Prediction of protein

Transcription

1 Prediction of protein contact t maps Piero Fariselli Department of Biology University of Bologna

2 From Sequence to Function Functional Genomics and Proteomics >BGAL_SULSO BETA-GALACTOSIDASE Sulfolobus solfataricus. MYSFPNSFRFGWSQAGFQSEMGTPGSEDPNTDWYKWVHDPENMAAGLVSG DLPENGPGYWGNYKTFHDNAQKMGLKIARLNVEWSRIFPNPLPRPQNFDE SKQDVTEVEINENELKRLDEYANKDALNHYREIFKDLKSRGLYFILNMYH WPLPLWLHDPIRVRRGDFTGPSGWLSTRTVYEFARFSAYIAWKFDDLVDE YSTMNEPNVVGGLGYVGVKSGFPPGYLSFELSRRHMYNIIQAHARAYDGI KSVSKKPVGIIYANSSFQPLTDKDMEAVEMAENDNRWWFFDAIIRGEITR GNEKIVRDDLKGRLDWIGVNYYTRTVVKRTEKGYVSLGGYGHGCERNSVS LAGLPTSDFGWEFFPEGLYDVLTKYWNRYHLYMYVTENGIADDADYQRPY YLVSHVYQVHRAINSGADVRGYLHWSLADNYEWASGFSMRFGLLKVDYNT KRLYWRPSALVYREIATNGAITDEIEHLNSVPPVKPLRH Genomic Protein sequences Protein structures sequences Protein functions

3 The Protein Folding T T C C P S I V A R S N F N V C R L P G T P E A L C A T Y T G C I I I P G A T C P G D Y A N

4 (Rost B.)

5 The Data Bases of Sequences and Structures EMBL: 195,241,608 sequences 292,078,866,691 nucleotides >BGAL_SULSO BETA-GALACTOSIDASE Sulfolobus solfataricus. MYSFPNSFRFGWSQAGFQSEMGTPGSEDPNTDWYKWVHDPENMAAGLVSG DLPENGPGYWGNYKTFHDNAQKMGLKIARLNVEWSRIFPNPLPRPQNFDE SKQDVTEVEINENELKRLDEYANKDALNHYREIFKDLKSRGLYFILNMYH WPLPLWLHDPIRVRRGDFTGPSGWLSTRTVYEFARFSAYIAWKFDDLVDE YSTMNEPNVVGGLGYVGVKSGFPPGYLSFELSRRHMYNIIQAHARAYDGI KSVSKKPVGIIYANSSFQPLTDKDMEAVEMAENDNRWWFFDAIIRGEITR GNEKIVRDDLKGRLDWIGVNYYTRTVVKRTEKGYVSLGGYGHGCERNSVS LAGLPTSDFGWEFFPEGLYDVLTKYWNRYHLYMYVTENGIADDADYQRPY YLVSHVYQVHRAINSGADVRGYLHWSLADNYEWASGFSMRFGLLKVDYNT KRLYWRPSALVYREIATNGAITDEIEHLNSVPPVKPLRH UNIPROT: sequences 154'416'236 residues PDB: D structures membrane proteins 1% November/2009

6 What is a multiple alignment? The short answer is this - VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDFYPGA--VTVAWKADS--VTVAWKADS AALGCLVKDYFPEP--VTVSWNSG--- VSLTCLVKGFYPSD--IAVEWESNG--

7 MSA quence profile Se 1 Y K D Y H S - D K K K G E L Y R D Y Q T - D Q K K G D L Y R D Y Q S - D H K K G E L Y R D Y V S - D H K K G E L Y R D Y Q F - D Q K K G S L Y K D Y N T - H Q K K N E S Y R D Y Q T - D H K K A D L G Y G F G - - L I K N T E T T K 9 T K G Y G F G L I K N T E T T K 10 T K G Y G F G L I K N T E T T K sequence position A C D E F G H K I L M N P Q R S T V W Y Evolutionary information Multiple Sequence Alignment (MSA) of similar sequences Sequence profile: for each position a 20- valued vector contains the aminoacidic composition of the aligned sequences.

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10 3D structure prediction of proteins New folds Existing folds Ab initio prediction Threading Building by homology Homology (%)

11 Contact definition Contacts and Contact Maps F 156 V 299 F 297 I 269 V 271 V 238 I 240

12 Protein contact definitions: 1. Based on C 2. Based on C 3. All-atom (without Hydrogens)

13 From the 3D structure to the contact map Given a protein of length L, and a square matrix M of dimension L L For each pair of residue i and j calculate distance between i and j if distance < threshold otherwise put 1 in the cell M(i,j) put 0 in the cell M(i,j)

14 From 3D Structure Computation of Contact Maps Computation of Contact Maps From 3D Structure F 156 V 299 To Contact Map TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN T F 297 V 271 I 269 T T C C P S I V A R I 240 V 238 R S N F N V C R L P G T P E A I C A T Y T G C I I I P G A T C P G D Y A N

