IT og Sundhed 2010/11

Transcription

1 IT og Sundhed 2010/11 Sequence based predictors. Secondary structure and surface accessibility Bent Petersen 13 January

2 NetSurfP Real Value Solvent Accessibility predictions with amino acid associated reliability 2

3 Objective Predict residues as being either buried or exposed (25 % threshold) - Two states/classes, Buried/Exposed Predict the Relative Solvent Accessibility, RSA - Real Value 3

4 What is ASA? Accessible Solvent Area, Å 2 Surface area accessible to a rolling water molecule 4

5 RSA RSA = ACC protein ASA tripeptid RSA = Relative Solvent Accessibility ACC = Accessible area in protein structure ASA = Accessible Surface Area in Gly-X-Gly or Ala-X-Ala Classification Networks Real value Networks Classification: Buried = RSA < 25 %, Exposed = RSA > 25 % Real Value: values 0-1, RSA > 1 set to 1 5

6 Why predict RSA? Residues exposed on surface can be: - Involved in PTM s - Potential epitopes - Involved in Protein-Protein interactions - Prediction of Disease-SNP s 6

7 How to start? What do we want? - We want to be able to predict the exposure of an AA What do we need? - A training dataset and an independent evaluation dataset What information do we need? - True structural information the Neural Network can train on Where do we get that? - PDB, DSSP 7

8 Protein Data Bank, PDB Berman, H.M., et al., The Protein Data Bank. Nucl. Acids Res., (1): p

9 Define Secondary Structure of Proteins, DSSP ==== Secondary Structure Definition by the program DSSP, updated CMBI version by ElmK / April 1,2000 ==== DATE=23-MAR REFERENCE W. KABSCH AND C.SANDER, BIOPOLYMERS 22 (1983) HEADER TOXIN 12-AUG-98 3BTA. COMPND 2 MOLECULE: PROTEIN (BOTULINUM NEUROTOXIN TYPE A);. SOURCE 2 ORGANISM_SCIENTIFIC: CLOSTRIDIUM BOTULINUM;. AUTHOR R.C.STEVENS,D.B.LACY TOTAL NUMBER OF RESIDUES, NUMBER OF CHAINS, NUMBER OF SS-BRIDGES(TOTAL,INTRACHAIN,INTERCHAIN) ACCESSIBLE SURFACE OF PROTEIN (ANGSTROM**2) TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(J), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS IN PARALLEL BRIDGES, SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS IN ANTIPARALLEL BRIDGES, SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I-5), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I-4), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+2), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+3), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+4), SAME NUMBER PER 100 RESIDUES TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+5), SAME NUMBER PER 100 RESIDUES *** HISTOGRAMS OF *** RESIDUES PER ALPHA HELIX PARALLEL BRIDGES PER LADDER ANTIPARALLEL BRIDGES PER LADDER LADDERS PER SHEET. # RESIDUE AA STRUCTURE BP1 BP2 ACC N-H-->O O-->H-N N-H-->O O-->H-N TCO KAPPA ALPHA PHI PSI X-CA Y-CA Z-CA 1 1 A P , 0.0 2,-3.8 0, 0.0 3, A F , ,-0.1 1, , A V , , ,-0.1-1, A N S S ,-0.3 2,-0.5-3, , A K S S ,-0.1 2,-0.5 1,-0.0-1, A Q ,-0.5 2,-0.1 1, , A F ,-0.5 2, ,-0.1 3, A N > ,-0.1 3,-0.9 1, , A Y T 3 S ,-0.3-1,-0.1 1, , A K T 3 S ,-0.1-1, ,-0.1 3, A D S < S ,-0.9 3,-0.1 1,-0.1 2, A P , 0.0-1,-0.1 0, 0.0-2, A V ,-0.1 6,-0.2 1,-0.1 4, A N ,-3.7 2,-1.4 2,-0.2 5, A G S S ,-0.4 2,-0.3 3,-0.2 4, A V S S ,-1.4-2,-0.2 2, , A D S S , , ,-0.1 2, A I E S+A 35 0A 6 17,-0.3-4, ,-0.0-2, Kabsch, W. and C. Sander, Dictionary of Protein Secondary Structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, (12): p

10 Define Secondary Structure of Proteins, DSSP DSSP defines 8 types of secondary structure - G = 3-turn helix (3-10 helix) - H = 4-turn helix (α-helix) - I = 5-turn helix (π-helix) - T = Hydrogen bonded turn (3, 4 or 5 turn) - E = Extended strand - B = Residue in isolated β-bridge - S = Bend - Rest is C = coil 10

11 Required datasets Training/test - Used for optimization of settings using 10-fold crossvalidation Evaluation - Used for final evaluation, less than 25 % homolog to the training/test dataset. 11

12 10-fold Cross Validation 10-fold Cross Validation - Break dataset into 10 sets of size 1/10 - Train on 9 datasets and test on 1 - Repeat 10 times and take a mean accuracy 12

13 Learning / Training dataset Training set: Cull_1764: - Max. Seq. ID: 25 % - Resolution: 2.0 Å - R-Factor: Seq. Length AA - Including X-ray entries only 13

