1. Protein Tertiary Structure Prediction Structural Bioinformatics

2. Primary: amino acid linear sequence. Secondary:  -helices, β-sheets and loops. Tertiary: the 3D shape of the fully folded polypeptide chain The Different levels of Protein Structure

3. How can we view the protein structure ? Download the coordinates of the structure from the PDB http://www.rcsb.org/pdb/ Launch a 3D viewer program For example we will use the program Pymol The program can be downloaded freely from the Pymol homepage http://pymol.sourceforge.net/http://pymol.sourceforge.net/ Upload the coordinates to the viewer

4. Pymol example Launch Pymol Open file “1aqb” (PDB coordinate file) Display sequence Hide everything Show main chain / hide main chain Show cartoon Color by ss Color red Color green, resi 1:40 Help http://pymol.sourceforge.net/newman/user/toc.htmlhttp://pymol.sourceforge.net/newman/user/toc.html

5. Predicting 3D Structure –Comparative modeling (homology) Based on structural homology –Fold recognition (threading) Outstanding difficult problem Based on sequence homology

6. Comparative Modeling Similar sequences suggests similar structure

7. Sequence and Structure alignments of two Retinol Binding Protein

8. Structure Alignments The outputs of a structural alignment are a superposition of the atomic coordinates and a minimal Root Mean Square Distance (RMSD) between the structures. The RMSD of two aligned structures indicates their divergence from one another. Low values of RMSD mean similar structures There are many different algorithms for structural Alignment.

9. Dali (Distance mAtrix aLIgnment) DALI offers pairwise alignments of protein structures. The algorithm uses the three- dimensional coordinates of each protein to calculate distance matrices comparing residues. See Holm L and Sander C (1993) J. Mol. Biol. 233:123-138. SALIGN http://salilab.org/DBALI/?page=tools

10. Fold classification based on structure-structure alignment of proteins (FSSP) Page 293 FSSP is based on a comprehensive comparison of PDB proteins (greater than 30 amino acids in length) using DALI. Representative sets exclude sequence homologs sharing > 25% amino acid identity. http://www.ebi.ac.uk/dali/fssp

12. Comparative Modeling Comparative structure prediction produces an all atom model of a sequence, based on its alignment to one or more related protein structures in the database Similar sequence suggests similar structure

13. Comparative Modeling Accuracy of the comparative model is related to the sequence identity on which it is based >50% sequence identity = high accuracy 30%-50% sequence identity= 90% modeled <30% sequence identity =low accuracy (many errors)

14. Homology Threshold for Different Alignment Lengths Alignment length (L) Homology Threshold (t) A sequence alignment between two proteins is considered to imply structural homology if the sequence identity is equal to or above the homology threshold t in a sequence region of a given length L. The threshold values t(L) are derived from PDB

15. Comparative Modeling Similarity particularly high in core –Alpha helices and beta sheets preserved –Even near-identical sequences vary in loops

16. Comparative Modeling Methods MODELLER (Sali –Rockefeller/UCSF) SCWRL (Dunbrack- UCSF ) SWISS-MODEL http://swissmodel.expasy.org//SWISS-MODEL.html

17. Comparative Modeling Modeling of a sequence based on known structures Consist of four major steps : 1.Finding a known structure(s) related to the sequence to be modeled (template), using sequence comparison methods such as PSI-BLAST 2. Aligning sequence with the templates 3. Building a model 4. Assessing the model

18. Fold Recognition

19. Protein Folds A combination of secondary structural units –Forms basic level of classification Each protein family belongs to a fold Different sequences can share similar folds

20. HemoglobinTIM Protein Folds: sequential and spatial arrangement of secondary structures

21. Protein Folds A combination of secondary structural units –Forms basic level of classification Each protein family belongs to a fold Different sequences can share similar folds

22. Similar folds usually mean similar function Homeodomain Transcription factors

23. Protein Folds A combination of secondary structural units –Forms basic level of classification Each protein family belongs to a fold Different sequences can share similar folds

24. The same fold can have multiple functions Rossmann TIM barrel 12 functions 31 functions

25. Fold classification: Class: All alpha All beta Alpha/beta Alpha+beta Fold Superfamily Family SCOP Structure Classification Of Proteins

26. Retinol Binding Protein

27. Fold Recognition Methods of protein fold recognition attempt to detect similarities between protein 3D structure that have no significant sequence similarity. Search for folds that are compatible with a particular sequence. "the turn the protein folding problem on it's head” rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence

28. Basic steps in Fold Recognition : Compare sequence against a Library of all known Protein Folds (finite number) Query sequence MTYGFRIPLNCERWGHKLSTVILKRP... Goal: find to what folding template the sequence fits best There are different ways to evaluate sequence-structure fit

29. MAHFPGFGQSLLFGYPVYVFGD... Potential fold... 1)... 56)... n)... -10... -123... 20.5 There are different ways to evaluate sequence-structure fit

30. Programs for fold recognition TOPITS (Rost 1995) GenTHREADER (Jones 1999) SAMT02 (UCSC HMM) 3D-PSSM http://www.sbg.bio.ic.ac.uk/~3dpssm/

31. Ab Initio Modeling Compute molecular structure from laws of physics and chemistry alone Theoretically Ideal solution Practically nearly impossible WHY ? –Exceptionally complex calculations –Biophysics understanding incomplete

32. Ab Initio Methods Rosetta (Bakers lab, Seattle) Undertaker (Karplus, UCSC)

33. CASP - Critical Assessment of Structure Prediction Competition among different groups for resolving the 3D structure of proteins that are about to be solved experimentally. Current state - –ab-initio - the worst, but greatly improved in the last years. –Modeling - performs very well when homologous sequences with known structures exist. –Fold recognition - performs well.

34. What’s Next Predicting function from structure

35. Structural Genomics : a large scale structure determination project designed to cover all representative protein structures Zarembinski, et al., Proc.Nat.Acad.Sci.USA, 99:15189 (1998) ATP binding domain of protein MJ0577

36. ~300 unique folds in PDB Currently ~800 unique folds

37. ~1000- 3000 unique folds in “structure space” Estimated

38. Structure Genomics expectations ~ 5 proteins to characterize the sequence space corresponding to 1 fold ~10000-15000 new structures expected

39. As a result of the Structure Genomic initiative many structures of proteins with unknown function will be solved Wanted ! Automated methods to predict function from the protein structures resulting from the structural genomic project.

40. Approaches for predicting function from structure ConSurf - Mapping the evolution conservation on the protein structure http://consurf.tau.ac.il/

41. Approaches for predicting function from structure PHPlus – Identifying positive electrostatic patches on the protein structure http://pfp.technion.ac.il/

42. Approaches for predicting function from structure SHARP2 – Identifying positive electrostatic patches on the protein structure http://www.bioinformatics.sussex.ac.uk/SHARP2