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

2. 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)

3. Protein Homology modeling? 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

4. How to do it? 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 –And maybe active sites Align protein sequence to template –Simple alignment methods –Sequence profiles –Threading methods –Pseudo force fields Model side chains and loops

5. Homology modeling and the human genome

6. 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

7. Example. >1K7C.A TTVYLAGDSTMAKNGGGSGTNGWGEYLASYLSATVVNDAVAGRSARSYTREGRFENIADV VTAGDYVIVEFGHNDGGSLSTDNGRTDCSGTGAEVCYSVYDGVNETILTFPAYLENAAKL FTAKGAKVILSSQTPNNPWETGTFVNSPTRFVEYAELAAEVAGVEYVDHWSYVDSIYETL GNATVNSYFPIDHTHTSPAGAEVVAEAFLKAVVCTGTSLKSVLTTTSFEGTCL What is the function Where is the active site? A post doc in our group did her PhD obtaining the structure of the sequence below

8. 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?

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

10. 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

11. Is it really impossible? Protein homology modeling is only possible if %id greater than 30-50% WRONG!!!!!!!

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

13. 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

14. 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

15. What goes wrong when Blast fails? Conventional sequence alignment uses a (Blosum) scoring matrix to identify amino acids matches in the two protein sequences

16. 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

17. What goes wrong when Blast fails? Conventional sequence alignment uses a (Blosum) scoring matrix to identify amino acids matches in the two protein sequences This scoring matrix is identical at all positions in the protein sequence! EVVFIGDSLVQLMHQC X X AGDS.GGGDSAGDS.GGGDS

18. Alignment accuracy. Scoring functions Blosum62 score matrix. Fg=1. Ng=0? Score =2+6+6+4-1=17 Alignment LAGDSD F0-2-3 -2-3 I21-4-3-2-3 G-4060 D-4-2606 S-210040 L4-4 -2-4 LAGDS I-GDS

19. When Blast works! 1PLC._ 1PLB._

20. When Blast fails! 1PLC._ 1PMY._

21. When Blast fails, use sequence profiles!

22. 1PLC._ 1PMY._

23. Sequence profiles 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 should be lower than the BLOSUM score Sequence profiles can capture these differences

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

25. All  : Hemoglobin (1bab)

26. All  : Immunoglobulin (8fab)

27.  Triose phosphate isomerase (1hti)

28.  : Lysozyme (1jsf)

29. 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 TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

30. How to make 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!)

31. Example. (SGNH active site)

32. 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

33. Profile-profile scoring matrix 1K7C.A 1WAB._

34. 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

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

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

37. How good are we?

38. Alignment accuracy. Scoring functions How to make the most of sequence profiles? A R N D C Q E G H I L K M F P S T W Y V T 1K7C.A 1 0.06 0.03 0.04 0.03 0.01 0.02 0.03 0.04 0.01 0.04 0.05 0.04 0.02 0.02 0.02 0.08 0.37 0.00 0.01 0.06 T 1K7C.A 2 0.04 0.21 0.02 0.02 0.01 0.02 0.03 0.02 0.01 0.02 0.03 0.04 0.01 0.01 0.01 0.07 0.36 0.00 0.01 0.03 V 1K7C.A 3 0.03 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.36 0.20 0.01 0.02 0.02 0.01 0.01 0.02 0.00 0.01 0.22 ….. G 1K7C.A 7 0.04 0.01 0.02 0.02 0.01 0.01 0.01 0.71 0.01 0.01 0.02 0.02 0.01 0.01 0.01 0.05 0.02 0.00 0.01 0.01 D 1K7C.A 8 0.02 0.02 0.05 0.67 0.00 0.02 0.05 0.02 0.01 0.01 0.01 0.02 0.00 0.01 0.01 0.03 0.02 0.00 0.01 0.01 S 1K7C.A 9 0.06 0.02 0.03 0.03 0.01 0.02 0.03 0.03 0.01 0.02 0.02 0.03 0.01 0.01 0.02 0.59 0.04 0.00 0.01 0.02 E 1WAB._ 40 0.04 0.21 0.06 0.12 0.00 0.06 0.06 0.02 0.02 0.01 0.02 0.19 0.01 0.01 0.04 0.04 0.04 0.00 0.01 0.03 V 1WAB._ 41 0.03 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.00 0.31 0.14 0.01 0.01 0.05 0.01 0.03 0.04 0.01 0.02 0.29 …. G 1WAB._ 45 0.03 0.02 0.01 0.01 0.00 0.01 0.01 0.81 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.02 0.02 0.00 0.00 0.01 D 1WAB._ 46 0.02 0.01 0.06 0.68 0.00 0.01 0.03 0.02 0.02 0.01 0.01 0.02 0.00 0.01 0.01 0.05 0.03 0.00 0.00 0.01 S 1WAB._ 47 0.04 0.02 0.02 0.02 0.01 0.01 0.03 0.02 0.01 0.01 0.01 0.03 0.01 0.01 0.01 0.68 0.03 0.00 0.01 0.01

39. Alignment accuracy

40. AUC performance measure Query Templ Score Hit/nonhit 1CJ0.A 1B78.A 0.170963 0 1CJ0.A 1B8A.A -0.040029 0 1CJ0.A 1B8B.A -0.012789 0 1CJ0.A 1B8G.A 12.342823 1 1CJ0.A 1B9H.A 13.394361 1 1CJ0.A 1BAR.A -1.281068 0 1CJ0.A 1BAV.C -1.091305 0 Query Templ Score Hit/nonhit 1CJ0.A 1B8G.A 12.342823 1 1CJ0.A 1DTY.A 11.867786 1 1CJ0.A 1DGD._ 11.271914 1 1CJ0.A 1GTX.A 11.010288 1 1CJ0.A 2GSA.A 10.958170 1 1CJ0.A 1BW9.A 2.651775 0 1CJ0.A 1AUP._ 2.507336 1 1CJ0.A 1GTM.A 2.444512 0 AUC (area under the ROC curve)

41. Fold recognition performance

42. Outlook Include position dependent gap penalties The conventional alignment methods use equal gap penalties through out the scoring matrix In real proteins placement of insertions and deletions is highly structure dependent No gaps in secondary structure elements Gaps most frequent in loops Distance dependency

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 You can do reliable fold recognition AND homology modeling when for low sequence homology Use sequence profiles and local protein structure to align sequences

44. What are (some of) the different available 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