14 PISCES 14

15 Learning / Training dataset Homology reduced towards evaluation set CB513 (302 sequences removed) Final Training set: sequences amino acids Buried: % ( amino acids) Exposed: % ( amino acids) 15

16 Learning / Training dataset ---Sequence/residue statistics--- Number of seq.: 1764 Longest seq.: 1T3T.A (1283) Shortest seq.: 1YTV.M(6) Number of amino acids: Assignment category statistics --- B ( 44.20%) A ( 55.80%) ---Amino acid statistics--- H ( 2.40%) G ( 7.59%) Y ( 3.57%) V ( 7.22%) E ( 6.64%) S ( 5.84%) P ( 4.69%) A ( 8.53%) R ( 5.13%) Q ( 3.72%) C 5202 ( 1.24%) K ( 5.52%) L ( 9.21%) N ( 4.25%) T ( 5.50%) F ( 4.11%) D ( 5.92%) I ( 5.63%) W 6365 ( 1.52%) M 7353 ( 1.76%) 16

17 Evaluation dataset Final Evaluation dataset: CB513: non-homologous sequences - Seq. Length aa amino acids - Buried: % ( amino acids) - Exposed: % ( amino acids) 17

18 Evaluation dataset ---Sequence/residue statistics--- Number of seq.: 513 Longest seq.: 6acn.all(754) Shortest seq.: 1atpi-1(20) Number of amino acids: Assignment category statistics --- B ( 44.19%) A ( 55.81%) ---Amino acid statistics--- R 3812 ( 4.53%) T 5015 ( 5.96%) D 4973 ( 5.91%) C 1381 ( 1.64%) Y 3065 ( 3.64%) G 6657 ( 7.91%) N 3976 ( 4.73%) V 5795 ( 6.89%) I 4642 ( 5.52%) A 7267 ( 8.64%) S 5222 ( 6.21%) K 4976 ( 5.92%) P 3903 ( 4.64%) E 5050 ( 6.00%) L 7134 ( 8.48%) Q 3108 ( 3.69%) M 1710 ( 2.03%) H 1865 ( 2.22%) W 1236 ( 1.47%) F 3268 ( 3.88%) X 19 ( 0.02%) B 31 ( 0.04%) Z 14 ( 0.02%) 18

19 Aminoacid Distribution % A C D E F G H I K L M N P Q R S T V W Y Cull/Learning CB513 Cull/Learning CB Amino acids 19

20 Neural Network - Input Position Specific Scoring Matrices, PSSM A R N D C Q E G H I L K M F P S T W Y V B H 2BEM.A A G 2BEM.A A Y 2BEM.A A V 2BEM.A B E 2BEM.A time iterativ psi-blast against nr70 Secondary Structure predictions B H 2BEM.A A G 2BEM.A A Y 2BEM.A A V 2BEM.A B E 2BEM.A (sec predictor by Pernille Andersen) 20

21 Secondary structure predictor Developed by Pernille Andersen, incorporated in NetSurfP Trained on 2,085 sequences using DSSP - H = H, E = E, C =., G, I, B, S and T - H 30 %, E 20 %, C 50 % Performance of ~80 % Maximum theoretical limit is ~88 % 21

22 Neural Network - Settings Window Size: Hidden units: 10, 20, 25, 30, 40, 50, 75, 150, (200) Learning rate: 0.01 / (0.005) Epocs (training rounds): fold cross-validation - 9/10 used for training, 1/10 for testing 22

23 Neural network window Sliding window of BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Serine, buried 23

24 Neural network window Sliding window of BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Proline, exposed 24

25 Neural network window Sliding window of BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Alanine, exposed 25

26 Method 26

27 Wisdom of the crowd Selecting best performing network architectures based on test performance Better than choosing any single network 10-fold % correct predictions Average of set A-J w. sec. structure % correct Average of top 1 Average of top 2 Average of top 3 Average of top 4 Average of top 5 Average of top 6 Average of top 7 Average of top 8 Average of top 9 Average of top 10 Series Average of top 11 Series1 Average of top 12 Average of top 13 Average of top 14 Average of top 15 Average of top 16 Average of top 17 Average of top 18 Average of top 19 Average of top 20 27

28 Results - Classification networks Training: % Correct MCC #Networks Best Single Architecture All Architectures Top 20 Architectures

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30 Results - Classification networks Training: % Correct MCC #Networks Best Single Architecture All Architectures Top 20 Architectures Evaluation: % Correct MCC Dor and Zhou 78.8 Not Published NetsurfP CB500/CB

31 Results Evaluation 31

32 NetSurfP /usr/cbs/bio/src/netsurfp/netsurfp -h 32

33 NetSurfP 33

34 NetDiseaseSNP Disease-SNP prediction (Morten Bo Johansen) Without NetSurfP: Cross-validation: MCC= Cross-Evaluation: MCC= With NetSurfP: Cross-validation: MCC= Cross-Evaluation: MCC=

35 Paper is out..what then? 35

36 Statistics Submissions to the webserver from CBS website 36

37 Paper is out..what then? 37

38 Paper is out..what then? 38

39 Paper is out..what then? 39

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43 As of 12 Jan sequences submitted from unique IP s 43

44 First citation 24 october 2009 :-) 44

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