15 Protein Structural Classes All- All- + /

16 An Example of a Contact map (All- ) C5A

17 An Example of a Contact map (All- ) SFP C N

18 An Example of Contact map ( ) 60 6PTI N C

19 From the contact map to the 3D structure Two methods have been proposed : 1. Bohr et al., Protein Structure from distance Inequalities J.Mol. Biol. 1993, 231: => based on a steepest descent procedure 2. Vendruscolo and Domany Fold. Des. 1998, 2: => based on a modified Metropolis procedure

20 6pti Reconstruction Efficiency (58 residues) Vendruscolo and Domany Fold. Des RMSD M (Number of random flipping) At M= 200 No of eliminated true contacts 6 % real contacts No of added false contacts 52 % real contacts

21 From the contact map to the 3D structure: the reconstruction efficiency

22 3-D Modelling through Contact Maps example: Bacteriorhodopsin 1QHJ (1.9 Å) N Model Contact map C RMSD = 2.5 Å

23 MARC efficiency in 3D reconstruction from the protein contact map after progressive elimination of true contacts (6pti) RM SD % missing contacts

24 MARC efficiency in 3D reconstruction after progressive addition of wrong contacts to a protein contact map with 30 % of true contacts (6pti) RMS SD % wrong contacts

25 Prediction of Contact Maps

26 Prediction of Contact Maps Several methods have been applied: Bohr et al., FEBS :43-46 => based on neural networks Göbel et al., PROTEINS : => based on correlated mutations in proteins Thomas et al., Prot. Eng : => based on a statistical method and evolution information Olmea and Valencia Fold. Des :S25-S32 => based on correlated mutations and other information Fariselli and Casadio Prot. Eng :15-21 => based on neural networks and evolutionary information Fariselli et al., CASP4/ and Prot. Eng. in press => Neural networks and other information Pollastri and Baldi al., Bioinformatics S62-S70 S70 => Recurrent Neural networks

27 Relevant points Contact Threshold Sequence separation (or sequence gap) No of contacts vs No of non-contacts

28 The Contact Threshold 16 Å

29 The Contact Threshold 16 Å Å

30 The Contact Threshold 16 Å Å 8 Å

31 The Contact Threshold 16 Å Å 8 Å 6 Å

32 Sequence separation VTISCTGSSSNIGAGNHVKWYQQLPG

33 The Sequence Separation example of a sequence separation = 10 residues

34 0.025 Frequency distribution of the real and hypothetical contacts as a function of sequence separation frequ ency of contac cts Theoretical Experimental sequence separation

35 Relation between the number of contacts and the protein length ts mber of contact Num Protein length

36 Evaluation of the efficiency of contact map predictions 1) Accuracy: A = Ncp * / Ncp where Ncp * and Ncp are the number of correctly assigned contacts and that of total predicted contacts, respectively. 2) Improvement over a random predictor : R = A / (Nc/Np) where Nc/Np is the accuracy of a random predictor ; Nc is the number of real contacts in the protein ti of flength thlp, and Np are all the possible contacts t 3) Difference in the distribution of the inter-residue distances in the 3D structure for predicted pairs compared with all pair distances in the structure (Pazos et al., 1997): Xd= i=1,n (P ic -P ia ) / n d i where n is the number of bins of the distance distribution (15 equally distributed bins from 4 to 60Å cluster all the possible distances of residue pairs observed in the protein structure); d i is the upper limit (normalised to 60 Å) for each bin, e.g. 8 Å for the 4 to 8 Å bin; P ic and P ia are the percentage of predicted contact pairs (with distance between d i and d i-1 ) and that of all possible pairs respectively

37 Tools out of machine learning approaches Neural Networks Training Data Base Subset TTCCPSIVARSNFNVCRLPGTPEAICATYTGCIIIPGATCPGDYAN Prediction New sequence General rules Known mapping Prediction

38 Contact definition used: C - C distance < 0.8 nm Sequence gap > 7 residues

39 The database of proteins used to train and test the contact map ppredictors. L<100 1c5a 1sco 2sn3 1bkf 1npk 3lzt 1juk 1axn 1a1i_A 1cfh 1spy 2sxl 1bkr_A 1pdn_C 3nul 1kid 1b0m 1a1t_A 1ctj 1sro 3gat_A 1br0 1pkp 5p21 1mml 1bg2 1a68 1cyo 1tbn 3mef_A 1bsn 1poa 7rsa 1mrj 1bgp 1a7i 1fna 1tiv 4mt2 1bv1 1put L: nls 1bxo 1acp 1hev 1tle 5pti 1bxa 1ra9 1ad2 1ppn 1dlc 1ah9 1hrz_A 1tsg L: c25 1rcf 1akz 1rgs 1irk 1aho 1kbs 1ubi 1a62 1cew_I 1rie 1amm 1rhs 1iso 1aie 1mbh 1uxd 1a6g 1cfe 1skz 1aol 1thv 1kvu 1ail 1mbj 2acy 1acz 1cyx 1tam 1ap8 1vin 1moq 1ajj 1msi 2adx 1asx 1dun 1vsd 1bf8 1xnb 1svb 1aoo 1mzm 2bop_A 1aud_A 1eca 1whi 1bjk 1yub 1uro_A 1ap0 1nxb 2ech 1ax3 1erv 2fsp 1byq_A 1zin 1ysc 1ark 1ocp 2fdn 1b10 1exg 2gdm 1c3d 2baa 2cae 1awd 1opd 2fn2 1bc4 1hfc 2ilk 1cdi 2fha 2dpg 1awj 1pce 2fow 1bd8 1ifc 2lfb 1cne L>300 2pgd 1awo 1plc 2hfh 1bea 1jvr 2pil 1cnv 16pk 3grs 1bbo 1pou 2hoa 1bfe_A 1kpf 2tgi 1csn 1a8e 1bc8_C 1ppt 2hqi 1bfg 1kte 2ucz 1ezm 1ads 1brf 1rof 2lef_A 1bgf 1mak 3chy 1fts 1arv

