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Neural Networks Predict Protein Structure and Function

  • Marco Punta
  • Burkhard Rost
Part of the Methods in Molecular Biology™ book series (MIMB, volume 458)

Abstract

Both supervised and unsupervised neural networks have been applied to the prediction of protein structure and function. Here, we focus on feedforward neural networks and describe how these learning machines can be applied to protein prediction. We discuss how to select an appropriate data set, how to choose and encode protein features into the neural network input, and how to assess the predictor's performance.

Keywords

Feedforward neural networks protein structure secondary structure overfitting performance estimate. 

Abbreviations

aa

amino acids

AUC

area under the ROC curve

FN

false negative

FP

false positive

FPR

false- positive rate

NFP

number of free parameters

NHN

number of hidden nodes

NN

feedforward neural network

PDB

protein data bank

ROC

receiver operating characteristics

SS

secondary structure

TN

true negative

TP

true positive

TPR

true-positive rate

Notes

Acknowledgements

Thanks to Hans-Erik G. Aronson (Columbia) for computer assistance; thanks to Dariusz Przybylski (Columbia) for important discussions and very useful comments on the manuscript. This work was supported by Grants U54-GM072980 and U54 GM75026-01 from the National Institutes of Health (NIH) and Grant NIH/NLM R01-LM07329-01 from the NIH and the National Library of Medicine..

