IT og Sundhed 2010/11

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Transcription:

IT og Sundhed 2010/11 Sequence based predictors. Secondary structure and surface accessibility Bent Petersen 13 January 2011 1

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

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

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

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

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

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

Protein Data Bank, PDB Berman, H.M., et al., The Protein Data Bank. Nucl. Acids Res., 2000. 28(1): p. 235-242. 8

Define Secondary Structure of Proteins, DSSP ==== Secondary Structure Definition by the program DSSP, updated CMBI version by ElmK / April 1,2000 ==== DATE=23-MAR-2009. REFERENCE W. KABSCH AND C.SANDER, BIOPOLYMERS 22 (1983) 2577-2637. 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. 1277 2 2 1 1 TOTAL NUMBER OF RESIDUES, NUMBER OF CHAINS, NUMBER OF SS-BRIDGES(TOTAL,INTRACHAIN,INTERCHAIN). 55121.0 ACCESSIBLE SURFACE OF PROTEIN (ANGSTROM**2). 815 63.8 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(J), SAME NUMBER PER 100 RESIDUES. 24 1.9 TOTAL NUMBER OF HYDROGEN BONDS IN PARALLEL BRIDGES, SAME NUMBER PER 100 RESIDUES. 198 15.5 TOTAL NUMBER OF HYDROGEN BONDS IN ANTIPARALLEL BRIDGES, SAME NUMBER PER 100 RESIDUES. 1 0.1 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I-5), SAME NUMBER PER 100 RESIDUES. 10 0.8 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I-4), SAME NUMBER PER 100 RESIDUES. 125 9.8 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+2), SAME NUMBER PER 100 RESIDUES. 134 10.5 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+3), SAME NUMBER PER 100 RESIDUES. 276 21.6 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+4), SAME NUMBER PER 100 RESIDUES. 9 0.7 TOTAL NUMBER OF HYDROGEN BONDS OF TYPE O(I)-->H-N(I+5), SAME NUMBER PER 100 RESIDUES. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 *** HISTOGRAMS OF ***. 0 0 0 0 0 3 3 1 2 1 0 3 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 2 RESIDUES PER ALPHA HELIX. 2 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PARALLEL BRIDGES PER LADDER. 15 10 7 5 8 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ANTIPARALLEL BRIDGES PER LADDER. 3 3 0 0 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 5 0, 0.0 2,-3.8 0, 0.0 3,-0.2 0.000 360.0 360.0 360.0 132.0 74.7 55.7 73.4 2 2 A F - 0 0 115 92,-0.4 93,-0.1 1,-0.1 36,-0.1-0.206 360.0-142.1 55.7-62.1 74.7 59.2 74.7 3 3 A V - 0 0 11-2,-3.8 35,-0.2 91,-0.1-1,-0.1 0.867 4.9-143.8 70.2 103.3 78.3 59.8 73.7 4 4 A N S S+ 0 0 127 33,-0.3 2,-0.5-3,-0.2 33,-0.1 0.914 73.7 44.0-67.5-53.8 80.1 61.9 76.4 5 5 A K S S- 0 0 94 32,-0.1 2,-0.5 1,-0.0-1,-0.1-0.857 79.6-124.0-105.1 133.1 82.5 64.2 74.5 6 6 A Q - 0 0 192-2,-0.5 2,-0.1 1,-0.1 82,-0.1-0.568 35.9-150.4-71.8 118.5 81.6 66.2 71.4 7 7 A F - 0 0 14-2,-0.5 2,-0.3 80,-0.1 3,-0.1-0.388 16.9-164.3-91.4 166.8 84.2 65.3 68.7 8 8 A N > - 0 0 71-2,-0.1 3,-0.9 1,-0.1 77,-0.0-0.977 28.9-124.4-143.4 141.5 85.7 67.1 65.7 9 9 A Y T 3 S+ 0 0 17-2,-0.3-1,-0.1 1,-0.2 72,-0.1 0.908 109.3 50.7-57.8-43.3 87.5 65.3 62.9 10 10 A K T 3 S+ 0 0 141-3,-0.1-1,-0.2 70,-0.1 3,-0.1 0.650 77.9 122.5-70.3-17.2 90.7 67.4 63.3 11 11 A D S < S- 0 0 45-3,-0.9 3,-0.1 1,-0.1 2,-0.1-0.203 77.6-91.4-48.0 134.3 91.0 66.8 67.1 12 12 A P - 0 0 99 0, 0.0-1,-0.1 0, 0.0-2,-0.1-0.246 38.0-108.3-57.6 128.3 94.4 65.3 67.8 13 13 A V + 0 0 41-3,-0.1 6,-0.2 1,-0.1 4,-0.1-0.238 38.6 179.2-51.8 138.5 94.8 61.5 67.8 14 14 A N - 0 0 67 4,-3.7 2,-1.4 2,-0.2 5,-0.2-0.085 45.1-107.4-144.3 45.7 95.4 60.3 71.4 15 15 A G S S+ 0 0 0 122,-0.4 2,-0.3 3,-0.2 4,-0.2 0.248 100.3 58.5 54.3-18.1 95.7 56.6 71.7 16 16 A V S S- 0 0 72-2,-1.4-2,-0.2 2,-0.5 20,-0.1-0.996 116.3-7.4-142.5 145.9 92.2 56.3 73.3 17 17 A D S S+ 0 0 22-2,-0.3 19,-2.5 18,-0.1 2,-0.2 0.389 136.6 45.3 53.3-7.2 88.7 57.3 72.3 18 18 A I E S+A 35 0A 6 17,-0.3-4,-3.7-11,-0.0-2,-0.5-0.649 85.9 128.7-161.1 96.3 90.4 59.0 69.2 Kabsch, W. and C. Sander, Dictionary of Protein Secondary Structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983. 22 (12): p. 2577--2637. 9

