Protein structure and homology modeling

1. Protein structure and homology modeling Morten Nielsen, CBS, BioCentrum, DTU

2. Objectives • Understand the basic concepts of homology modeling • Learn why even sequences with very low sequence similarity can be modeled • Understand why is %id such a terrible measure for reliability • See the beauty of sequence profiles? • Learn where to find the best public methods

3. Outline • Why homology modeling • How is it done • How to decide when to use homology modeling • Why is %id such a terrible measure • What are the best methods? • Models in immunology

4. Why protein modeling? • Because it works! • Close to 50% of all new sequences can be homology modeled • Experimental effort to determine protein structure is very large and costly • The gap between the size of the protein sequence data and protein structure data is large and increasing

5. Homology modeling and the human genome

6. ~200.000 in Swiss-Prot ~ 2.000.000 if include Tremble Swiss-Prot database

7. PDB New Fold Growth • The number of unique folds in nature is fairly small (possibly a few thousands) • 90% of new structures submitted to PDB in the past three years have similar structural folds in PDB Old folds New PDB structures New folds

8. Identification of fold • If sequence similarity is high proteins share structure (Safe zone) • If sequence similarity is low proteins may share structure (Twilight zone) • Most proteins do not have a high sequence homologous partner Rajesh Nair & Burkhard Rost Protein Science, 2002, 11, 2836-47

9. Why %id is so bad!! 1200 models sharing 25-95% sequence identity with the submitted sequences (www.expasy.ch/swissmod)

10. Identification of correct fold • % ID is a poor measure • Many evolutionary related proteins share low sequence homology • Alignment score even worse • Many sequences will score high against every thing (hydrophobic stretches) • P-value or E-value more reliable

11. Score 150 10 hits with higher score (E=10) 10000 hits in database => P=10/10000 = 0.001 P(Score) Score What are P and E values? • E-value • Number of expected hits in database with score higher than match • Depends on database size • P-value • Probability that a random hit will have score higher than match • Database size independent

12. Identify fold (template) for modeling Find the structure in the PDB database that resembles your new protein the most Can be used to predict function Align protein sequence to template Simple alignment methods Sequence profiles Threading methods Pseudo force fields Model side chains and loops How to do it

13. Template identification • Simple sequence based methods • Align (BLAST) sequence against sequence of proteins with known structure (PDB database) • Sequence profile based methods • Align sequence profile (Psi-BLAST) against sequence of proteins with known structure (PDB) • Align sequence profile against profile of proteins with known structure (FFAS) • Sequence and structure based methods • Align profile and predicted secondary structure against proteins with known structure (3D-PSSM)

14. Sequence profiles • In conventional alignment, a scoring matrix (BLOSUM62) gives the score for matching two amino acids • In reality not all positions in a protein are equally likely to mutate • Some amino acids (active cites) are highly conserved, and the score for mismatch must be very high • Other amino acids are mutate almost for free, and the score for mismatch is lower than the BLOSUM score • Sequence profiles can capture these differences

15. Protein superfamily Protein world New Fold Protein family Protein fold Protein structure classification

16. Non-conserved Conserved Sequence profiles ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASKISMSEEDLLN TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWHGLLVPFGMDGVPGVSFPG---I -TSMAPAFGVQEFYRTVKQGDEVTVTIT-----NIDQIED-VSHGFVVVNHGVSME---I IE--KMKYLTPEVFYTIKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD--- -TSVAPSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAME---V ASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPEGVEGFKSRINDE---- TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP Matching any thing but G => large negative score Any thing can match

17. Sequence profiles Align (BLAST) sequence against large sequence database (Swiss-Prot) Select significant alignments and make profile (weight matrix) using techniques for sequence weighting and pseudo counts Use weight matrix to align against sequence database to find new significant hits Repeat 2 and 3 (normally 3 times!)

18. Example. >1K7C.A TTVYLAGDSTMAKNGGGSGTNGWGEYLASYLSATVVNDAVAGRSARSYTREGRFENIADV VTAGDYVIVEFGHNDGGSLSTDNGRTDCSGTGAEVCYSVYDGVNETILTFPAYLENAAKL FTAKGAKVILSSQTPNNPWETGTFVNSPTRFVEYAELAAEVAGVEYVDHWSYVDSIYETL GNATVNSYFPIDHTHTSPAGAEVVAEAFLKAVVCTGTSLKSVLTTTSFEGTCL • What is the function • Where is the active site?

19. Example. • Function • Run Blast against PDB • No significant hits • Run Blast against NR (Sequence database) • Function is Acetylesterase? • Where is the active site?

