1. An Introduction to Bioinformatics Protein Structure Prediction

2. Aims Understand the use of algorithms Recognize different approaches Understand the limitations Objectives Predict occurrence of aspects of structure To select appropriate tools

3. Introduction Structure has several levels –1  primary –2  secondary –3  tertiary –4  quaternary

4. 1  primary Amino acid sequence NH 2 -MRLSWYDPDFQARLTRSNSKCQGQLEV YLKDGWHMVC SQSWGRSSKQWEDPSQASKVCQRLNCGVPLSLGPFLVTYTP QSSIICYGQLGSFSNCSHSRNDMCHSLGLTCLE-COOH

5. 2  secondary Localized organisation  -helices and  - sheets

6. 3  tertiary Three-dimensional organisation

7. 4  quaternary Multi protein assembly

8. The problem….. The best way is by X-ray crystallography or NMR etc… Structure databases only hold about 10,000 + structures Therefore devise programs to deduce structural solutions Complex!

9. Secondary Structure prediction Signal peptides Intracellular targeting Trans-membrane  -helices  -helices and  -sheets Super-secondary structure (motifs)

10. Signal peptides Short N-terminal amino acid sequences Direct to membrane Cleaved after translocation SignalP –Nobel Prize 1999 Günter Blobel

11. SignalP predicts signal peptide cleavage sites Only first 50-70  Using neural networks

12. Is the sequence a signal peptide? # Measure Position Value Cutoff Conclusion max. C 25 0.910 0.37 YES max. Y 25 0.861 0.34 YES max. S 12 0.960 0.88 YES mean S 1-24 0.892 0.48 YES # Most likely cleavage site between pos. 24 and 25: SRA-LE

13. Intracellular targeting TargetP Predict subcellular location of eukaryotic protein Presequences –Chloroplasts –Mitochondria –signal peptide

14. Transmembrane Domains Lots of programs TMHMM –  -helices –hydrophobic   –helix topology –R or K +ve charge cytoplasmic side –Hidden Markov Modelling

15. Paste as FASTA file e.g Serotonin Receptor

16. Predicts the transmembrane domains and orientation

17.  -helices and  -sheets GOR algorithim Assigns each residue to one conformational state of  -helix, extended chain, reverse turn or coil 64.4% accurate Many other sites most use multiple alignments

18.  -helices and  - sheets 10 20 30 40 50 60 70 | | | | | | | MKFSWRTALLWSLPLLVVGFFFWQGSFGGADANLGSNTANTRMTYGRFLEYVDAGRITSVDLYENGRTAI cccceeeeeecccceeeeeeeeccccccccccccccccccchhhhcceeeeccccceeeeeeccccceee VQVSDPEVDRTLRSRVDLPTNAPELIARLRDSNIRLDSHPVRNNGMVWGFVGNLIFPVLLIASLFFLFRR eeccccccchhhhccccccccchhhhhhhhhccccccccceecccceeeeecccccchhhhhhhhheeec SSNMPGGPGQAMNFGKSKARFQMDAKTGVMFDDVAGIDEAKEELQEVVTFLKQPERFTAVGAKIPKGVLL cccccccccchhhhcchhhhhhhhccceeeecchhhhhhhhhhhhhhhhhhcccchhhhhcccccceeee VGPPGTGKTLLAKAIAGEAGVPFFSISGSEFVEMFVGVGASRVRDLFKKAKENAPCLIFIDEIDAVGRQR ecccccchhhhhhhhhcccccceeecccccceeeeeecccchhhhhhhhhcccccceeeecchhhhcccc GAGIGGGNDEREQTLNQLLTEMDGFEGNTGIIIIAATNRPDVLDSALMRPGRFDRQVMVDAPDYSGRKEI ccccccccchhhhhhhhhhhhhcccccccceeeeeeccccchhhhhhccccccceeeeecccccccchhh LEVHARNKKLAPEVSIDSIARRTPGFSGADLANLLNEAAILTARRRKSAITLLEIDDAVDRVVAGMEGTP hhhhhhhhccccccchhhhccccccccchhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhheeecccccc LVDSKSKRLIAYHEVGHAIVGTLLKDHDPVQKVTLIPRGQAQGLTWFTPNEEQGLTTKAQLMARIAGAMG cccccccchhhhhcccceeeeeecccccccceeeecccccccceeccccccccchhhhhhhhhhhhhhhh GRAAEEEVFGDDEVTTGAGGDLQQVTEMARQMVTRFGMSNLGPISLESSGGEVFLGGGLMNRSEYSEEVA hhhhhhhcccccceeeccccchhhhhhhhhhhhhhhccccccccccccccceeeecccccccccchhhhh TRIDAQVRQLAEQGHQMARKIVQEQREVVDRLVDLLIEKETIDGEEFRQIVAEYAEVPVKEQLIPQL hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhcccccchhhhhhhhhhcccccccccccc

19. Super-secondary Structure Secondary structure elements combined into specific geometric arrangements known as motifs Beta corner

20. Super-secondary Structure Several programs/websites for specific domains e.g. PAIRCOIL and MULTICOIL - detect coiled- coiled regions –regions separating domains TRESPASSER - detects Leucine Zippers –Leu-X6-Leu-X6-Leu-X6-Leu protein interaction domain NPS@nalysis Helix-Turn-Helix –Protein interaction/DNA binding

21. Integrated stucture prediction One stop shop! Predict Protein at EBI –secondary structure –solvent accessibility globular regions –transmembrane helices coiled-coil regions –a multiple sequence alignment P roSite sequence motifs –low-complexity retions –ProDom domain assignments

22. Tertiary Structure Prediction Homology modelling Fold recognition Threading Model building

23. Protein sequence (primary structure) Database searching for homologues Homologue of known structure No homologue of known structure Comparative modelling 3D-structure Fold prediction, ab initio methods etc.

24. Homology Modelling Method of choice following BLAST search SWISS Model is a good WWW Interface URL: http://www.expasy.ch/swissmod/SWISS-MODEL.html

25. Requires at least one sequence of known 3D-structure with significant similarity to the target sequence. Compare the target sequence with database - FastA and BLAST. Sequences with a FastA score 10.0 standard deviations above the mean of the random scores or a P(N) lower than 10-5 (BLAST) considered for the model building Restrict to those which share at least 30% residue identity Homology Modelling

26. Framework construction – compare atom positions - C  s Build non-conserved loops Complete backbone - add other atoms Add side chains Refine

27. Insulin like gene from C.elegans Red = Insulin Blue = ILGF1

28. What if I have no homologue? Ab initio methods - Threading Sequence of unknown structure Thread through a through a sequence of known structure Move query sequence through residue by resudue and compare computationally – include thermodynamic criteria, solvent accessibility, secondary structure information Computing intensive

29. http://www.cs.bgu.ac.il/~bioinbgu/form.html