cell Viability
MTS proliferation assay was used to analyze the anti proliferative and
cytotoxic activity of all the five compounds in MCF7 cells. Viable cell masses
at the time of drug addition (time 0), and following 72 hours of drug exposure
were determined. For this, serial dilutions of these compounds were prepared
at a final concentration of 0.064, 0.32, 1.6, 8, 40, 200 and 1000 µM
respectively. The compounds and untreated control (UnC) were tested in
triplicate. To investigate the effect of compounds on the growth of MCF 7 cell,
the cells were treated with the compounds of indicated concentration and
incubated for 72h. The absorbance measures were normalized to that of
control untreated cells at 72h. Anti-proliferative and cytotoxicity, elicited by
all 5 compounds in the MCF 7 cell line were obtained as shown in Fig 33.
From this data we observe that the all compounds exhibit varied activity on
MCF7 cells. As shown in Fig. 33, marked reduction in the cell growth was
observed in the cell treated with compound 1 after 72 h treatment. This
inhibitory effect of compound 1 is observed between 1 and 40 μM. On the
other hand compound 4 has cytotoxic effect at low concentration and as the
concentration increases cytotoxicity decreases. This could be because of poor
compound solubility at high concentrations. No cytotoxicity or anti-
proliferative activity was observed in cells treated with the compound 2.
However, compounds 5 and 3 showed minimal inhibition of growth when
compared to the UnC. These results indicate that the compounds have limited
activity in cell line. These observations frame critical considerations to focus
on the other structural analogues or by side chain modifications, which could
increase the pharmacokinetics property of the drug compounds.
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Figure 33. Cell proliferative effects of compounds in MCF7 cell line. Antiproliferative or cytotoxicity effects of all five compounds in MCF 7 cell
was analyzed by MTS assay by measuring the number of viable cells. The
values are reported as the fold change relative to control at 72 hrs.
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4.2.11 Analysis of the specificity of the compound 1 for Siah2 in MCF7
cells
Since compound 1 possesses effective anti proliferative activity, the
compound was chosen to study the specificity of compounds for Siah2. To
determine whether the anti-proliferative and cytotoxic activity of compound 1
is dependent on the presence of Siah2, we repeated the assay in MCF 7 cells
with siRNA mediated Siah2 knock down. Since the maximum inhibition was
observed at a concentration of 40 µM, we used this concentration for the
knock down studies. However, there was a similar growth inhibition in Siah2
knock down compared to control cell. This result suggests that compound 1
has low specificity in cells and exerts Siah2 independent effects specificity of
compounds within the cells (Fig 34).
Figure 34. Specificity of Compound 1 to Siah2 in MCF 7 cell line. Cell
proliferation of MCF7 cells at 40 µM in the presence of non specific and
specific control Siah2 siRNA was measured by MTS assay for 72 hrs.
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4.3 Discussions
4.3.1 Advantages
In our second study, we addressed one of the important criteria which
determine the medical value of a candidate drug, 'specificity'. The
physiological effect of a drug should be defined to an extent. It has to
specifically bind to a target protein in order to minimize undesired side-
effects. However, efficacy is not always a result of lack of specificity alone but
also could be from the response body to drug and related regulation of
malfunction. Molecular level specificity includes two independent
mechanisms. First the drug has to bind to its substrate with suitable affinity
and second it has to either stimulate or inhibit the regulation of its target
protein. Both mechanisms are mediated by a variety of interactions between
the drug and target.
The results from our biochemical, biophysical and cell-biological
assays indicate that the novel compounds identified through computational
screening have partial selective inhibitory property for the SBD of Siah2 in the
in vitro and in vivo binding assays. We characterized the thermodynamic
parameters of the interaction of the SBD of Siah2 with the substrate PHD3 by
identified test compounds. We also analyzed the anti-proliferative, cytotoxic
profile and specificity of the compounds. The relationship between in vitro
affinity and in vivo inhibition constants is reported in Appendix 3. Although
the molecular details of the specificity of the compounds towards SBD
remains unclear, our study provides the strategy of identifying small molecules
through SBDD and validation for its specificity through in vitro and cell based
binding assays.
.
4.3.2 Limitations
There could be various reasons for the partial activity of the
compounds. The first reason is drug solubility. Although the computational
predictions of pharmacokinetic and bioavailability properties of all these
compounds, falls under acceptable ranges, there could be some issues with
poor drug solubility. The solubility profile of a substance is required to
identify the potential issues in drug precipitation in vivo. With the aid of
advancement in combinatorial and medicinal chemistry, a slight chemical
91
modification of a molecule can lead to enhancement in solubility (Amidon,
Lennernas et al. 1995). In addition to solubility, factors such as trans-
membrane delivery or cell uptake efficacy, bio-stability, and nonspecific
absorption or binding by serum or cellular proteins may contribute to better
efficacy in cells. The reason for not including menadione as one of the control
is because of their potential toxicity in human use which is banned by FDA.