20. Example. Where is the active site? 1G66 Acetylxylan esterase 1USW Hydrolase 1WAB Acetylhydrolase

21. Example. Where is the active site? • Align sequence against structures of known acetylesterase, like • 1WAB, 1FXW, … • Cannot be aligned. Too low sequence similarity 1K7C.A 1WAB._ RMSD 11.2397 QAL 1K7C.A 71 GHNDGGSLSTDNGRTDCSGTGAEVCYSVYDGVNETILTF DAL 1WAB._ 160 GHPRAHFLDADPGFVHSDGTISH--HDMYDYLHLSRLGY

22. Example. Where is the active site? • Sequence profiles might show you where to look! • The active site could be around • S9, G42, N74, and H195

23. Example. Where is the active site? Align using sequence profiles ALN 1K7C.A 1WAB._RMSD = 5.29522 1K7C.A TVYLAGDSTMAKNGGGSGTNGWGEYLASYLSATVVNDAVAGRSARSYTREGRFENIADVVTAGDYVIVEFGHNDGGSLSTDN S G N 1WAB._ EVVFIGDSLVQLMHQCE---IWRELFS---PLHALNFGIGGDSTQHVLW--RLENGELEHIRPKIVVVWVGTNNHG------ 1K7C.A GRTDCSGTGAEVCYSVYDGVNETILTFPAYLENAAKLFTAK--GAKVILSSQTPNNPWETGTFVNSPTRFVEYAEL-AAEVA 1WAB._ ---------------------HTAEQVTGGIKAIVQLVNERQPQARVVVLGLLPRGQ-HPNPLREKNRRVNELVRAALAGHP 1K7C.A GVEYVDHWSYVDSIYETLGNATVNSYFPIDHTHTSPAGAEVVAEAFLKAVVCTGTSL H 1WAB._ RAHFLDADPG---FVHSDG--TISHHDMYDYLHLSRLGYTPVCRALHSLLLRL---L

24. Structural superposition Blue: 1K7C.A Red: 1WAB._

25. Where was the active site? Rhamnogalacturonan acetylesterase (1k7c)

26. Including structure • Sequence with in a protein superfamily share remote sequence homology • , but they share high structural homology • Structure is known for template • Predict structural properties for query • Secondary structure • Surface exposure • Position specific gap penalties derived from secondary structure and surface exposure

27. Structure biased alignment (3D-PSSM) http://www.sbg.bio.ic.ac.uk/~3dpssm/

28. CASP. Which are the best methods • Critical Assessment of Structure Predictions • Every second year • Sequences from about-to-be-solved-structures are given to groups who submit their predictions before the structure is published • Modelers make prediction • Meeting in December where correct answers are revealed

29. CASP6 results

30. The top 4 homology modeling groups in CASP6 • All winners use consensus predictions • The wisdom of the crowd • Same approach as in CASP5! • Nothing has happened in 2 years!

31. The Wisdom of the Crowds • The Wisdom of Crowds. Why the Many are Smarter than the Few. James Surowiecki One day in the fall of 1906, the British scientist Fracis Galton left his home and headed for a country fair… He believed that only a very few people had the characteristics necessary to keep societies healthy. He had devoted much of his career to measuring those characteristics, in fact, in order to prove that the vast majority of people did not have them. … Galton came across a weight-judging competition…Eight hundred people tried their luck. They were a diverse lot, butchers, farmers, clerks and many other no-experts…The crowd had guessed … 1.197 pounds, the ox weighted 1.198

32. The wisdom of the crowd! • The highest scoring hit will often be wrong • Not one single prediction method is consistently best • Many prediction methods will have the correct fold among the top 10-20 hits • If many different prediction methods all have a common fold among the top hits, this fold is probably correct

33. 3D-Jury (Best group) • Inspired by Ab initio modeling methods • Average of frequently obtained low energy structures is often closer to the native structure than the lowest energy structure • Find most abundant high scoring model in a list of prediction from several predictors • Use output from a set of servers • Superimpose all pairs of structures • Similarity score Sij = # of Ca pairs within 3.5Å (if #>40;else Sij=0) • 3D-Jury score = SijSij/(N+1) • Similar methods developed by A Elofsson (Pcons) and D Fischer (3D shotgun)

34. How to do it? Where is the crowd • Meta prediction server • Web interface to a list of public protein structure prediction servers • Submit query sequence to all selected servers in one go • http://bioinfo.pl/meta/

36. Meta Server Evaluating the crowd.

37. Meta Server Evaluating the crowd. 3D Jury

38. From fold to structure • Flying to the moon has not made man conquer space • Finding the right fold does not allow you to make accurate protein models • Can allow prediction of protein function • Alignment is still a very hard problem • Most protein interactions are determined by the loops, and they are the least conserved parts of a protein structure

39. Ab initio protein modeling Modeling of newfold proteins • Only when every thing else fails • Challenge • Close to impossible to model Natures folding potential

40. Challenge. Folding potential • New folds are in general constructed from a set of subunits, where each subunit is part of a known fold. • The subunits are small compared to the overall fold of the protein. No objective function exists to guide the global packing of the subunits. Objective function sij = 120aa dij = 6Å

41. A way to solution • Glue structure piece wise from fragments. • Guide process by empirical/statistical potential Fragments with correct local structure Natures potential Empirical potential

42. Example (Rosetta web server) www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php Rosetta prediction Structure

43. Take home message • Identifying the correct fold is only a small step towards successful homology modeling • Do not trust % ID or alignment score to identify the fold. Use p-values • Use sequence profiles and local protein structure to align sequences • Do not trust one single prediction method, use consensus methods (3D Jury) • Only if every things fail, use ab initio methods