Peptide bioinformatics: Peptide classification using peptide machines

Methods in Molecular Biology

Volumn 458, Issue , 2008, Pages 159-183

  Yang, Zheng Rong ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

bioinformatics; peptide classification; peptide machines

Indexed keywords

PEPTIDE; PROTEIN;

ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CHEMISTRY; CLASSIFICATION; HUMAN; METHODOLOGY; MUTATION; REVIEW; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BIOLOGY; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, STATISTICAL; MUTATION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PEPTIDES; PROTEINS;

EID: 58149393648     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-60327-101-1_9     Document Type: Article

References (58)

1. Schechter I, Berger A (1968) On the active site of proteases, 3. Mapping the active site of papain; specific peptide inhibitors of papain. Biochem Biophys Res Comms 32:898.
2. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294:1351-1362.
3. Kobata A (1984) The carbohydrates of glycoproteins. In: GinsburgV, Robins PW (eds) Biology of carbohydrates. Wiley, New York.
4. Yang ZR, Wang L, Young N, Trudgian D, Chou KC (2005) Machine learning algorithms for protein functional site recognition. Current Protein and Peptide Science 6:479-491.
5. Dunker AK, Obradovic Z, Romero P, Garner EC, Brown CJ (2000) Intrinsic protein disorder in complete genomes. Genome Inform Ser Workshop Genome Inform 11:161-171.
6. Baldi P, Pollastri G, Andersen C, Brunak S (2000) Matching protein beta-sheet partners by feedforward and recurrent neural networks. Proceedings of International Conference on Intelligent Systems for Molecular Biology, ISMB 8:25-36.
7. Yang ZR, Thomson R, McNeil P, Esnouf R (2005) RONN: Use of the bio-basis function neural network technique for the detection of natively disordered regions in proteins. Bioinformatics 21:3369-3376.
8. Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models. J Molec Biol 202:865-884.
9. Thomson R, Hodgman TC, Yang ZR, Doyle AK (2003) Characterising proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics 19:1741-1747.
10. Yang ZR, Thomson R (2005) A novel neural network method in mining molecular sequence data. IEEE Trans on Neural Networks 16:263-274.
11. Altschul SF, Gish W, Miller W, Myers E, Lipman, DJ (1990) Basic local alignment search tool. J. Molec. Biol. 215:403-410.
12. Dayhoff MO, Schwartz RM, Orcutt BC 1978 A model of evolutionary change in proteins. matrices for detecting distant relationships. In: Dayhoff MO (ed) Atlas of protein sequence and structure, 5:345-358.
13. Johnson MS, Overington JP (1993) A structural basis for sequence comparisons-An evaluation of scoring methodologies. J Mol Biol 233:716-738.
14. Yang ZR (2004) Application of support vector machines to biology. Briefings in Bioinformatics 5:328-338.
15. Edelman J, White SH (1989) Linear optimization of predictors for secondary structure: Application to transbilayer segments of membrane proteins. J Mole Biol 210:195-209.
16. Yang ZR, Berry E (2004) Reduced bio-basis function neural networks for protease cleavage site prediction. J Comput Biol Bioinformatics 2:511-531.
17. Thomson R, Esnouf R (2004) Predict disordered proteins using bio-basis function neural networks. Lecture Notes in Computer Science 3177:19-27.
18. Berry E, Dalby A, Yang ZR (2004) Reduced bio basis function neural network for identification of protein phosphorylation sites: Comparison with pattern recognition algorithms. Comput Biol Chem 28:75-85.
19. Senawongse P Dalby AD, Yang ZR (2005) Predicting the phosphorylation sites using hidden Markov models and machine learning methods. J Chem Info Comp Sci 45:1147-1152.
20. Yang ZR, Chou KC (2004) Bio-basis function neural networks for the prediction of the Olinkage sites in glyco-proteins. Bioinformatics 20:903-908.
21. Sidhu A, Yang ZR (2006) Predict signal peptides using bio-basis function neural networks. Applied Bioinformatics 5(1):13-19.
22. Yang ZR, Dry J, Thomson R, Hodgman C (2006) A bio-basis function neural network for protein peptide cleavage activity characterisation. Neural Networks 19:401-407.
23. Yang ZR, Young N (2005) Bio-kernel self-organizing map for HIV drug resistance classification. Lecture Notes in Computer Science 3610:179-184.
24. Yang ZR (2005) Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks. Bioinformatics 21:1831-1837.
25. Yang ZR (2005) Mining SARS-coV protease cleavage data using decision trees, a novel method for decisive template searching. Bioinformatics 21:2644-2650.
26. Yang ZR, Johanathan F (2005) Predict T-cell epitopes using bio-support vector machines. J Chem Inform Comp Sci 45:1142-1148.
27. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York.
28. Scholkopf B (2000) The kernel trick for distances. Technical report, Microsoft Research, May.
29. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Machine Learning Res 1:211-244.
30. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans on Neural Networks 2:302-309.
31. Yang ZR, Chou KC (2003) Bio-support vector machines for computational proteomics. Bioinformatics 19:1-7.
32. MacKay DJ (1992) A practical Bayesian framework for backpropagation networks. Neural Computation 4:448-472.
33. Yang ZR (2005) Orthogonal kernel machine in prediction of functional sites in proteins. IEEE Trans on Systems, Man and Cybernetics 35:100-106.
34. Yang ZR, Thomas A, Young N, Everson R (2006) Relevance peptide machine for HIV-1 drug resistance prediction. IEEE Trans on Computational Biology and Bioinformatics (in press).
35. Putney, S (1992) How antibodies block HIV infection: Paths to an AIDS vaccine. Trends in Biochem Sci 7:191-196.
36. Klausner RD et al. (2003) The need for a global HIV vaccine enterprise. Science 300:2036-2039.
37. Kathryn S (2003) HIV vaccine still out of our grasp. The Lancet Infectious Diseases 3:457.
38. Weber IT, Harrison RW (1999) Molecular mechanics analysis of drug-resistant mutants of HIV protease. Protein Eng 12:469-474.
39. Jenwitheesuk E, Samudrala R (2005) Prediction of HIV-1 protease inhibitor resistance using a protein-inhibitor flexible docking approach. Antivir Ther 10:157-166.
40. Gallego O, Martin-Carbonero L, Aguero J, de Mendoza C, Corral A, Soriano V (2004) Correlation between rules-based interpretation and virtual phenotype interpretation of HIV-1 genotypes for predicting drug resistance in HIV-infected individuals. J Virol Methods 121:115-118.
41. De Luca A, Cingolani A et al. (2003) Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance. J Infect Dis 187:1934-1943.
42. Shenderovich MD, Kagan RM, Heseltine PN, Ramnarayan K (2003) Structure-based phenotyping predicts HIV-1 protease inhibitor resistance. Protein Sci 12:1706-1718.
43. Brenwinkel N, Schmidt B, Walter H, Kaiser R, Lengauer T, Hoffmann D, Korn K, Selbig J (2002) Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype. PNAS 99:8271-8276.
44. Sturmer M, Doerr HW, Staszewski S, Preiser W (2003) Comparison of nine resistance interpretation systems for HIV-1 genotyping. Antivir Ther 8:239-244.
45. Vergu E, Mallet A, Golmard JL (2002) The role of resistance characteristics of viral strains in the prediction of the response to antiretroviral therapy in HIV infection. J Acquir Immune Defic Syndr 30:263-270.
46. Smith CJ, Staszewski S et al. (2004) Use of viral load measured after 4 weeks of highly active antiretroviral therapy to predict virologic putcome at 24 weeks for HIV-1-positive individuals. J Acquir Immune Defic Syndr 37:1155-1159.
47. Beerenwinkel N, Daumer M et al. (2003) Geno2pheno: Estimating phenotypic drug resistance from HIV-1 genotypes. Nucleic Acids Res 31:3850-3855.
48. Foulkes AS, De GV (2002) Characterizing the relationship between HIV-1 genotype and phenotype: Prediction-based classification. Biometrics 58:145-156.
49. De Luca A, Vendittelli et al. (2004) Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes. Antivir Ther 9:583-593.
50. Draghici S, Potter RB (2003) Predicting HIV drug resistance with neural networks. Bioinformatics 19:98-107.
51. Shafer RW, Stevenson D, Chan B (1999) Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res 27:348-352.
52. Walter H, Schmidt B, Korn K, Vandamme AM, Harrer T, Uberla K (1999) Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors. J Clin Virol.13:71-80.
53. Sa-Filho DJ, Costa LJ, de Oliveira CF, Guimaraes APC, Accetturi CA, Tanuri A, Diaz RS (2003) Analysis of the protease sequences of HIV-1 infected individuals after indinavir monotherapy. J Clin Virology 28:186-202.
54. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405:442-451.
55. Francki R, Fauquet C, Knudson D, Brown F (1991) Classification and nomenclature of virus: Fifth report of the International Committee on Taxonomy of Viruses. Arch Virology 2:223s.
56. Choo Q, Kuo G, Weiner AJ, Overby L, Bradley D, Houghton M (1989) Isolation of a cDNA clone derived from a blood-borne non-A non-B viral hepatitis genome. Science 244:359-362.
57. Kuo G, Choo Q et al. (1989) An assay for circulating antibodies to a major etiologic virus of human non-A non-B hepatitis. Science 244:362-364.
58. Yang ZR (2006) Predicting hepatitis C virus protease cleavage sites using generalised linear indicator regression models. IEEE Trans on Biomedical Engineering 53:2119-2123.