Thinking Outside the Box: Applications Including Finding Off-targets for Major Pharmaceuticals

Thinking Outside the Box: Applications Including Finding

Off-targets for Major Pharmaceuticals

Philip E. [email protected]

Agenda

• Overall Theme - Thinking differently about proteins:– Spherical harmonics and phylogeny– The Gaussian Network Model and new

modes of motion– The Geometric Potential for Describing

Ligand Binding Sites– SOIPPA for finding off-site targets

The Curse of the Ribbon

The conventional view of a protein (left) has had a remarkable impact on our understanding of living systems, but its time for new views It is not how a ligand sees a protein after all.

Limitations

• A local viewpoint – does not capture the global properties of the protein

• A local viewpoint does not capture the global properties of a protein

• Cartesian coordinates do not necessarily capture the properties of the protein

• Comparative analysis is limited

Agenda




Protein Kinase A – Open Book View

Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49

Superfamily Members – The Same But Different

Scheeff & Bourne 2005 PLoS Comp. Biol. 1(5): e49

• Roots in spherical harmonics• Parameter space and boundary

conditions can be a variety of properties• Order of the multipoles defines the

granularity of the descriptors• Bottom line – interpreted as shape

descriptors

An Alternative Approach: Multipolar Representation

Gramada & Bourne 2006 BMC Bioinformatics 7:242

Geometric Comparison Does Not Reflect Biological Reality


Results – Protein Kinase Like Superfamily Alignment

Clear distinction between families.

Some clustering seen inside TPKs that resemble various groups, even though there is little shape discrimination at this level.


Results – Protein Kinase Like Superfamily Alignment


Possibilities – Structure Based Phylogenetic Analysis

Scheeff & Bourne Multipoles

Gramada & Bourne 2007 PLoS ONE submitted

Agenda




Protein Motion

OrderedStructures

DisorderedStructures

Structures exist in a spectrum from order to disorder

Gu, Gribskov & Bourne 2006 PLoS Comp. Biol. 2(7) e90

Obtaining Protein Dynamic InformationProtein Structures Treated as a

3-D Elastic Network

Bahar, I., A.R. Atilgan, and B. Erman Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential.

Folding & Design, 1997. 2(3): p. 173-181.

Gaussian Network Model• Each C is a node in the network.

• Each node undergoes Gaussian-distributed fluctuations influenced by neighboring interactions within a given cutoff distance. (7Å)

• Decompose protein fluctuation into a summation of different modes.

Functional Flexibility Score

• Utilize correlated movements to help define regional flexibility with functional importance.

Functionally Flexible Score

For each residue:1. Find Maximum and

Minimum Correlation.2. Use to scale normalized

fluctuation to determine functional importance.


Identifying FFRs in HIV Protease


Other Examples BPTI and Calmodulin


Side Note: Gaussian Network Model vs Molecular Dynamics

• GNM relatively course grained• GNM fast to compute vs MD

–Look over larger time scales–Suitable for high throughput

Agenda




Motivation

• What if we can characterize a protein-ligand binding site from a 3D structure (primary site) and search for that site on a proteome wide scale?

• We could perhaps find alternative binding sites (secondary sites) for existing pharmaceuticals?

• We could use it for lead optimization and possible ADME/Tox prediction

Background – PDB Contains Major Pharmaceuticals Bound to Receptors

Generic Name Other Name Treatment PDBid

Lipitor Atorvastatin High cholesterol 1HWK, 1HW8…

Testosterone Testosterone Osteoporosis 1AFS, 1I9J ..

Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH

Viagra Sildenafil citrate ED, pulmonary arterial hypertension

1TBF, 1UDT, 1XOS..

Digoxin Lanoxin Congestive heart failure

1IGJ

Background – Superfamily (Derived from Structure) Covers 38% of the Human Proteome

http://supfam.mrc-lmb.cam.ac.uk/SUPERFAMILY

Background – Advantage to Using Functional Site Similarity

ProteinSequence/Structure

Similarity

ProteinFunctional Site

Similarity

Small moleculeSimilarity

. Not adequately reflecting functional relationship. Not directly addressing drug design problem

• Poor correlation between structure and activity• Infinite chemical space

. Build closer structure- function relationships . Limit chemical space through co-evolution

Overview of Algorithm

Protein structure is represented with C atoms only and is characterized with a geometric potential

• tolerant to protein flexibility and model uncertainty

Optimum superimposition is achieved with a maximum weighted sub-graph algorithm with geometric constraints

• sequence order independent to detect cross-fold relationships

• to identify sub site similarity

Functional site similarity is measured with both evolutionary correlation and physiochemical similarity