Since the compounds are screened against SBD, there was a limitation to
measure the activity of compounds in degrading PHD3 as it requires full
length protein.
The Siah protein levels may be differentially regulated because of
stability as these proteins are reported in auto ubiquitination (Hu and Fearon
1999; Depaux, Regnier-Ricard et al. 2006), and hence this may limit the
availability of the proteins to bind to the target or compounds. We could not
confirm this hypothesis since we were not able to detect the endogenous
protein expression levels because of poor antibodies.
Another important reason is cross reactivity. Due to high sequence
similarity between the Siah1 and Siah2 SBD, it is suspected the identified drug
compounds could also cross react with Siah1. Our initial screening of the
compounds also ignored Siah1 specificity. In support to this, most of the
tumor suppressor role of Siah1 was identified in breast cancer. Hence we
hypothesize that the activity of compounds in the Siah2 knockdown cells may
likely be affected due to the cross reactivity with Siah1.
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CHAPTER 5. IDENTIFICATION OF CRITICAL RESIDUES
INVOLVED IN SUBSTRATE SPECIFICITY OF SIAH2
5.1 Introduction
Siah1 and Siah2 share a highly conserved protein sequence which
reflects similar functional roles in a few instances (Confalonieri, Quarto et al.
2009; Kramer, Stauber et al. 2013). However, there are notable differences in
the expression of Siah1 and Siah2. Siah1 is induced by p53 upon genomic
stress (DNA damage), while Siah2 is induced by hypoxia (Matsuzawa,
Takayama et al. 1998; Qi, Nakayama et al. 2008).
Siah1 and Siah2 have a number of substrates in common but there are
some proteins targeted by one and not the other (Qi, Kim et al. 2013), which
might affect different cellular signaling networks. For example, DCC is
regulated by both Siah1 and Siah2 (Hu, Zhang et al. 1997). In some cases,
substrates are targeted for degradation by solely one Siah member, for
example TRAF2 is targeted for proteasomal degradation by Siah2 but not
Siah1 (Habelhah, Frew et al. 2002). In most publications however, only one
Siah family member is analyzed and often it is not clear which Siah family
member is under scrutiny. Thus, further investigation is needed to determine
whether or not the identified substrates are regulated by each of Siah isoform.
Animal model system uniformly supports an oncogenic role of siah
proteins in various cancers, whereas cell based and patient cohort observations
describe Siah2 as tumor promoter and Siah1 as tumor suppressor in most cases
(chapter 1). A recent review highlights the different roles of Siah proteins,
highlighting the necessity for more in-depth understanding of both Siah1 and
Siah2 protein function (Wong and Moller 2013). In spite of this, most of the
studies reported, do not discriminate Siah1 and Siah2 expression in same
cancer type and therefore, are not conclusive whether both or only one is a
valuable prognostic marker.
A report investigating the physiological functions of Siah1 and Siah2
genes by generating knock-out mice demonstrated that Siah1a knockout mice
are sub viable and growth retarded whereas Siah2 knock-out mice are
completely viable. However Siah2 Siah1a double mutant mice die at birth
93
(Frew, Hammond et al. 2003). This supports the notion that Siah1 and Siah2
proteins have both distinct and overlapping functions, but their exact roles are
still not clear. Because of these differences, they are also thought to have
unique roles.
Hence, we decided to investigate the critical residues involved in
substrate specificity of Siah2 with respect to Siah1, which will allow us to gain
a better understanding of their unique roles. In order to identify critical
residues, we verified the binding studies with other Siah substrates; β catenin
(DNA damage) and Nrf2 (Hypoxia) in addition to PHD3 (Hypoxia).
β-catenin is a multifunctional protein that plays an essential role in the
transduction of Wnt signals. Studies reported that Siah-1 mediates a β-catenin
degradation pathway linking p53 activation to cell cycle control with the
interacting protein. However, this degradation is mediated through a complex
including Ebi, Sip and APC, which facilitates the degradation of β-catenin in a
p53-dependent manner (Liu, Stevens et al. 2001; Matsuzawa and Reed 2001).
On the other hand it is reported that Siah2 mediates PHD3 degradation
under hypoxic conditions. It is also highlighted that PHD3 might require an
unknown adaptor protein for Siah2 mediated degradation (Nakayama, Frew et
al. 2004). However, our binding studies show the direct binding of Siah2 with
PHD3 (Fig 14). So we also analyzed the direct binding of Siah isoforms with
β-catenin along with PHD3 to gain more insights about substrate specificity.
Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is a key
transcriptional activator of antioxidant-response elements (AREs) and a
central regulator of the induction of antioxidant responsive enzymes. Under
normal conditions, Nrf2 is constantly degraded through the ubiquitin-
proteasome pathway in a Keap1-mediated manner (McMahon, Itoh et al.
2003). It has been very recently reported that Nrf2 is regulated by Siah2 under
hypoxic condition and the direct binding of Siah2 with Nrf2 was also reported
(Baba, Morimoto et al. 2013). In this respect our final findings were also
verified with Nrf2 to confirm the role of identified critical residues.
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5.2 Results
In general, the roles of Siah1 and Siah 2 are expected to overlap and
the unique features of each protein are not clear. We tried to investigate the
Siah1-PHD3 interaction in order to know is there any significant difference in
the level of binding of PHD3 with Siah1 and Siah2. In this respect, a FLAG
tagged Siah1 expression plasmid in pcDNA3.1 was generated. Cloning was
confirmed and coimmunoprecipitation studies were carried out in HEK293T
cells.
5.2.1 Siah1-PHD3 interaction
It has been reported that Siah2 is more efficient than Siah1 in
destabilizing over expressed PHD3 (Nakayama, Frew et al. 2004). Another
study reported that Siah1 (human) and Siah1a (mouse) reduced PHD3 protein
levels similar to Siah2 but Siah1a M180K mutant did not alter the PHD3
protein levels suggesting that the Siah substrate-binding groove is important
for PHD ubiquitination and degradation (Moller, House et al. 2009). However
no direct binding of Siah1 with PHD3 has been reported so far.
In this regard, we first investigated the interaction of Siah1 with PHD3
(Fig 35). To this end, HEK293T cells were cotransfected with the
corresponding expression constructs, followed by immunoprecipitation using
an anti-flag antibody. Interestingly we observed a weak interaction of SBD of
Siah1 with PHD3 when compared to Siah2 with PHD3 (Fig 35a). A
subsequent reciprocal immunoprecipitation assay was also performed to check
the binding of both full length and SBD of Siah1 with PHD3 as it shows weak
binding with the FLAG immunoprecipitaion of the SDB of Siah1. For this,
HEK293T cells were cotransfected with the corresponding expression
constructs, followed by immunoprecipitation using an anti-HA antibody. HA
immunoprecipitation of PHD3 exhibits no binding of PHD3 with Siah1 (Fig
35b). Hence the data obtained by both immunoprecipitation and reciprocal
immunoprecipitation assays, suggest the difference in binding levels between
Siah1 and Siah2.
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Figure 35. Association of Siah1 with PHD3. HEK293T cells were transfected in 60-mm cell culture plates for 2 days with expression
constructs for the proteins indicated at the top of panel. The cells were lysed. (a) The lysates were subjected to FLAG-IP and aliquots of the cell
lysates were analyzed by western blotting with the anti-HA antibodies. Weak binding of SBD of Siah1 with PHD3 was observed compared to
Siah2. (b) The lysates were subjected to HA-IP. Immunoprecipitates and aliquots of the cell lysates were analyzed by Western blotting with the
anti-FLAG antibodies. Both the Full length and SBD of Siah1 did not show binding with PHD3 by HA-IP (reciprocal immunoprecipitation). The
membrane was also immunoblotted using anti-FLAG or anti-HA antibody for the detection of protein in the lysates.
a b
96
5.2.2 SBD of Siah1 and Siah2 region swapped to obtain chimeric forms
Amino acid residues 90-282 (193) and 130-322(193) contribute to the
SBD of Siah1 and Siah2, respectively. In order to avoid confusion residue
numbers for both the SBD are labeled as 1-193 (Fig. 36). To identify which
region of SBD favours the binding, region swapping using fusion PCR was
performed as described in Materials and Methods, in which residues from 101-
193 region of Siah1 was swapped with the corresponding region of Siah2 and
vice versa to obtain the SBD of Siah1-2 and Siah2-1 constructs (Fig 36).
Figure 36. Diagrammatic representation of SBD of Siah1 and Siah2. SBD
of Siah1 and Siah2 (Top panel). SBD of Siah1-2 and Siah2-1(bottom panel).
Corresponding full length residue numbers are given in given in parentheses.
