1. Protein Fold recognition Morten Nielsen, Thomas Nordahl CBS, BioCentrum, DTU

2. Introduction What is a protein fold Protein fold Protein sequence id Protein sequence/structure databases Alignment values Scores, E-values & P-values Protein classifications Fold, Superfamily, Family & protein

3. Introduction What is a protein fold A protein fold is the scaffold that can be used as a template to model a query protein sequence. Fold recognition is technique that is used to identify the scaffold to be used, from a known protein structure. The sequence similarity is low and therefore the fold is difficult to recognize by use of simple sequence alignment tools (blosum62 matrix).

4. Outline Many textbooks and experts state that %ID is the only determining factor for successful homology modeling This is WRONG! %ID is a very poor measure to determine if a protein can be modeled Many sequences with sequence homology ~10- 15% can be accurately modeled

5. 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

6. 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

7. Human genome ~ 30.000 proteins Homology modeling and the human genome

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

9. PDB New Fold Growth New folds Old folds New PDB structures

10. PDB New Fold Growth New PDB structures

11. PDB New Fold Growth New PDB structures

12. Identification of fold Rajesh Nair & Burkhard Rost Protein Science, 2002, 11, 2836-47

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

14. 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

15. 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 Score P(Score) Score 150 10 hits with higher score (E=10) 10000 hits in database => P=10/10000 = 0.001

16. Protein classifications

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

18. Superfamilies Proteins which are (remote) evolutionarily related –Sequence similarity low –Share function –Share special structural features –Same evolutionary ancestor Relationships between members of a superfamily may not be readily recognizable from the sequence alone Fold Family Superfamily Proteins

19. 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)

20. 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 can mutate almost for free, and the score for mismatch is lower than the BLOSUM score Sequence profiles (just like a HMM) can capture these differences

21. TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI Sequence profiles/blosum62 scores a)TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP b)TKAVVLTFNTSVEICLVMQ-GTSIVAAESHPLHLHGFNFPSNFNLVDPMERNTAGVP Which alignment is most correct a) or b) ? Blosum62 scores: G-G: 6 H-H: 8 TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

22. Blosum scoring matrix A R N D C Q E G H I L K M F P S T W Y V A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

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

24. 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!)

25. Example. Sequence profiles Alignment of protein sequences 1PLC._ and 1GYC.A –E-value > 1000 Profile alignment –Align 1PLC._ against Swiss-prot –Make position specific weight matrix from alignment –Use this matrix to align 1PLC._ against 1GYC.A E-value < 10 -22. Rmsd=3.3

26. Sequence profiles Score = 97.1 bits (241), Expect = 9e-22 Identities = 13/107 (12%), Positives = 27/107 (25%), Gaps = 17/107 (15%) 1PLC._: 3 ADDGSLAFVPSEFSISPGEKI------VFKNNAGFPHNIVFDEDSIPSGVDASKIS 56 F + G++ N+ + +G + + 1GYC.A: 26 ------VFPSPLITGKKGDRFQLNVVDTLTNHTMLKSTSIHWHGFFQAGTNWADGP 79 1PLC._: 57 MSEEDLLNAKGETFEVAL---SNKGEYSFYCSP--HQGAGMVGKVTV 98 A G +F G + ++ G+ G V 1GYC.A: 80 AFVNQCPIASGHSFLYDFHVPDQAGTFWYHSHLSTQYCDGLRGPFVV 126 Rmsd=3.3 Å Structure red Template blue

27. Sequence logo / Sequence profile 0 iterations (Blosum62) 2 iterations 1 iterations 3 iterations

28. Profile-profile alignment Query Template Compare amino acid preference for the two proteins and pair similar positions (HHpred)

29. Including structure Sequence within 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

30. Using structure Sequence&structure profile-profile based alignments –Template profiles Multiple structure alignments Sequence based profiles –Query profile Sequence based profile Predicted secondary structure –Position specific gap penalties derived from secondary structure

31. 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

32. CASP6 results

33. 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!

34. The wisdom of the crowd! –Why the many are smarter than the few –A general method useful to improve prediction accuracy –No single method or expert will always be the best

35. 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 same fold among the top hits, this fold is probably correct

36. 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/

37. Meta Server

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. Modeling of new protein folds Only when everything else fails Challenge Close to impossible to model Natures folding potential Ab initio protein modeling

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

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

42. 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 everythings fail, use ab initio methods