Case Study - 卓越课程中心3.0course.sdu.edu.cn/G2S/eWebEditor/uploadfile/... · Case 2 Introduction to Bioinformatics. Case 1 How SIGIRR inhibit the Toll-like receptors TLR4

Case

Study

Introduction to Bioinformatics

How SIGIRR inhibit the TLR4 and 7 signaling pathways?

Case 1

Homology modeling of Toll-like receptor ectodomains.

Case 2


Case 1

How SIGIRR inhibit the Toll-like receptors TLR4 and 7 signaling pathways?


Leucine-rich repeat (LRR)

Ectodomain(ECD)

Transmembranedomain

TIR domain

Background : Structure of Toll-like receptors (TLRs)

TLRs belong to the Toll-like receptor/ interleukin-1 receptor (TLR/IL-1R) superfamily, which is defined by the presence of a conserved cytoplasmicToll/interleukin-1 receptor (TIR) domainconnected to an ectodomain through asingle transmembrane stretch. Their ectodomains consist of 16–28 leucine-rich repeats (LRRs).


TLR signaling pathways

These LRRs provide a variety of structural frameworks for the binding of protein and non-protein ligandsincluding lipopoly-saccharide (LPS), lipopeptide, CpG DNA, flagellin, and double-/single-stranded RNA.


TLRs are capable of recognizing ligands in a dimer form.

Determined crystal structures of TLR ECD-ligand-ECD complexes:

human TLR2-1,mouse TLR3-3,human TLR4-4,mouse TLR2-6.


Upon receptor activation, an intracellular TIR signaling complex is formed between the receptor and downstream adaptor TIR domains. MyD88(Myeloid differentiation primary response protein 88) was the first intracellular adaptor molecule characterized among all known adaptors in the TLR signaling. It consists of an N-terminal death domain (DD) separated from its C-terminal TIR domain by a linker sequence. MyD88 also forms a dimer through DD-DD and TIR-TIR domain interactions when recruited to the receptor complex. MyD88 can recruit IRAK (IL-1RI-associated protein kinases) through its DD to continue signaling and, finally, to induce the nuclear factor-kB (NF-kB) leading to the expression of type I interferons.

TIR

DD


Leucine-rich repeats (LRRs)

(single immunoglobulin interleukin-1 receptor-related molecule)

Single immunoglobulin (Ig)

Toll/interleukin-1receptor (TIR) domain

TIR domain

73 AA C-terminal tail

TLR SIGIRR

SIGIRR (Single immunoglobulin interleukin-1 receptor-related molecule), also known as TIR8, was initially identified as an Ig domain-containing receptor of the TLR/IL-1R superfamily. Both the extracellular and intracellular domains of SIGIRR differ from those of other Ig domain-containing receptors, as its single extracellular Ig domain does not support ligand-binding. Its intracellular TIR domain cannot activate NF-kB. Moreover, the TIR domain of SIGIRR extends that of the typical TLR/IL-1R superfamily member by >73 amino acids at the C-terminal (C-tail).


Instead, SIGIRR acts as an endogenous inhibitor for MyD88-dependent TLR and IL-1R signaling. This behavior was shown by over expression of SIGIRR in Jurkat or HepG2 cells which showed substantially reduced LPS, CpG DNA or IL-1-induced activation of NF-kB. Thus, SIGIRR has attracted tremendous research interest because of its regulating function in cancer-related inflammation and autoimmunity.

For example, systemic lupus erythematosus (SLE, 系统性

红斑狼疮) is caused by TLR7-mediated induction of type I interferons. Compared with wild type mice Sigirr-deficient mice develop excessive lymphoproliferation when introduced into the context of a lupus susceptibility gene.

Although the significance of SIGIRR has been widely acknowledged, its inhibition mechanism remains unclear owing to a lack of structural information.

mouse B6lpr/lprSigirr-/-

mouse B6lpr/lprSigirr+/+

Lech et al., JEM, 2008


bind to TLR4 inhibit signaling

ΔN yes yes

ΔC yes yes

ΔTIR no no

Full-length

yes yes

Mutagenesis studies investigated three deletion mutants of SIGIRR: ΔN (lacking the extracellular Igdomain), ΔTIR (lacking the intracellular TIR domain) and ΔC(lacking the C-tail of the TIR domain with deletion of residues 313–410).

