Protein Fold Recognition Morten Nielsen, CBS, Department Of

Protein Fold recognition

Morten Nielsen, CBS, Department of Systems Biology, DTU Objectives

• Understand the basic concepts of fold recognition • 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 – Position specific scoring matrices (PSSMs) Objectives

• and ..... • See the beauty of sequence profiles – Position specific scoring matrices (PSSMs) Background. 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 Growth of databases Homology modeling and the human genome How can we do it?

• Identify template(s) – initial alignment • Can give you protein function • Improve alignment • Can give you active site • Backbone generation • Loop modeling • Most difficult part • Side chains • Refinement • Validation 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 Structural Genomics in North America

• 10 year $600 million project initiated in 2000, funded largely by NIH • AIM: structural information on 10000 unique proteins (now 4-6000), so far 1000 have been determined • Improve current techniques to reduce time (from months to days) and cost (from $100.000 to $20.000/structure) • 9 research centers currently funded (2005), targets are from model and disease-causing organisms (a separate project on TB proteins) Homology modeling for structural genomics

What a new fold can give

Roberto Sánchez et al. Nature Structural Biology 7,!986!-!990 (2000) Example.

>1K7C.A TTVYLAGDSTMAKNGGGSGTNGWGEYLASYLSATVVNDAVAGRSARSYTREGRFENIADV VTAGDYVIVEFGHNDGGSLSTDNGRTDCSGTGAEVCYSVYDGVNETILTFPAYLENAAKL FTAKGAKVILSSQTPNNPWETGTFVNSPTRFVEYAELAAEVAGVEYVDHWSYVDSIYETL GNATVNSYFPIDHTHTSPAGAEVVAEAFLKAVVCTGTSLKSVLTTTSFEGTCL

• What is the function • Where is the active site? What would you do?

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

1G66 Acetylxylan esterase

1USW Hydrolase

1WAB Acetylhydrolase 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 Is it really impossible?

Protein homology modeling is only possible if %id greater than 30-50% WRONG!!!!!!! Why %id is so bad!!

1200 models sharing 25-95% sequence identity with the submitted sequences (www.expasy.ch/swissmod) Identification of correct fold

• % ID is a poor measure – Many evolutionary related proteins share low sequence homology – A short alignment of 5 amino acids can share 100% id, what does this mean? • Alignment score even worse – Many sequences will score high against every thing (hydrophobic stretches) • P-value or E-value more reliable What are P and E values?

• E-value – Number of expected hits Score 150 in database with score 10 hits with higher score (E=10) higher than match 10000 hits in – Depends on database size database => • P-value P=10/10000 = 0.001 – Probability that a random

hit will have score higher P(Score) than match – Database size independent

Score When Blast fails! 1K7A.A

1WAB._ Sequence profiles

Conserved Non-conserved ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASKISMSEEDLLN TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWHGLLVPFGMDGVPGVSFPG---I -TSMAPAFGVQEFYRTVKQGDEVTVTIT-----NIDQIED-VSHGFVVVNHGVSME---I IE--KMKYLTPEVFYTIKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD--- -TSVAPSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAME---V ASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPEGVEGFKSRINDE---- TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDGMERNTAGVP

Matching any thing Any thing can match but G => large negative score Blast iterations

Protein world

Protein Blast2logo Blast2logo Blast2logo

Last position-specific scoring matrix computed, A R N D C Q E G H I L K M F P S T W Y V 1 V -2 -4 -4 -5 -2 -4 -4 -5 -4 5 2 -4 0 -1 -4 -3 -2 -4 -2 4 2 A 5 0 -3 -3 -3 -2 1 -2 -3 0 -3 -2 -2 -4 0 0 -2 -4 -3 0 3 L -4 -5 -6 -6 -4 -5 -5 -6 -5 5 4 -5 1 -2 -5 -5 -3 -4 0 1 4 A 1 -4 -1 -1 3 -1 2 -4 -3 0 -1 -2 -3 1 -4 0 0 -4 2 2 5 E -2 0 -2 6 -6 0 4 -4 2 -5 -5 -2 -5 -6 -4 -2 0 -6 -4 -5 6 L -1 -2 -4 -4 -4 -2 -1 2 3 3 2 -1 0 -2 -5 -1 -1 -5 -3 1 7 Y -4 -5 -5 -6 -4 -5 -5 -4 0 1 4 -5 -1 3 -5 -5 -4 -3 5 3 8 I -1 -2 -5 -5 -4 -5 -2 -6 -5 4 3 -5 -1 3 -5 -4 -2 -4 -1 3 9 P 3 -4 -4 -3 -4 1 1 -4 -2 -2 -3 -2 -4 -5 6 -1 0 -5 -5 -2 10 E 2 -2 -3 -2 -3 0 1 -1 -3 -4 -3 -1 -1 -4 6 -2 -2 -4 -4 -3 . . . Example.

• What is the function • Where is the active site? Profile-profile scoring matrix 1K7C.A

1WAB._ Example. (SGNH active site) 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 Example. Where is the active site?

Align using sequence profiles

ALN 1K7C.A 1WAB._ RMSD = 5.29522. 14% ID 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 Structural superposition

Blue: 1K7C.A Red: 1WAB._ Where was the active site?

Rhamnogalacturonan acetylesterase (1k7c) 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 Using structure

Sequence & structure profile-profile based alignments – Template • Sequence based profiles • Annotated secondary structure • Predicted secondary structure – Query • Sequence based profile • Predicted secondary structure – Position specific gap penalties derived from secondary structure What are the different methods?

• 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, FUGUE) – 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, Phyre) • Sequence profiles and structure based methods – HHpred, CpHModels • Multiple template methods • Modeler (via HHpred, 3D jury) 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 • You can do reliable fold recognition AND homology modeling when for low sequence homology • Use sequence profiles and local protein structure to align sequences 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 CASP6 results The top 4 homology modeling groups in CASP6

• All winners use consensus predictions – The wisdom of the crowd • Same approach as in earlier CASPs 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 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 3D-Jury

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 1. Use output from a set of servers 2. Superimpose all pairs of structures

3. Similarity score Sij = # of Ca pairs within 3.5Å (if #>40;else Sij=0) 4. 3D-Jury score = SijSij/(N+1) Similar methods developed by A Elofsson (Pcons) and D Fischer (3D shotgun) 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/

Meta Server

Evaluating the crowd. Meta Server

Evaluating the crowd. 3D Jury 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, or E-values • Use sequence profiles and local protein structure to align sequences • Do not trust one single prediction method, use consensus methods (like 3D Jury)