5.2.3 Binding of wild type (WT) and chimera SBD's with substrates
Next, we investigated the interactions of the SBD of Siah1-2 and
Siah2-1 with PHD3. Along with this, direct interaction of β-catenin with the
WT and chimeric Siah isoforms were also analyzed using
coimmunoprecipitation. To this end, HEK293T cells were cotransfected with
the corresponding expression constructs, followed by immunoprecipitation
using an anti-FLAG antibody. In these experiments, only WT SBD of Siah2
interacted with PHD3, whereas Siah2-1 interaction was markedly reduced
compared to WT SBD, suggesting the importance of the region 101-193 in
binding. Interestingly Siah1-2 did not exhibit any binding to PHD3 even
though it contains the swapped region of Siah2 (Fig 37). These results suggest
97
that both regions of SBD 1-100 and 101-193 are equally important for binding
with substrate. We observed no direct binding of β-catenin with both SBDs of
(Fig 37). This study supports the finding that Siah1 mediates β-catenin
degradation through SIP or APC by UV-induced DNA damage whereas Siah2
causes PHD3 degradation under hypoxia conditions and suggests that the
substrate specificity of Siah seems to be dependent on the types of stress that
cells encounter.
Figure 37. Association of wild type and chimeric SBD's of Siah with its
substrates. HEK293T cells were co-transfected in 60mm plate with
pcDNA3.1 containing HA-β-Catenin, HA-PHD3, FLAG-SBD-Siah2,FLAG
SBD-Siah1, FLAG-SBD-Siah1-2, FLAG SBD of Siah2-1 as indicated in the
top of panel. Forty eight hours after transfection, the cells were lysed and cell
lysates were subjected to FLAG immunoprecipitation and then immunoblotted
using anti-HA antibody. We also immunoblotted using anti-FLAG antibody
for the detection of protein in the lysates (bottom panel).
5.2.4 Identification of critical residues unique for Siah2
In order to identify the critical residues in SDB of Siah2 which confer a
substrate specificity, sequence alignment was performed to identify the unique
amino acids in which Siah1 and Siah2 differ. Pairwise sequence alignment
was performed by the EMBOSS Needle tool, which creates an optimal global
alignment of two sequences using the Needleman-Wunsch algorithm (Rice,
98
Longden et al. 2000). From the alignment of SBD of the Siah isoforms, twenty
six amino acids are different while the remaining residues are identical or
conserved (Fig. 38). Out of 26, ten amino acids are found be less similar or
different, based on amino acid properties. Hence we narrowed to 10 amino
acids for further mutational analysis.
Figure 38. Sequence alignment of SBD of Siah1 and Siah2. Unique 26
aminoacids are highted in grey. Dissimilar amino acids are highlighted by ‘*’.
Highly Similar amino acids are highlighted by ‘:’ and identical amino acids
are highlighted by ‘|’.
5.2.5 Mutagenesis of Siah1-2 and Siah2-1
Immunoprecipitation studies between SBD of swapped clones, Siah1-2
and Siah2-1, revealed that both the regions 1-100 and 101-193 are important
for binding. Out of the 10 residues that differ between Siah1 and Siah2, 6
amino acid of Siah2 in region 1-100 remain under the Siah2-1construct and
remaining 4 amino acids of Siah2 in region 101-193 fall under the swapped
Siah1-2 construct. From the corresponding 6 amino acid positions of Siah1 in
Siah1-2 clone, three were mutated back to Siah2 (Siah1-2Mut). Similarly from
the remaining four corresponding Siah1 amino acid position in Siah2-1 clone,
two were mutated back to Siah2 (Siah2-1Mut) (Fig. 39).
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Figure 39. Representation of mutagenesis and distribution of unique
amino acids in chimeric forms of Siah. (a) 10 unique amino acids in which
Siah1 and Siah2 differ are highlighted in diagrammatic representation of SBD
of Siah1-2 and Siah2-1 (top panel). Three mutated residues of Siah1 in Siah1-
2 clone in region 1-100 to Siah2 and two mutated residues of Siah1 in Siah2-1
clone in region 101-193 to Siah2 are highlighted (bottom panel).
Corresponding full length residue number Siah2 are indicated in parentheses.
(b) sequence alignment of SBD of Siah1-2 and Siah2-1 highlighting the
respective mutations.
b
a
100
5.2.6 Binding of wild type and mutated chimeric SBD's with substrates
We proceeded with coimmunoprecipitation studies of the SBDs of
Siah1-2Mut and Siah2-1Mut with PHD3 and Nrf2. PHD3 is a well-studied
hypoxia substrate and Nrf2 is a recently identified novel hypoxia substrate
whose direct binding has been reported. The results show that Siah2-1Mut
regains its binding with PHD3 and Nrf2, equivalent to binding levels of WT,
proving the crucial roles of the amino acids Leu121 (original number 250) and
Ala160 (289) of Siah2 in binding (Fig 40). On the other hand, Siah1-2Mut
(E17S, P57S and F98H) did not exhibit the strong binding compared to the
Siah2 WT and Siah2-1Mut, which verifies the importance of the other three
residues, His21(150), Pro26(155) and Ala62(191) in binding. Fig 40b also
shows the specificity of Nrf2 towards the SBD of Siah2 but not that of Siah1.