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

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

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

Learning / Training dataset Training set: Cull_1764: - Max. Seq. ID: 25 % - Resolution: 2.0 Å - R-Factor: 0.2 - Seq. Length 30-3000 AA - Including X-ray entries only 13

PISCES 14

Learning / Training dataset Homology reduced towards evaluation set CB513 (302 sequences removed) Final Training set: - 1764 sequences - 417.978 amino acids Buried: 55.80 % (233.221 amino acids) Exposed: 44.20 % (184.757 amino acids) 15

Learning / Training dataset ---Sequence/residue statistics--- Number of seq.: 1764 Longest seq.: 1T3T.A (1283) Shortest seq.: 1YTV.M(6) Number of amino acids: 417978 ---Assignment category statistics --- B 184757 ( 44.20%) A 233221 ( 55.80%) ---Amino acid statistics--- H 10025 ( 2.40%) G 31743 ( 7.59%) Y 14927 ( 3.57%) V 30171 ( 7.22%) E 27774 ( 6.64%) S 24430 ( 5.84%) P 19589 ( 4.69%) A 35658 ( 8.53%) R 21435 ( 5.13%) Q 15535 ( 3.72%) C 5202 ( 1.24%) K 23054 ( 5.52%) L 38489 ( 9.21%) N 17756 ( 4.25%) T 22998 ( 5.50%) F 17181 ( 4.11%) D 24743 ( 5.92%) I 23550 ( 5.63%) W 6365 ( 1.52%) M 7353 ( 1.76%) 16

Evaluation dataset Final Evaluation dataset: CB513: - 513 non-homologous sequences - Seq. Length 20-754 aa - 84.119 amino acids - Buried: 55.81 % (46.948 amino acids) - Exposed: 44.19 % (37.171 amino acids) 17

Evaluation dataset ---Sequence/residue statistics--- Number of seq.: 513 Longest seq.: 6acn.all(754) Shortest seq.: 1atpi-1(20) Number of amino acids: 84119 ---Assignment category statistics --- B 37171 ( 44.19%) A 46948 ( 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

