Introduction to Bioinformatics Introduction to Bioinformatics

Transcription

1 Dr. rer. nat. Gong Jing Cancer Research center Medicine School of Shandong University

2 Case Study 2

3 Case 1 Case 2 How SIGIRR inhibit the TLR4 and 7 signaling pathways? Model Construction Tolllike receptor ectodomains. 3

4 Case 1 How SIGIRR inhibit the Toll-like receptors TLR4 and 7 signaling pathways? 4

5 Background : Structure of Toll-like receptors (TLRs) Ectodomain (ECD) Leucine-rich repeat (LRR) Transmembrane domain TIR domain 5

6 TLR signaling pathways 6

7 E Determined crystal structures of TLR ECD-ligand-ECD complexes: A: human TLR2-1 C: human TLR4-4 E: human TLR5-5 B: mouse TLR3-3 D: mouse TLR2-6 7

8 Upon receptor activation, an intracellular TIR signaling complex is med between the receptor and downstream adaptor TIR domains. TIR DD 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 ms 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. 8

9 Leucine-rich repeats (LRRs) TLR SIGIRR (single immunoglobulin interleukin-1 receptorrelated molecule) Single immunoglobulin (Ig) Toll/interleukin-1 receptor (TIR) domain TIR domain 73 AA C-terminal tail SIGIRR (Single immunoglobulin interleukin-1 receptor-related molecule, TIR8) was initially identified as an Ig domain-containing receptor of the TLR/IL-1R superfamily. But, both the extracellular and intracellular domains of SIGIRR differ from those of other Ig domain-containing receptors 9

10 SIGIRR acts as an endogenous inhibitor MyD88-dependent TLR and IL-1R signaling. Systemic Lupus Erythematosus (SLE, 系统性红斑狼疮 ) is caused by TLR7-mediated induction of type I interferons. mouse B6 lpr/lpr Sigirr +/+ mouse B6 lpr/lpr Sigirr -/- Lech et al., JEM,

11 bind to TLR4 inhibit signaling ΔN yes yes ΔC yes yes ΔTIR no no Fulllength ΔN : lacking the extracellular Ig domain ΔTIR : lacking the intracellular TIR domain ΔC : lacking the C-tail of the TIR domain Conclusion: only the TIR domain (excluding the C-tail part) is necessary SIGIRR to inhibit TLR4 signaling. yes yes Qin et al., 2005 JBC 11

12 Hypothesis: SIGIRR blocks the molecular interface of TLR4 and MyD88 via its TIR domain Objective: to find a structural explanation these TIR-TIR interactions. 1. Structure prediction of TIR domains of TLRs, MyD88 and SIGIRR. 2. Structure analysis/docking. 12

13 Step 1 : model construction Amino acid sequences of the target proteins, human TLR4, TLR7, MyD88, and SIGIRR were extracted from the Expasy Uniprot 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 TIR domains of TLR1 (1FYV), TLR2 (1FYW), TLR10 (2J67) and IL-1RAPL (1T3G). In the secondary structure-aided alignments 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 inmation to improve the alignment quality. The secondary structure of each target was predicted by PSIPRED. 13

14 Step 1 : model construction The resulting structures exhibit a typical TIR domain conmation in which a central five-stranded parallel β-sheet (βa- βe) is surrounded by a total of five 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 BBloop connects β-strand B and α-helix B. The structure of NSF-N was identified as a template SIGIRR s C-tail through protein threading. 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. crystal structure of IL1-RAPL (1T3G) 14

15 Step 1 : model construction 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. 15

16 Step 1 : model construction Surface charge distribution (APBS electrostatics generated by VMD) 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. 16

17 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 Institute (EBI). 17

18 Step 3 : hypothesis model construction From a large number of docking results we established such a model of SIGIRR inhibiting the TLR7 signaling pathways. 18

19 Step 3 : hypothesis model construction From a large number of docking results and we established such a model of SIGIRR inhibiting the TLR7 signaling pathways. 19

20 Step 3 : hypothesis model construction 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. 20

21 Step 3 : hypothesis model construction From a large number of docking results and we established such a model of SIGIRR inhibiting the TLR4 signaling pathways. 21

22 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 efts 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: IF: 4.06, SCI citation times: 10 Jing Gong, Tiandi Wei, Robert W. Stark, Ferdinand Jamitzky, Wolfgang M. Heckl, Hans-Joachim Anders, Maciej Lech and Shaila C. Röessle. 22

23 Case 2 Model Construction Toll-like receptor ectodomains 23

24 English Courses TLR sequences So far, there are about 3000 protein sequences of different TLRs from different species saved in primary protein databases. The number will continue growing. 24