The AxVxP motif, is the consensus sequence found in some of the Siah
substrate proteins (House, Frew et al. 2003; House, Hancock et al. 2006). Both
Nrf2 and PHD3 have the AXVXP motif at positions 170–174 in Nrf2
(AQVAP) and at positions 176-180 in PHD3 (ADVEP). Siah2 binds to Nrf2
through binding motif (Baba, Morimoto et al. 2013). The mutational study
analysis of Siah isoforms and their chimeric forms with both PHD3 and Nrf2
are consistent in terms of direct binding, suggesting the importance of the
identified residues in Siah2. Since Siah2-1M restore binding equivalent to
Siah2 WT, we next investigated which of the two mutants (either one or both)
is critical for this preferential binding. For this point mutants were made and
the binding with PHD3 was analysed. We observed that Q250L restores
binding compared to T289A suggesting the importance of Leucine 250 in the
C terminal region (Fig 41a). Similarly, in the N terminal region of Siah1-2, we
made remaining 3 mutants and also all 6 mutants to investigate the binding
with PHD3. But both 3M and 6M only partially restores binding but not
equivalent to Siah2 WT (Fig. 41b). We next focused on the 10 similar amino
acid on the N terminal region of which Serine 133 and tyrosine 164 could be
invovled in hyrogen bonding with its OH group. So we made additional 3
mutant constructs which include 8M(6+SY), 7M(6+S) and 7M(6+Y). Binding
of these mutants with PHD3 was analysed. It was observed that 8M mutant
increased binding compared to 6M alone (Fig. 41c). This results suggest that
all the amino acid in the Nterminal region might be involved in binding.
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Figure 40. Siah2-1Mut restores its binding with its substrates. HEK293T cells were transfected with (a) WT FLAG SBD of Siah1, WT
FLAG SBD of Siah2 , FLAG Siah1-2Mut, FLAG Siah2-1Mut, HA PHD3 or empty vector; (b) WT FLAG SBD of Siah1, WT FLAG SBD of
Siah2 , Siah1-2Mut, Siah2-1Mut, Nrf2 or empty vector as indicated on the top of panel, followed by immunoprecipitation (IP) of cell lysates
with FLAG antibody. Immunoprecipitates were then analyzed for coimunoprecipitates of PHD3 and Nrf2 by Western blotting (WB). Both the
results showed that Siah2-1Mut restores binding with its substrates.
a b
102
Figure 41. Analyses of Binding between mutated 2-1 and 1-2 chimeric SBD (a) Binding of wild type and mutated 2-1SBD Chimeric with
PHD3; (b&c) Binding of wild type and mutated 1-2SBD Chimeric with PHD3.
HA- PHD3
FLAG-SBD Siah1 WT
FLAG-SBD Siah2-1 M1
FLAG-SBD Siah2-1 M2
FLAG-SBD Siah2 WT
Empty pcDNA3.1
HA- PHD3
FLAG-SBD Siah
HA
FLAG
FLAG-SBD Siah2-1
IP-FLAG Lysates
HA- PHD3
FLAG-SBD Siah1 WT
FLAG-SBD Siah2-1 3M
FLAG-SBD Siah2-1 6M
FLAG-SBD Siah2 WT
Empty pcDNA3.1
HA- PHD3
FLAG-SBD Siah
HA
FLAG
FLAG-SBD Siah 2-1
IP-FLAG Lysates
HA- PHD3
FLAG-SBD Siah2 WT
FLAG-SBD Siah2-1 8M
FLAG-SBD Siah2-1 7MS
FLAG-SBD Siah1-2
Empty pcDNA3.1
HA- PHD3
FLAG-SBD Siah
HA
FLAG
FLAG-SBD Siah 6M
FLAG-SBD Siah2-1 7MY
IP-FLAG Lysates
Heavy Chain
Light Chain
b c
a
103
5.2.7 Crystallization
Simultaneously, attempts were carried out to solve the structure of
SBD of Siah2 to analyze whether any significant change in the fold could
contribute to the differential binding. Even though the crystal structures of
Siah1 SBD with the peptide EKPAAVVAPITTG from SIP and that for murine
Siah, in complex with a peptide from phyllopod (discussed in chapter 1.5), are
known, the Siah2 structure is still important to gain more insights into the
mechanism of specificity. Hence, we attempted to crystallize the MBP-SBD of
Siah2.
Purified MBP-Siah2-SBD was concentrated up to 10mg/ml and its
homogeneity was analyzed using dynamic light scattering (DLS). The results
confirm that MBP-SBD-Siah2-H6 is homogenous but the predicted molecular
weight was very high suggesting homogeneous aggregation after
concentrating (Fig 41).