The results showed that only the TIR domain (excluding the C-tail part) is necessary for SIGIRR to inhibit TLR4 signaling.

Nevertheless, detailed structural interaction mechanisms of SIGIRR’s TIR domain are still missing.Qin et al., 2005 JBC


Objective: to find a structural explanation for these TIR-TIR interactions.1. Structure prediction of TIR domains of TLRs, MyD88 and SIGIRR.2. Structure analysis/docking.

Hypothesis: SIGIRR blocks the molecular interface of TLR4 and MyD88 via its TIR domain


Step 1 : model construction

Amino acid sequences of the target proteins, human TLR4, TLR7, MyD88, and SIGIRR were extracted from the NCBI protein database.

Three-dimensional models of TLR4, TLR7, MyD88 and SIGIRR (without the C-tail) were constructed by homology modeling. Due to the homology of the target proteins, four common templates were obtained via BLAST search against the Protein Data Bank (PDB). They were TLR1 (1FYV), TLR2 (1FYW), TLR10 (2J67) and IL-1RAPL (1T3G).

In the secondary structure-aided alignments for the homology modeling, the average target-template sequence similarity of TLR4, TLR7, MyD88 and SIGIRR was 51.7%, 50.4%, 44.5% and 42.7%, respectively

Multiple sequence alignment of each target with the templates was generated with MUSCLE and analyzed with Jalview. Because the secondary structure of the TIR domain is composed of well-organized alternating β-strands and α-helixes, the alignments were adjusted manually according to the secondary structure information to improve the alignment quality. The secondary structure of each target was predicted by PSIPRED.



The resulting structures exhibit a typical TIR domain conformation in which a central five-stranded parallel β-sheet (βA- βE) is surrounded by a total offive α-helixes (αA–αE) on both sides. The loops are named by the letters of the secondary structure elements that they connect. For example, the BB-loop connects β-strand B and α-helix B. The structure of NSF-N was identified as a template for SIGIRR’s C-tail through protein threading.

crystal structure of IL1-RAPL (1T3G)

To improve the model quality, ModLoop was used to rebuild the coordinates of the low quality loop regions. Finally, model quality assessment programs: ProQ, ModFOLD and MetaMQAP were used to evaluate the output candidate models and select the most reliable one.



The BB-loop and αE of TLR4, TLR7 and MyD88, along with the BB-loop of SIGIRR, may be important to ensure binding specificity achieved by different combinations of TIRs during signaling.



Surface charge distribution (APBS electrostatics) of BB-loop and αE were represented with red indicating areas of negative charge and blue indicating positive charge.

Accordingly, all BB-loops can be divided into two self-complementary parts. The N-terminal (upper region of BB-loops) is negatively charged, whereas the C-terminal (lower region of BB-loops) is positively charged. The αEs, by contrast, are predominantly positive.


Step 2 : protein-protein docking

Unrestrained pairwise model docking included eight complexes of TIR domains: TLR4-TLR4, TLR7-TLR7, MyD88-MyD88, TLR4 dimer-MyD88 dimer (tetramer), TLR7 dimer-MyD88 dimer (tetramer), TLR4-SIGIRR, TLR7-SIGIRR and MyD88-SIGIRR. We used GRAMM-X and ZDOCK, which are widely accepted rigid-body protein-protein docking programs, to predict and assess the interactions between these complexes.

The buried surface interaction area of dimer models were calculated with the protein interfaces, surfaces and assemblies service (PISA) at the European Bioinformatics Institute (EBI).


Step 3 : hypothesis model construction

From a large number of docking results we established such a model of SIGIRR inhibiting the TLR7 signaling pathways.



From a large number of docking results and we established such a model of SIGIRR inhibiting the TLR7 signaling pathways.




Lech et al., 2010 J. Pathol.





Step 4 : Conclusion

In summary, we propose a residue-detailed structural framework of SIGIRR inhibiting the TLR4 and 7 signaling pathways. These results were obtained by computer modeling and are expected to facilitate efforts to design further site-directed mutagenesis experiments to clarity the regulatory role of SIGIRR in inflammatory and innate immune responses.