• to distinguish divergent and convergent evolution

Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

Characterization of the Ligand Binding Site - Conceptual

1. Represent the protein structure

2. Determine the environmental boundary

3. Determine the protein boundary

4. Computation of the geometric potential

5. Computation of the virtual ligand

1

2

3

4

5

a b

c


• Initially assign C atom with a value that is the distance to the environmental boundary

• Update the value with those of surrounding C atoms dependent on distances and orientation – atoms within a 10A radius define i

0.20.1)cos(

0.1

iDiPiPGP

neighbors

Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments

Characterization of the Ligand Binding Site - Conceptual


Discrimination Power of the Geometric Potential

0

0.5

1

1.5

2

2.5

3

3.5

4

0 11 22 33 44 55 66 77 88 99

Geometric Potential

binding sitenon-binding site

• Geometric potential can distinguish binding and non-binding sites

100 0

Geometric Potential Scale

Boundary Accuracy of Ligand Binding Site Prediction

0

5

10

15

20

25

10 20 30 40 50 60 70 80 90 100

Sensitivity (%)

Dis

trib

utio

n (%

)

0

10

20

30

40

50

60

70

10 20 30 40 50 60 70 80 90 100

Specificity (%)

Dis

trib

utio

n (%

)

• ~90% of the binding sites can be identified with above 50% sensitivity

• The specificity of ~70% binding sites identified is above 90%

So Far…

• Geometric potential dependant on local environment of a residue – relative to other residues and the environmental boundary

• Geometric potential reasonably good at discriminating between ligand binding sites and non-ligand binding sites

• Boundary of the binding site reasonably well defined

• How to compare sites ???

Agenda




• Geometric and graph characterization of the protein structure

• Chemical similarity matrix and evolutionary relationship with profile-profile comparison

• Optimum alignment with maximum-weight sub-graph algorithm

Identification of Functional Similarity with Local Sequence Order Independent Alignment

Xie and Bourne 2007 PNAS, Submitted

Similarity Matrix of Alignment

Chemical Similarity• Amino acid grouping: (LVIMC), (AGSTP), (FYW), and

(EDNQKRH)• Amino acid chemical similarity matrix

Evolutionary Correlation• Amino acid substitution matrix such as BLOSUM45• Similarity score between two sequence profiles

ia

i

ib

ib

i

ia SfSfd

fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, Sb are the PSSM of profile a and b, respectively Xie and Bourne 2007 PNAS, Submitted

Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm

L E R

V K D L

L E R

V K D L

Structure A Structure B

• Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix

• The maximum-weight clique corresponds to the optimum alignment of the two structures

Efficient Functional Site Comparison with Evolutionary and Geometric Constraints

• The search space is segmented with the residue clusters determined from the geometric potential

• The nodes and edges are greatly reduced with the robust residue boundary orientation and neighbors

a

b c

1

2

a a2 1

bb

12

cc

21

a a2 1

bb

12

cc

21

+

The time complexity is almost linearly dependant on the number of residues

Improved Performance of Alignment Quality and Search Sensitivity and Specificity

0

10

20

30

40

50

60

70

80

90

<1.0 <3.0 <5.0 <7.0 <9.0 <11.0

RMSD (Angsgroms)

Freq

uenc

y (%

)

Amino Acid GroupingChemical SimilaritySubstitution MatrixProfile-Profile

0

0.005

0.01

0.015

0.02

0.025

0.03

0 0.04 0.08 0.12 0.16 0.2

True Positive Ratio

Fals

e Po

sitiv

e R

atio

Amino Acid GroupChemical SimilaritySubstitution MatrixProfile-Profile

RMSD distribution of the aligned common fragments of ligands from 247 test cases showing four scores: amino acid grouping, chemical similarity, substitution matrix and profile-profile.

.

So What is the Potential of this Methodology?

Lead Discovery from Fragment Assembly

• Privileged molecular moieties in medicinal chemistry

• Structural genomics and high throughput screening generate a large number of protein-fragment complexes

• Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery

1HQC: Holliday junction migration motor protein from Thermus thermophilus1ZEF: Rio1 atypical serine protein kinase from A. fulgidus

Lead Optimization from Conformational Constraints

• Same ligand can bind to different proteins, but with different conformations

• By recognizing the conformational changes in the binding site, it is possible to improve the binding specificity with conformational constraints placed on the ligand

1ECJ: amido-phosphoribosyltransferase from E. Coli1H3D: ATP-phosphoribosyltransferase from E. Coli

Finding Secondary Binding Sites for Major Pharmaceuticals

• Scan known binding sites for major pharmaceuticals bound to their receptors against the human proteome

• Try and correlate strong hits with known data from the literature, databases, clinical trials etc. to provide molecular evidence of secondary effects

A Case Study

Selective Estrogen Receptor Modulators (SERM)

• One of the largest classes of drugs

• Breast cancer, osteoporosis, birth control etc.