Crystallization screening for MBP-SBD-Siah2-6xHis was performed
by hanging drop vapor diffusion method at room temperature using various
commercially available Hampton and Emerald biosystems screens. 1 μl of
purified protein was mixed with 1 μl of the corresponding reservoir solution
and allowed to equilibrate against 500 μl of the reservoir solution at room
temperature. Tiny crystals not suitable for diffraction was obtained in wizard
screen I. Further optimization of the initial conditions is underway.
Crystallization trials were also aided by the use of the Mosquito robotic
crystallization system (TTP Labtech, UK). The robotic screening was carried
out using hanging drop method in 96 well plates. 0.2 µL of protein sample was
mixed with 0.2 µL of mother liquor. Crystallization screens with Wizard
screens 1 to 4 were setup using this method. Currently the drops are under
observation.
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Figure 42. DLS Profile of MBP-SBD of Siah2 at 10mg/ml.
5.3 Discussion
There could be various reasons for the varied substrate specificity of
both Siah1 and Siah2, in spite of their high sequence similarity in terms of
direct binding. One apparent reason is that one or more unique amino acids
that is specific for Siah2, favors this selective binding. The other reason could
be the differences in the conformation of the two proteins. However, this can
be compared only when the crystal structure of Siah2 is available. We first
intended to identify the critical residues involved in selective binding of Siah2
and then we attempted to solve the structure of SBD of Siah2. From our first
study, we narrowed down 5 critical amino acids, specific for Siah2, that confer
to substrate specificity. Our results suggest that two regions of the SBD of
Siah2 are involved in binding and we believe that further validation of these
residues by using specific Siah1 and Siah2 substrates will provide us with a
better insight into the unique functions of these proteins. Our study highlights
the differences in binding between Siah1 and Siah2 with their substrates based
on over-expression studies.
105
CHAPTER 6. CONCLUSION AND FUTURE DIRECTIONS
6.1 Conclusion
6.1.1 Siah2 inhibition as anti-cancer therapy
The SBD of Siah1 and Siah2 are highly conserved with 86% sequence
similarity, making it challenging to design Siah2 specific inhibitor. The N-
terminal regions of Siah1 and Siah2 differ from each other (Della, Senior et al.
1993), which makes it feasible to design Siah2-specific RING domain
inhibitors. However, targeting the RING domain does not impair the ability of
Siah2 to bind to its substrate proteins via the SBD. Another important issue in
targeting the RING domain is its similarity among the Ring-finger ligase
family (Deshaies and Joazeiro 2009) and thus all the inhibitors for E3 ligases
obtained so far inhibit multiple ligases (Garber 2005). The Siah proteins are
unique in their SBD, which adopts a pocket and forms the binding site for
adaptor and substrate containing an AxVxP motif, a consensus motif found in
most of the Siah binding proteins (House, Frew et al. 2003). Thus, screening
inhibitors against the Siah2 SBD may lead to identification of specific
inhibitors that will not affect other ring-finger E3 ligase. Blocking the SBD
using a peptide inhibitor is capable of reducing tumor growth and metastasis in
various models (Qi, Nakayama et al. 2008; Shah, Stebbins et al. 2009; Qi,
Nakayama et al. 2010). So far, menadione is the only available Siah2 specific
inhibitor, identified from a Meso-Scale-based assay of 2000 compounds
(Shah, Stebbins et al. 2009). Because of high homology, SBD inhibitors, if
available, will inhibit both Siah1 and Siah2. In general, the roles of Siah1 and
Siah 2 are expected to overlap and unique feature for each of the proteins is
not clear. However based on the current understanding of the role of Siah
isoforms in cancer, Siah1 is more often described as tumor suppressor and
Siah2 as an oncogene.
Addressing the above facts, our results has contributed the following
findings,
1. In the first part of the thesis, SBDD approach was implied and we have
identified five drug like molecules for SBD of Siah2. These drug-like
molecules were tested for their efficacy to be potential inhibitors against Siah2
106
by biophysical, biochemical, in vitro and in vivo binding assays, which were
carried out in the second part of this thesis. The identified compounds were
experimentally validated for their activity in disrupting the interaction of Siah2
with its high affinity substrate PHD3 by in vitro and in vivo immuno
precipitation assays. The thermodynamic properties of the binding between
SBD and PHD3 or SBD and the compounds were also characterized. From
these studies, compound 5 is a promising inhibitor among all. The anti-
proliferative and cytotoxic properties of the compounds were also analyzed by
the MTS assay.