Inhibition of the Toll-like receptors TLR4 and 7 signaling pathways by SIGIRR: a computational approach

J. Struct. Biol., 2010, 169:323-330

IF: 4.06, SCI citation times: 5

Jing Gong, Tiandi Wei, Robert W. Stark, Ferdinand Jamitzky, Wolfgang M. Heckl, Hans-Joachim Anders,

Maciej Lech and Shaila C. Röessle.


Case 2

Homology modeling of Toll-like receptor ectodomains


TLR sequencesSo far, there are about 3000 protein sequences of different TLRs from different species saved in primary protein databases. The number will continue growing.

… …


Leucine-rich repeat (LRR)

Ectodomain(ECD)

Transmembranedomain

TIR domain

Background : Structure of Toll-like receptors (TLRs)

TLRs belong to the Toll-like receptor/ interleukin-1 receptor (TLR/IL-1R) superfamily, which is defined by the presence of a conserved cytoplasmicToll/interleukin-1 receptor (TIR) domainconnected to an ectodomain through asingle transmembrane stretch. Their ectodomains consist of 16–28 leucine-rich repeats (LRRs).


ECD ofhuman TLR3

23 LRRs+

2 N/CT LRRs

22 LRR + 1 CT

22 LRR6 LRR + 2 N/CT

6 LRR + 1 CT 17 LRR

+ 2 N/CT

LRR identification

Introduction to BioinformaticsEnglish Courses for Graduate Students

LRR identification

LxxLxLxxNxLxxLxxxxFxxLxx

PTNITVLNLTHNQLRRLPAANFTR

PTNITVLNLTHNQLRRLPAANFTR

NITVLNLTHNQLRRLPAANFTRY

PTNITVLNLTHNQLRRLPAA

NITVLNLTHNQLRRLPAANFTRY


LRR identification

Structural Motifs (3 Levels)Domains of each TLR

Signal Peptide (SP)Ectodomain (ECD)Transmembrane Domain (TD)TIR Domain

LRRs of each ECD

Segments of each LRRHighly Conserved Segment (HCS)Variable Segment (VS)Inserted Segment (IS)

2734 sequences, 2011/08/01


TollML database


Construction pipeline

Domain

s

LRRs

Segments


Position

Am

ino

acid

s


LRR Finder

main algorithm :a position-specific weight matrix of LRR motifs

YesYes%

Example: … LPTNLTVLMLLHNQLRRLPAANFTRYSQLTSLDVGFNT …3.800 1.054

cutoffcutoff

NoNo2.232

Sens

itivi

ty/ S

peci

ficity

Cutoff score

Cutoff 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5Sensitivity 0.942 0.933 0.924 0.916 0.907 0.886 0.868 0.858 0.842 0.822 0.805

Specificity 0.852 0.882 0.902 0.916 0.935 0.954 0.970 0.981 0.988 0.992 0.994

Spe. (filter) 0.914 0.930 0.953 0.959 0.972 0.981 0.987 0.991 0.994 0.996 0.997


3.800 1.0542.232Yes No No

Example: … LPTNLTVLMLLHNQLRRLPAANFTRYSQLTSLDVGFNT …

filterfilter


This database is freely available at http://tollml.lrz.de. Any internet user can search and download data from the database, but only registered users can define and save labels for arbitrary entries.

TollML: a database of toll-like receptor strutural motifs

J. Mol. Model., 2010, 16(7):1283-1289


Jing Gong, Tiandi Wei, Ning Zhang, Ferdinand Jamitzky, Wolfgang M. Heckl, Shaila C. Rössle and Robert W. Stark

2010/11


Construction pipeline




Every LRR structure can be viewed with an online molecular viewer – Jmol.


To simplify the homology modeling, the similarity search was implemented. It returns the structures of the most similar LRRs for a structure unknown LRR. At first, a global pairwisesequence alignment with sequence identity will be generated for the target LRR and each of the LRRs in the user selected set. Then, the most similar LRRs will be returned as template candidates, ranked by sequence identity.


LRRML contains individual three-dimensional LRR structures with manual structural annotations. It presents useful sources for homology modeling and structural analysis of LRR proteins. This database is freely available at http://tollml.lrz.de.