• Amine and benzine moiety

Xie, Wang and Bourne 2007 Nature Biotechnology, Submitted.

Adverse Effects of SERMs

cardiac abnormalities

thromboembolic disorders

ocular toxicities

loss of calcium homeostatis ?????


Ligand Binding Site Similarity Search On a Proteome Scale

• Searching human proteins covering ~38% of the drugable genome against SERM binding site

• Matching Sacroplasmic Reticulum (SR) Ca2+ ion channel ATPase (SERCA) TG1 inhibitor site

• ER ranked top with p-value<0.0001 from reversed search against SERCA

ER

0 20 40 60 80

0.00

0.02

0.04

0.06

Score

Den

sity

SERCA


Structure and Function of SERCA

• Regulating cytosolic calcium levels in cardiac and skeletal muscle

• Cytosolic and transmembrane domains

• Predicted SERM binding site locates in the TM, inhibiting Ca2+ uptakes


Binding Poses of SERMs in SERCA from Docking Studies

• Salt bridge interaction between amine group and GLU

• Aromatic interactions for both N-, and C-moiety

6 SERMS A-F (red)

Off-Target of SERMs

cardiac abnormalities

thromboembolic disorders

ocular toxicities

loss of calcium homeostatis SERCA !

in vivo and in vitro Studies TAM play roles in regulating calcium uptake activity of cardiac SR TAM reduce intracellular calcium concentration and release in the platelets Cataract results from TG1 inhibited SERCA up-regulations EDS increases intracellular calcium in lens epithelial cells by inhibiting SERCA

in silico Studies Ligand binding site similarity Binding affinity correlation

Conclusion• By thinking differently about how to

represent proteins we have seen potential value in:– Phylogenetic analysis– The study of the dynamics of proteins– Improvements to the drug discovery

process

Acknowledgements

Jenny GuProtein Motions

Apostol GramadaMultipole Analysis

Support Open Access

Lei Xie

Jian Yang

Swiss-Prot - 20 Year Celebration

www.pdb.org • [email protected] on Drug Development

Affinity (ER Site) Affinity (SERCA) Affinity Difference

Bazedoxifene(BAZ) -9.44 +/- 0.54 -7.23 +/- 0.13 2.21

Lasofoxifene(LAS) -8.66 +/- 0.40 -6.54 +/- 0.20 2.12

Ormeloxifene(ORM) -8.67 +/- 0.18 -5.84 +/- 0.33 2.83

Raloxifene(RAL) -8.08 +/- 0.64 -5.78 +/- 0.23 2.30

4-hydroxytamoxifen(OHT) -7.67 +/- 0.47 -5.40 +/- 0.15 2.27

Tamoxifen(TAM) -7.30 +/- 0.28 -5.64 +/- 0.28 1.66

• Taking account of both target and off-target for lead optimization

• Drug delivery and administration regime

A Protein is More than the Union of its Parts

• Breaking the protein into parts changes the object of the comparison

• This is interpreted in many cases to imply that the rmsd measure is inadequate.

• The reality is that it is the aligning of structure that breaks the triangle inequality and not the measure per se. The reason for failure is that we effectively compare different objects then we say we do.

From Røgen & Fain (2003), PNAS 100:119-124

New Tricks – Protein Representation


Roots in Spherical Harmonics• Parameterization

+ boundary conditionsgCharge distribution (i.e. structure) Ð

f qlm out;M lm in;qilm; M i

lmg

Scalar potential

Gramada & Bourne 2006 BMC Bioinformatics 7:242New Tricks – Protein Representation

Spatial distribution ofa scalar quantity

• “Out” Multipoles

qlm = Pi=1

Nrl

i Y ãlm(òi;þi); l = 0;ááá;1 ; m = à l;ááá;l

For a given rank l, they form a 2l+1 dimensional vector under 3D rotations

ql = fql;mgm=à l;ááá;l

Vector algebra applies => metric properties



New Tricks – Protein Representation

The multipoles can be interpreted as shape descriptors

In principle, from the entire series of multipoles one can reconstruct the scalar field and therefore the density, i.e the entire set of Cartesian coordinates, i. e. of the structure with a geometric level of detail

The partitioning of the multipole series according to various representation of the rotational group allows for a multi-scale description of the structure


Gramada & Bourne 2006 BMC Bioinformatics 7:242New Tricks – Protein Representation

Documents

Thinking Outside the Box: Applications Including Finding Off-targets for Major Pharmaceuticals