2. In our initial high throughput virtual screening for compounds against the
SBD of Siah2, menadione was used as a reference ligand for achieving
specificity in screening. However the compound specificity for Siah1 and
Siah2 was not addressed due to the high sequence similarity and the possibility
of overlapping functions. Current understanding highlights the controversial
roles of Siah1 and Siah2 in cancer. The partial activity of the previously
screened compounds might be due to the lack of specificity. In this regard, the
third part of the thesis, intends to identify critical amino acids involved in the
substrate specificity of the SBD of Siah2 with respect to Siah1. With the help
of chimeric and mutational analysis, a few residues were narrowed down for
substrate specificity that might help in developing Siah2 specific inhibitors in
future and could also address the cross-talk between the roles of Siah1 and
Siah2 in cancer.
Overall, our work presents an attempt to translate the mechanistic
information of Siah2 mediated hypoxic signaling in cancer to structure based
design of chemical inhibitors which might be useful for anticancer therapy.
This approach may initiate broad implications in drug discovery efforts
targeting diverse signaling networks. To more clearly define the mechanism of
action, it will be a priority in future studies to obtain the structure of Siah2
alone or in complex with its inhibitors and their derivative.
107
6.2 Future directions
Based on the experiments and results reported in this thesis, the
following continuation is proposed. Site directed mutagenesis of the identified
residues should be performed to further narrow down to specific residues that
would confer specificity for Siah2 substrates. Using high-throughput virtual
screening (HTVS) more specific lead compounds can be screened against the
SBD of Siah2 using the identified residues that show better substrate
specificity. HTVS from other databases like ZINC, NCI and FDA, which were
not available during the initial screening, has to be undertaken. In addition, the
identified compounds should be cross validated against Siah1 using in vitro
and in vivo studies as the specificity selection of the compounds was not
implied in our initial screening.
To understand the structural and functional role of Siah2 with respect
to the identified residues, site directed mutation studies could be performed on
the full length Siah2, to check the affect on activity of the protein to its
substrate. In order to further substantiate the findings, another choice would
have been to use a peptide such as the PHYL peptide that is necessary and
sufficient for SBD binding to confirm the importance of identified residues.
Also various Siah substrates (both unique and common for Siah1 and Siah2)
can be tested to against this identified unique residues which could help us in
addressing the cross talk between the roles of Siah1 and Siah2.
More crystallization trials should be undertaken to determine the
structure of SBD of Siah2. As crystal formation of Siah2 wildtype was
limiting step which could answer most of the hypotheses, it is recommended to
attempt crystalization trails with mutated Siah2 residues. This could render a
better comparative picture of the Siah2 protein scaffold with respect to Siah1.
Co-crystallization of the SBD-inhibitor complex along with apo structure, will
provide more insights in understanding the interactions between them.
Their structural backbone of the inhibitors could serve as building
blocks in designing better drug-like molecules targeting E3 ligases in cancer
therapy. Further side chain modification, with the help of medicinal and
combinatorial chemistry can be undertaken to increase the solubility of the
inhibitors (Fig. 42). Further screens with different bio-relevant buffer systems,
108
termed as pH profiling will allow the detection of a potential counter ion
exchange and formation of more stable/less-soluble salts of a molecule that
will lead to precipitation in vivo.
Figure 43. Example illustration of steps involved in combinatorial
chemistry based design of ligands. An example of a drug molecule from a
database is presented in the above figure. Using combinatorial chemistry,
where the backbone scaffold of the drug molecules has been slightly modified
with an additional side chain of benzene rings that is in blue colour.
109
APPENDICES
APPENDIX 1
Table A1. List of constructs and corresponding primers
Recombinant
Constructs
Vector
Forward
Primer(FP)
Reverse
Primer(RP)
Restiction
Sites
FP RP
MBP-SBD-
Siah2
Modified
pET Duet
TTCAATCATGC
CCAGCATCAG
GAACCTGGCTA
T
TTCTGCGGCCGC
GTGGTGATGGT
GATGATGTCAA
C ATGTAGAAAT
AGTAACATTG
EcoRI NotI
GST-SBD-
Siah2
pGEX6P-1 ATGCGGATCCG
TGGCCTCGGCA
GTCC
ATGCCTCGAGTC
AACATGTAGAA
ATAGTAACATT
G
BamHI XhoI
MBP-SBD-
PHD3
Modified
pET Duet
GCGGAATTCAT
GCCCCTGGGAC
ATC
ACAGCGGCCGC
TCAGTCTTCA EcoRI NotI
FLAG-SBD-
Siah1 pcDNA3.1
TGTAAGCTTGT
GGCT AAT
TCAGTAC
GCATTCTAGATT
ATGGACAACAT
GTAGAAATAG
HindIII XbaI
FLAG-SBD-
Siah2 pcDNA3.1
ATCGTAAGCTT
AGCCGCCCGTC
CTC
GTGCTCTAGATT
AACATGTAGAA
ATAGTAACATT
G
HindIII XbaI
FLAG-Siah1 pcDNA3.1
TTGCAAGCTTA
TGAGCCGCCCG
TCCTCC
ACGCTCTAGATT
C ACA TGG AAA
TAG
HindIII XbaI
FLAG-Siah2 pcDNA3.1
GTGCAAGCTTA
TGGTGGCCTCG
GCAGTCC
ATCGTCTAGATT
ATGGACAACAT
GTAGAAATAG
HindIII XbaI
PHD3-His pET-M
TGTGAATTCAA
TGCC
CCTGGGAC
ACACTCGAGTT
AGTCTTCAGTGA
G GG
EcoRI XhoI
110
APPENDIX 2
A2.1 Purification of PHD3 by refolding
PHD3 was initially cloned in the pETM vector with N terminal His tag.
