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IT og Sundhed 2010/11

Sequence based predictors. Secondary structure and surface accessibility Bent Petersen 13 January 2011. IT og Sundhed 2010/11. NetSurfP. Real Value Solvent Accessibility predictions with amino acid associated reliability. Objective.

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IT og Sundhed 2010/11

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  1. Sequence based predictors. Secondary structure and surface accessibility Bent Petersen13 January 2011 IT og Sundhed 2010/11

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

  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

  4. What is ASA? • Accessible Solvent Area, Å2 • Surface area accessible to a rolling water molecule

  5. RSA RSA = Relative Solvent AccessibilityACC = Accessible area in protein structureASA = 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

  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

  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

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

  9. Define Secondary Structure of Proteins, DSSP 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. ==== 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

  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

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

  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

  13. 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

  14. PISCES

  15. 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)

  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: 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%)

  17. 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)

  18. 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%)

  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 PROTEINHGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFTWKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAFYQAIDVNLSKBAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAAABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAAAAAAAABABB Prediction on middle residue Serine, buried

  23. Neural network window Sliding window of 7 170 2BEM.A mol:aa CHITIN-BINDING PROTEINHGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFTWKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAFYQAIDVNLSKBAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAAABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAAAAAAAABABB Prediction on middle residue Proline, exposed

  24. Neural network window Sliding window of 7 170 2BEM.A mol:aa CHITIN-BINDING PROTEINHGYVESPASRAYQCKLQLNTQCGSVQYEPQSVEGLKGFPQAGPADGHIASADKSTFFELDQQTPTRWNKLNLKTGPNSFTWKLTARHSTTSWRYFITKPNWDASQPLTRASFDLTPFCQFNDGGAIPAAQVTHQCNIPADRSGSHVILAVWDIADTANAFYQAIDVNLSKBAAABBAAAAAAAABBBBABBABBAABBABAABABBBAABBBABBABAAAABBBBABAAABABBBAABABBABAABABAAABABBBBAABAAAAAAABBBABABBBAAABAABBBAAAAAABBBBBABBBABABABAABBABBBAAAAAAAAABBBBBAAAAAAAABABB 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

  27. Results - Classification networks • Training:

  28. Results - Classification networks • Training: • Evaluation:

  29. Results • Evaluation

  30. NetSurfP /usr/cbs/bio/src/NetSurfP/NetSurfP -h

  31. NetSurfP

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

  33. Paper is out..What then?

  34. Statistics • Submissions to the webserver from CBS website

  35. Paper is out..What then?

  36. Paper is out..What then?

  37. Paper is out..What then?

  38. As of 12 Jan 2011 136003 sequences submitted from 13494unique IP’s

  39. First citation 24 october 2009 :-)

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