Aminoacid Distribution % 10 8 6 4 2 0 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 8.53 1.24 5.92 6.64 4.11 7.59 2.40 5.63 5.52 9.21 1.76 4.25 4.69 3.72 5.13 5.84 5.50 7.22 1.52 3.57 CB513 8.64 1.64 5.91 6.00 3.88 7.91 2.22 5.52 5.92 8.48 2.03 4.73 4.64 3.69 4.53 6.21 5.96 6.89 1.47 3.64 Amino acids 19

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 1-4 -3-2 -4-6 -2-3 -5 11-6 -5-3 -4-4 -5-3 -4-5 -1-6 A G 2BEM.A 2-2 -5-3 -4-5 -4-5 7-5 -7-6 -4-5 -6-5 -3-4 -5-6 -6 A Y 2BEM.A 3-1 1-4 -3-5 -4-4 -4 1-4 -1-4 -1 2-5 0-1 4 7-2 A V 2BEM.A 4-1 -5-5 -6-4 -4-5 -5-5 4 1-5 6-3 -2-2 0-5 -4 4 B E 2BEM.A 5-2 -4-3 0-4 -1 3-2 -4 0-3 -2 1-2 -3 3 3-5 -4 0 4 time iterativ psi-blast against nr70 Secondary Structure predictions B H 2BEM.A 1 0.003 0.003 0.966 A G 2BEM.A 2 0.018 0.086 0.868 A Y 2BEM.A 3 0.020 0.199 0.752 A V 2BEM.A 4 0.021 0.271 0.679 B E 2BEM.A 5 0.020 0.199 0.752 (sec predictor by Pernille Andersen) 20

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

Neural Network - Settings Window Size: 11-19 Hidden units: 10, 20, 25, 30, 40, 50, 75, 150, (200) Learning rate: 0.01 / (0.005) Epocs (training rounds): 200 10-fold cross-validation - 9/10 used for training, 1/10 for testing 22

Neural network window Sliding window of 7 170 2BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Serine, buried 23

Neural network window Sliding window of 7 170 2BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Proline, exposed 24

Neural network window Sliding window of 7 170 2BEM.A mol:aa CHITIN-BINDING PROTEIN HGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFT WKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAF YQAIDVNLSK BAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAA ABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAA AAAAAABABB Prediction on middle residue Alanine, exposed 25

Method 26

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 79.80 79.75 79.72 79.75 79.75 79.75 79.74 79.75 79.76 79.77 79.77 79.76 79.75 79.76 79.75 79.75 79.76 79.77 79.77 79.70 79.69 79.66 79.65 % correct 79.60 79.55 79.55 79.50 79.45 79.40 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 Series1 79.55 79.66 79.69 79.72 79.75 79.75 79.75 79.74 79.75 79.76 79.77 79.77 79.76 79.75 79.76 79.75 79.75 79.76 79.77 79.77 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

Results - Classification networks Training: % Correct MCC #Networks Best Single Architecture 79.5 0.587 10 All Architectures 79.7 0.592 400 Top 20 Architectures 79.8 0.593 200 28

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Results - Classification networks Training: % Correct MCC #Networks Best Single Architecture 79.5 0.587 10 All Architectures 79.7 0.592 400 Top 20 Architectures 79.8 0.593 200 Evaluation: % Correct MCC Dor and Zhou 78.8 Not Published NetsurfP CB500/CB513 79.0 0 0.577 30

Results Evaluation 31

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

NetSurfP 33

NetDiseaseSNP Disease-SNP prediction (Morten Bo Johansen) Without NetSurfP: Cross-validation: MCC= 0.569 Cross-Evaluation: MCC= 0.560 With NetSurfP: Cross-validation: MCC= 0.583 Cross-Evaluation: MCC= 0.572 34

Paper is out..what then? 35

Statistics Submissions to the webserver from CBS website 36

Paper is out..what then? 37

Paper is out..what then? 38

Paper is out..what then? 39

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As of 12 Jan 2011 136003 sequences submitted from 13494 unique IP s 43

First citation 24 october 2009 :-) 44

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