It was expressed as an insoluble protein in E. coli (BL21). The proteins in the
inclusion bodies was denatured using 6 M GnHCL. The denatured protein was
first purified with the Ni-NTA resin and then refolded by dialysis. Beyond 2
M GnHCL, the protein tends to precipitate completely. Hence dialysis was
stopped at 1.5M GnHCL and then purified by gel filtration using an S75
column with 0.5M GnHCL.
Fig. 43a shows the gel filtration results of refolded PHD3. Most of the
refolded protein got aggregated. Fig. 43b shows the presence of the PHD3
protein in the gel filtration fractions (mostly in the aggregates) and the protein
size by SDS-PAGE. As the refolding was not successful, purification of MBP-
PHD3 was carried out.
Aggregates
Phd3
a
111
Figure 44. Purification of PHD3-His by refolding. (a) The gel filtration
profile of Phd3 with His tag when loaded onto a size exclusion column. (b)
SDS-PAGE gel showing the FPLC fractions of the refolded protein with 0.5 M
GnHCL run on an S200 gel filtration column. Lanes 1-7 are aggregates.
A2.2 MS-MS Analysis of SBD of Siah2 and PHD3
For MS/MS analysis, sample digestion, desalting and concentration
steps were carried out by using the Montage® In-Gel digestion Kits (Millipore
Corp.) Protein spots were analyzed using an Applied Biosystems 4700
Proteomics Analyzer MALDI-TOF/TOF (Applied Biosystems, Framigham,
MA, USA). Data processing and interpretation was carried out using the GPS
Explorer Software (Applied Biosystems) and database searching was achieved
by using the MASCOT program (Matrix Science Ltd., London, UK). The
NCBI database was used for combined MS and MS/MS search. Samples for
the mass spectrometry experiments were prepared and analyzed from PPC,
DBS-NUS facility.
b
112
Figure 45. Identification of MBP-SBD of Siah2 and MBP-PHD3 by
peptide mass fingerprint. The peptide mass fingerprint of peak from gel-
filtration (Fig. 20 and Fig 23) result revealed the identity of the Siah2 and
PHD3.
113
APPENDIX 3
A3.1 IC50 and affinity
In biochemical assays, the dissociation constant Kd is termed as the
inhibition constant Ki. It is important to be aware that data from inhibition is
often not reported as Ki (or, equivalently, Kd), but instead as the IC50, the
concentration of a ligand that reduces particular activity by 50%. The
difference between IC50 and Ki results from the fact, that in a biochemical
binding assay of a competitive inhibitor, the inhibitor is not the only molecule
trying to bind the target but also competing with the substrate, so the
concentration of the ligand needed to reduce the activity by 50% depends on
the concentration of substrate and how tightly it binds the enzyme. IC50 is not
a direct indicator of affinity although the two can be related at least for
competitive agonists and antagonists by the Cheng-Prusoff equation (Cheng
and Prusoff 1973). In general, then, the IC50 is expected to be greater than Ki
(the Cheng-Prusoff equation). IC50 is not a direct indicator of affinity, nor is
Kd/Ki a direct indicator of inhibition. However, the two can be related using
the Cheng-Prusoff equation:
where Ki is the binding affinity of the inhibitor, IC50 is the functional strength
of the inhibitor, [S] is substrate concentration and Km is the concentration of
substrate at which enzyme activity is at half maximal (but is frequently
confused with substrate affinity for the enzyme, which it is not). Whereas the
IC50 value for a compound may vary between experiments depending on
experimental conditions, the Ki is an absolute value. An ITC assay tends to
require a larger quantity of sample than an enzyme inhibition assays.
However, ITC has the advantage of not requiring the development of a
specialized substrate whose reaction can be detected. The natural heat release
of the binding reaction is what is required.
114
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