References

  1. 1.
    Przybylski D, Rost B (2006) Predicting simplified features of protein structure. In: Lengauer T (ed) Bioinformatics: from genomes to therapies. Wiley-VCH.Google Scholar
  2. 2.
    Blom N, Hansen J, Blaas D, Brunak S (1996) Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks Protein Sci 5:2203–2216.CrossRefPubMedGoogle Scholar
  3. 3.
    Nielsen H, Engelbrecht J, Brunak S, von Heijne G (1997) A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites Int J Neural Syst 8:581–599.CrossRefPubMedGoogle Scholar
  4. 4.
    Nielsen H, Brunak S, von Heijne G (1999) Machine learning approaches for the prediction of signal peptides and other protein sorting signals Protein Eng 12:3–9.CrossRefPubMedGoogle Scholar
  5. 5.
    Li X, Romero P, Rani M, Dunker AK, Obradovic Z (1999) Predicting protein disorder for N-, C-, and internal regions. Genome inform ser workshop. Genome Inform. 10:30–40.PubMedGoogle Scholar
  6. 6.
    Sodhi JS, Bryson K, McGuffin LJ, Ward JJ, Wernisch L, Jones DT (2004) Predicting metal-binding site residues in low-resolution structural models J Mol Biol 342:307–320.CrossRefPubMedGoogle Scholar
  7. 7.
    Passerini A, Punta M, Ceroni A, Rost B, Frasconi P (2006) Identifying cysteines and histidines in transition metal binding sites using support vector machines and neural networks Proteins: Structure, Function and Bioinformatics 65:305–316.CrossRefGoogle Scholar
  8. 8.
    Nair R, Rost B (2003) Better prediction of sub-cellular localization by combining evolutionary and structural information Proteins 53:917–930.CrossRefPubMedGoogle Scholar
  9. 9.
    Emanuelsson O, Nielsen H, Brunak S, von Heijne, G. (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300:1005–1016.CrossRefPubMedGoogle Scholar
  10. 10.
    Reinhardt A, Hubbard T (1998) Using neural networks for prediction of the subcellular location of proteins Nucleic Acids Res 26:2230–2236.CrossRefPubMedGoogle Scholar
  11. 11.
    Jensen LJ, Gupta R, Blom N, Devos D, Tamames J, Kesmir C, Nielsen H, Staerfeldt HH, Rapacki K, Workman C, Andersen CA, Knudsen S, Krogh A, Valencia A, Brunak S (2002) Prediction of human protein function from post-translational modifications and localization features. J Mol Biol 319:1257–1265.CrossRefPubMedGoogle Scholar
  12. 12.
    Wu CH (1997) Artificial neural networks for molecular sequence analysis Comput Chem 21:237–256.CrossRefPubMedGoogle Scholar
  13. 13.
    Creighton TE (1993) Proteins: structure and molecular properties. W.H. Freeman, New York.Google Scholar
  14. 14.
    Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z (2002) Intrinsic disorder and protein function Biochemistry. 41:6573–6582.CrossRefPubMedGoogle Scholar
  15. 15.
    Dunker AK, Cortese MS, Romero P, Iakoucheva LM, Uversky VN (2005) Flexible nets. The roles of intrinsic disorder in protein interaction networks Febs J 272:5129–5148.CrossRefPubMedGoogle Scholar
  16. 16.
    Soto C, Estrada L, Castilla J (2006) Amyloids, prions and the inherent infectious nature of misfolded protein aggregates Trends Biochem Sci 31:150–155.CrossRefPubMedGoogle Scholar
  17. 17.
    Carugo O, Argos P (1997) Protein-protein crystal-packing contacts Protein Sci 6:2261–2263.CrossRefPubMedGoogle Scholar
  18. 18.
    Snyder DA, Bhattacharya A, Huang YJ, Montelione GT (2005) Assessing precision and accuracy of protein structures derived from NMR data Proteins 59:655–661.CrossRefPubMedGoogle Scholar
  19. 19.
    Brenner SE (2001) A tour of structural genomics Nat Rev Genet 2:801–809.CrossRefPubMedGoogle Scholar
  20. 20.
    Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, et al.(1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Scienc. 269:496–512.Google Scholar
  21. 21.
    Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden- Tillson H, Pfannkoch C, Rogers YH, Smith H.O (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304:66–74.CrossRefPubMedGoogle Scholar
  22. 22.
    Tringe SG, Rubin EM (2005) Metagenomics: DNA sequencing of environmental samples. Nat Rev Genet 6:805–814.CrossRefPubMedGoogle Scholar
  23. 23.
    Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Feng Z, Gilliland GL, Iype L, Jain S, Fagan P, Marvin J, Padilla D, Ravichandran V, Schneider B, Thanki N, Weissig H, Westbrook JD, Zardecki C (2002) The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 58:899–907.CrossRefPubMedGoogle Scholar
  24. 24.
    Chandonia JM, Brenner SE (2006) The impact of structural genomics: expectations and outcomes. Science 311:347–351.CrossRefPubMedGoogle Scholar
  25. 25.
    Petrey D, Honig B (2005) Protein structure prediction: inroads to biology. Mol Cell 20:811–819.CrossRefPubMedGoogle Scholar
  26. 26.
    Jacobson M, Sali A (2004) Comparative protein structure modeling and its applications to drug discovery Annual Reports in Medicinal Chemistry 39:259–276.CrossRefGoogle Scholar
  27. 27.
    Godzik A (2003) Fold recognition methods. Methods Biochem Anal 44:525–546.PubMedGoogle Scholar
  28. 28.
    Watson JD, Laskowski RA, Thornton JM (2005) Predicting protein function from sequence and structural data. Curr Opin Struct Biol 15:275–284.CrossRefPubMedGoogle Scholar
  29. 29.
    Whisstock JC, Lesk AM (2003) Prediction of protein function from protein sequence and structure. Q Rev Biophys 36:307–340.CrossRefPubMedGoogle Scholar
  30. 30.
    Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31:365–370.CrossRefPubMedGoogle Scholar
  31. 31.
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman D J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402.CrossRefPubMedGoogle Scholar
  32. 32.