LRRML: a conformational database and an XML description of leucine-rich repeats (LRRs)

BMC Struct. Biol., 2008, 8:47


Tiandi Wei, Jing Gong*, Ferdinand Jamitzky, Wolfgang M. Heckl, Robert W. Stark and Shaila C. Rössle

*corresponding author


In mammalian, 13 TLRs have been identified. Protein sequences are available for a number of mammalian species. Using these sequences, a complete molecular phylogeneticanalysis and a phylogenetic tree of the known TLRs were reported. According to this tree, mammalian TLRs can be divided into six subfamilies. TLR1, 2, 6 and 10 belong to the TLR1 subfamily. TLR3 constitutes the TLR3 subfamily. TLR4 constitutes the TLR4 subfamily and TLR5 constitutes the TLR5 subfamily. TLR7, 8 and 9 compose the TLR7 subfamily. TLR11, 12 and 13 belong to the TLR11 subfamily.

Since 2000 the crystal structure of human TLR3 ECD was firstly reported, four crystal structures of receptor-ligand complexes have been determined.

They are :human TLR2-1 heterodimer, mouse TLR3 homodimer, human TLR4 homodimer, mouse TLR2-6 heterodimer.


TLR sequences

~3000 known TLR sequences

… …


Compared with the small number of crystal structures, there are about 3000 known protein sequences of different TLRs from different species. Because the X-ray crystallography remains time-consuming and sometimes it is very difficult to crystallize proteins, computational methods can perform fast and large-scale structural predictions based on the sequences. Currently, the most accurate protein structure prediction method is homology modeling.

When applying the homology modeling on the TLR ectodomains, we encountered a problem. The sequence identity between the target and the full-length template(s), namely the aforementioned crystal structures, is much lower than 30% because of diverse numbers and arrangements of LRRs contained in the TLR ectodomains. This problem is also described by the phylogenetic tree. Thus we could not get a proper model.

To solve this problem we developed an LRR template assembly approach with the help of both TollML and LRRML databases.



Flowchart of the LRR template assembly approach


Threading method Crystal structureFull-length templates LRR assembly TLR3 ECD

Superimposition of the model (blue) and crystal structure (orange) of TLR3 at the two ligand interaction regions. Global root mean square deviation: 1.96 Å and 1.90 Å.


Zhang et al., 2009.


If the root mean square deviation between a model and a structure is < 3 Å, the model is very good and can be used to perform ligand-docking and molecular replacement.


Average target-template sequence identity >= 45%


Superimposition of the model (green) and crystal structure (orange) of TLR6. Global root mean square deviation: 1.94 Å; ligand-binding region: 1.18 Å.


These models can be used to perform ligand-docking studies or to design mutagenesis experiments to investigate TLR ligand-binding mechanisms, and thus help to develop new TLR agonists and antagonists that have therapeutic significance for infectious diseases.

A leucine-rich repeat assembly approach for homology modeling of human TLR5-10 and mouse TLR11-13 ectodomains.

J. Mol. Model ., 2011, 17(1):27-36


Tiandi Wei, Jing Gong*, Ferdinand Jamitzky, Wolfgang M. Heckl, Shaila C. Rössle and Robert W. Stark

*corresponding author

Exam

Thesis



Exam Thesis

Topic : What can bioinformatics do for you?

Language : English

Word count : 1000 - 2000

Deadline : 2011/11/30

Submit to : [email protected]


Format : 1. The following word processor file formats are acceptable for the thesis:

Microsoft Word (.doc) Rich text format (RTF) Portable document format (PDF)

2. You should choose a legible font and use double line-spacing. Your font should be no smaller than 11 pt font and no bigger than 12 pt font with standard margins.3. Use hard returns only to end headings and paragraphs, not to rearrange lines.4. All references must be numbered consecutively, in square brackets, in the order in which they are cited in the text, followed by any in tables or legends. 5. All pages should be numbered. 6. Greek and other special characters may be included. If you are unable to reproduce a particular special character, please type out the name of the symbol in full.


Thank you very much for your attention！

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Case Study - 卓越课程中心3.0course.sdu.edu.cn/G2S/eWebEditor/uploadfile/... · Case 2 Introduction to Bioinformatics. Case 1 How SIGIRR inhibit the Toll-like receptors TLR4