40 Neural Network-based predictor 1 output neuron (contact/non-contact) 1 hidden layer with 8 neurons Input layer with 1071 input neurons : Ordered residue pairs (1050 neurons) Secondary structures (18 neurons) Correlated mutations (1 neuron) Sequence conservation (2 neurons)

41 Representation of the input coding based on ordered couples. (A) An alignment of 5 (hypothetical) sequences they are represented in a HSSP file (Sander and (A) An alignment of 5 (hypothetical) sequences they are represented in a HSSP file (Sander and Schneider, 1991). i and j stand for the positions of the two residues making or not making contact (A and D in the leading sequence or sequence 1). (B) Single sequence coding. The position representing the couple (AD) in the vector is set to 1.0 while the other positions are set to 0. (C) Multiple sequence coding. For each sequence in the alignment (1 to 5 in the scheme in A) a couple of residues in position i and j is counted. The final input coding representing the frequency of each couple in the alignment is normalized to the number of the sequences

42 N seque ences M = N (N N-1)/2 couple es Correlated mutations Multiple sequence alignment 1 MVKGPGLYTDIGKKARDLLYKDYHSDKKFTISTYSPTGVAITSS 2 MVKGPGLYSDIGKRARDLLYRDYQSDHKFTLTTYTANGVAITST 3 MVKGPGLYTEIGKKARDLLYRDYQGDQKFSVTTYSSTGVAITTT M-valued vectors: i 1 MVKGPGLYTDIGKKARDLLYKDYHSDKKFTISTYSPTGVAITSS 2 MVKGPGLYSDIGKRARDLLYRDYQSDHKFTLTTYTANGVAITST 1 MVKGPGLYTDIGKKARDLLYKDYHSDKKFTISTYSPTGVAITSS 3 MVKGPGLYTEIGKKARDLLYRDYQGDQKFSVTTYSSTGVAITTT 2 MVKGPGLYSDIGKRARDLLYRDYQSDHKFTLTTYTANGVAITST 3 MVKGPGLYTEIGKKARDLLYRDYQGDQKFSVTTYSSTGVAITTT V i S : McLachlan substitution matrix S(T;S) S(T;T) S(S;T) j S(I;L) S(I;V) S(L;V) V j Correlation: C ij 1 M M V (k) V V (k) V i σ V σ V k 1 i j i j j

43 The neural network architecture for prediction of contact maps

44 Accuracy of contact map prediction using a crosslidtddt data set t(170 proteins) ti validated No of proteins Accuracy

45 T0087: 310 residues (A = 0.20 FR/NF ) C N

46 T0106: 123 residues (A=0.06 FR / NF ) C N

47 T0128: 222 residues (A = 0.24 CM ) N C

48 T0110: 128 residues (A = 0.30 FR ) N C

49 T0125: 141 residues (A = 0.03 CM ) N C

50 T0124: 242 residues (A = 0.01 NF) C N

51 Sequenc ce posit tion TARGET: T0115 (300 residues) (A = 0.17 FR/NF) PDB code: 1FWK (Homoserine kinase, Methanococcus jannaschii) C Sequence position N

52 Predictive performance on 29 targets Predicted Fr(H) Predicted Fr(E) Observed Fr(H) Observed Fr(E) Lp Nal Xd A Class Target Q3 (SS) or T FR/NF T CM/FR/NF T FR/NF T CM/FR T FR T FR T FR/NF T FR T FR T FR T FR T CM T CM/FR T FR/NF T FR/NF T CM/FR T CM T CM T FR T FR T CM all- T FR all- T FR/NF T FR/NF T NF T FR T FR/NF T NF T CM Q3=secondary structure prediction accuarcy; Fr(H) and Fr(E)= frequency of predicted and observed alfa and beta structures in the chain; Lp=protein length in residues; Nal= number of sequences in the alignment; Xd and A are as defined in equations 2 and 1, respectively; Class is the classification of targets by predictio difficulty: CM=comparative modeling, FR=fold recognition, NF=new fold.

53 COMMENTS The predictor is trained mainly on globular mixed proteins Contacts among beta structures dominate Contacts in all-alpha proteins are more difficult to predict A filtering i algorithm is needed