    Rost, B. (2003) Neural networks predict protein structure: hype or hit? In: Frasconi P (ed) Artificial intelligence and heuristic methods for bioinformatics. IOS Press, Amsterdam, pp. 34–50.Google Scholar
  33. 33.
    Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Mazumder R, O'Donovan C, Redaschi N, Suzek B (2006) The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res 34:D187–D191.CrossRefPubMedGoogle Scholar
  34. 34.
    Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bradley P, Bork P, Bucher P, Cerutti L, Copley R, Courcelle E, Das U, Durbin R, Fleischmann W, Gough J, Haft D, Harte N, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lonsdale D, Lopez R, Letunic I, Madera M, Maslen J, McDowall J, Mitchell A, Nikolskaya AN, Orchard S, Pagni M, Ponting CP, Quevillon E, Selengut J, Sigrist CJ, Silventoinen V, Studholme DJ, Vaughan R, Wu CH (2005) InterPro, progress and status in 2005. Nucleic Acids Res 33:D201–D205.CrossRefPubMedGoogle Scholar
  35. 35.
    Andreeva A, Howorth D, Brenner SE, Hubbard TJ, Chothia C, Murzin AG (2004) SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 32:D226–D229.CrossRefPubMedGoogle Scholar
  36. 36.
    Pearl F, Todd A, Sillitoe I, Dibley M, Redfern O, Lewis T, Bennett C, Marsden R, Grant A, Lee D, Akpor A, Maibaum M, Harrison A, Dallman T, Reeves G, Diboun I, Addou S, Lise S, Johnston C, Sillero A, Thornton J, Orengo C (2005) The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis. Nucleic Acids Res 33:D247–D251.CrossRefPubMedGoogle Scholar
  37. 37.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29.CrossRefPubMedGoogle Scholar
  38. 38.
    Li W, Jaroszewski L, Godzik A (2001) Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17:282–283.CrossRefPubMedGoogle Scholar
  39. 39.
    Holm, L, Sander C (1998) Removing near-neighbour redundancy from large protein sequence collections. Bioinformatics 14:423–439.CrossRefPubMedGoogle Scholar
  40. 40.
    Rost B (1999) Twilight zone of protein sequence alignments. Protein Eng 12:85–94.CrossRefPubMedGoogle Scholar
  41. 41.
    Rost B, Liu J, Nair R, Wrzeszczynski KO, Ofran Y (2003) Automatic prediction of protein function. Cell Mol Life Sci 60:2637–2650.CrossRefPubMedGoogle Scholar
  42. 42.
    Koh IY, Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, Eswar N, Grana O, Pazos F, Valencia A, Sali A, Rost B (2003) EVA: Evaluation of protein structure prediction servers. Nucleic Acids Res 31:3311–3315.CrossRefPubMedGoogle Scholar
  43. 43.
    Sander C, Schneider R (1991) Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 9:56–68.CrossRefPubMedGoogle Scholar
  44. 44.
    Mika, S, Rost B (2003) UniqueProt: Creating representative protein sequence sets. Nucleic Acids Res 31:3789–3791.CrossRefPubMedGoogle Scholar
  45. 45.
    Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99.CrossRefPubMedGoogle Scholar
  46. 46.
    Dunbrack RL Jr (2006) Sequence comparison and protein structure prediction. Curr Opin Struct Biol 16:374–384.CrossRefPubMedGoogle Scholar
  47. 47.
    Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47:228–235.CrossRefPubMedGoogle Scholar
  48. 48.
    Karchin R, Cline M, Mandel-Gutfreund Y, Karplus K (2003) Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry. Proteins 51:504–514.CrossRefPubMedGoogle Scholar
  49. 49.
    Chen CP, Kernytsky A, Rost B (2002) Transmembrane helix predictions revisited. Protein Sci 11:2774–2791.CrossRefPubMedGoogle Scholar
  50. 50.
    Siew N, Fischer D (2003) Analysis of singleton ORFans in fully sequenced microbial genomes. Proteins 53:241–2451.CrossRefPubMedGoogle Scholar
  51. 51.
    Siew N, Fischer D (2003) Twenty thousand ORFan microbial protein families for the biologist? Structure 11:7–9.CrossRefPubMedGoogle Scholar
  52. 52.
    Kyrpides NC, Ouzounis CA (1998) Errors in genome reviews. Science 281:1457.CrossRefPubMedGoogle Scholar
  53. 53.
    Iyer LM, Aravind L, Bork P, Hofmann K, Mushegian AR, Zhulin IB, Koonin EV (2001) Quod erat demonstrandum? The mystery of experimental validation of apparently erroneous computational analyses of protein sequences. Genome Biol 2:51.CrossRefGoogle Scholar
  54. 54.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637.CrossRefPubMedGoogle Scholar
  55. 55.
    Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins 23:566–5579.CrossRefPubMedGoogle Scholar
  56. 56.
    Chou PY, Fasman GD (1974) Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. Biochemistry 13:211–222.CrossRefPubMedGoogle Scholar
  57. 57.
    Demeler B, Zhou GW (1991) Neural network optimization for E. coli promoter prediction. Nucleic Acids Res 19:1593–1599.CrossRefPubMedGoogle Scholar
  58. 58.
    Fan, K, Wang W (2003) What is the minimum number of letters required to fold a protein? J Mol Biol 328:921–926.CrossRefPubMedGoogle Scholar
  59. 59.
    Wang J, Wang W (1999) A computational approach to simplifying the protein folding alphabet. Nat Struct Biol 6:1033–1038.CrossRefPubMedGoogle Scholar
  60. 60.
    Chan HS (1999) Folding alphabets. Nat Struct Biol 6:994–996.CrossRefPubMedGoogle Scholar
  61. 61.
    Rost B. Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232:584–599.CrossRefPubMedGoogle Scholar
  62. 62.
    Rychlewski L. Fischer D (2005) LiveBench-8: the large-scale, continuous assessment of automated protein structure prediction. Protein Sci 14:240–245.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science + Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular BiophysicsColumbia UniversityNew YorkUSA
  2. 2.Department of Biochemistry and Molecular Biophysics, Columbia UniversityColumbia University Center for Computational Biology and Bioinformatics; and North East Structural Genomics Consortium (NESG), Columbia UniversityUSA

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