25 Background : Structure of Toll-like receptors (TLRs) Ectodomain (ECD) Leucine-rich repeat (LRR) Transmembrane domain TIR domain 25

26 LRR identification 22 LRR + 1 CT 6 LRR + 2 N/CT 6 LRR + 1 CT LRR identification ECD of human TLR3 23 LRRs + 2 N/CT LRRs 17 LRR + 2 N/CT 22 LRR 26

27 LRR identification LxxLxLxxNxLxxLxxxxFxxLxx PTNITVLNLTHNQLRRLPAANFTR PTNITVLNLTHNQLRRLPAANFTR NITVLNLTHNQLRRLPAANFTRY PTNITVLNLTHNQLRRLPAA NITVLNLTHNQLRRLPAANFTRY 27

28 TollML database 2734 sequences, 2011/08/01 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 LRR Highly Conserved Segment (HCS) Variable Segment (VS) Inserted Segment (IS) 28

29 Construction pipeline 29

30 Domains LRRs Segments 30

31 LRR Finder main algorithm : a position-specific weight matrix of LRR motifs Position Amino acids Example: LPTNLTVLMLLHNQLRRLPAANFTRYSQLTSLDVGFNT % cutoff Yes 31 No

32 Example: LPTNLTVLMLLHNQLRRLPAANFTRYSQLTSLDVGFNT Yes No No filter Sensitivity / Specificity Cutoff score Cutoff Sensitivity Specificity Spe. (filter)

33 TollML and LRRFinder are freely available at Any internet user can search and download data from the database, but only registered users can define and save labels arbitrary entries. TollML: a database of toll-like receptor strutural motifs J. Mol. Model., 2010, 16(7): IF: 2.34, SCI citation times: 4 Jing Gong, Tiandi Wei, Ning Zhang, Ferdinand Jamitzky, Wolfgang M. Heckl, Shaila C. Rössle and Robert W. Stark 33

34 2010/11 34

35 Construction pipeline 35

36 36

37 Every LRR structure can be viewed with an online molecular viewer Jmol. 37

38 To simplify the homology modeling, the similarity search was implemented. It returns the structures of the most similar LRRs a structure unknown LRR. At first, a global pairwise sequence alignment with sequence identity will be generated 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. 38

39 LRRML contains individual three-dimensional LRR structures with manual structural annotations. It presents useful sources homology modeling and structural analysis of LRR proteins. This database is freely available at LRRML: a conmational database and an XML description of leucine-rich repeats (LRRs) BMC Struct. Biol., 2008, 8:47 IF: 3.06, SCI citation times: 10 Tiandi Wei, Jing Gong*, Ferdinand Jamitzky, Wolfgang M. Heckl, Robert W. Stark and Shaila C. Rössle *corresponding author 39

40 In mammalian, 13 TLRs have been identified. Protein sequences are available a number of mammalian species. Using these sequences, a complete molecular phylogenetic analysis 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. 40

41 E Since 2000 the crystal structure of human TLR3 ECD was firstly reported, five 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, human TLR5 homodimer 41

42 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 timeconsuming and sometimes it is very difficult to crystallize proteins, computational methods can perm fast and large-scale structural predictions based on the sequences. Currently, the most accurate protein structure prediction method is homology modeling. 42

43 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 aementioned 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. 43

44 Flowchart of the LRR template assembly approach 44

45 Threading method Our Full-length templates LRR assembly Crystal structure TLR3 ECD 45

46 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. 46

47 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 perm liganddocking and molecular replacement. Zhang et al.,

48 Average target-template sequence identity >= 45% 48

49 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. 49

50 These models can be used to perm 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 infectious diseases. A leucine-rich repeat assembly approach homology modeling of human TLR5-10 and mouse TLR11-13 ectodomains. J. Mol. Model., 2011, 17(1):27-36 IF: 2.34, SCI citation times: 4 Tiandi Wei, Jing Gong*, Ferdinand Jamitzky, Wolfgang M. Heckl, Shaila C. Rössle and Robert W. Stark *corresponding author 50

51 Exam Thesis 51

52 Exam Thesis Topic : What can bioinmatics do you? Language : English Word count : Deadline : 2012/11/30 Submit to : gongjing@sdu.edu.cn 52

53 Format : 1. The following word processor file mats are acceptable the thesis: Microsoft Word (.doc) Rich text mat (RTF) Portable document mat (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. 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. 4. All pages should be numbered. 5. 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. 53

54 asdfsadf Thank you very much your attention! 54