Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences

Proteins: Structure, Function and Genetics

Volumn 55, Issue 4, 2004, Pages 1036-1042

  Chen, Yu Ching ^a   Lin, Yeong Shin ^a   Lin, Chih Jen ^b   Hwang, Jenn Kang ^a  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Cysteine state sequences; Disulfide bonds; Multiple feature vectors; Support vector machines

Indexed keywords

CYSTEINE;

ACCURACY; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; CORRELATION COEFFICIENT; CYSTEINE STATE SEQUENCE; DISULFIDE BOND; MACHINE; MULTIPLE FEATURE VECTOR; PREDICTION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

AMINO ACIDS; COMPUTATIONAL BIOLOGY; CYSTEINE; CYSTINE; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 2542586190     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.20079     Document Type: Article

References (40)

1. Clark J, Fersht A. Engineered disulfide bonds as probes of the folding pathway of branase-increasing the stability of proteins against the rate of denaturation. Biochemistry 1993;32:4322-4329.
2. Hwang J-K, Pan J-J. Classical trajectory mapping approach for simulations of classical reactions in solution and in enzymes. J Phys Chem 1996;100:909-912.
3. Akamatsu Y, Ohno T, Hirota K, Kagoshima H, Yodoi J, Shigesada K. Redox regulation of the DNA binding activity in transcription factor PEBP2: the roles of two conserved cysteine residues. J Biol Chem 1997;272:14497-14500.
4. Abkevich VI, Shaknovich EI. What can disulfide bonds tell us about protein energetics, function and folding: simulations and bioinformatics analysis. J Mol Biol 2000;300:975-985.
5. Clarke J, Hounslow AH, Bond CJ, Fersht AR, Daggett V. The effects of disulfide bonds on the denatured state of barnase. Protein Sci 2000;9:2394-2404.
6. Wedemeyer WJ, Welker E, Narayan M, Scheraga HA. Disulfide bonds and protein folding. Biochemistry 2000;39:4207-4215.
7. Yokota A, Izutani K, Takai M, Kubo Y, Noda Y, Koumoto Y, Tachibana H, Segawas S. The transition state in the folding-unfolding reaction of four species of three-disulfide variant of hen lysozyme: the role of each disulfide bridge. J Mol Biol 2000;295: 1275-1288.
8. Gladyshev VN. Thioredoxin and peptide methionine sulfoxide reductase: convergence of similar structure and function in distinct structural folds. Proteins 2002;46:149-152.
9. Anfinsen C, Scheraga HA. Experimental and theoretical aspects of protein folding. Adv Protein Chem 1975;29:205-300.
10. Vielle C, Zeikus G. Hyperthermophilic enzymes: sources, uses, and molecular mechanisms for thermostability. Microbiol Mol Biol Rev 2001;65:1-43.
11. Hogg PJ. Disulfide bonds as switches for protein function. Trends Biochem Sci 2003;28:210-214.
12. Chuang CC, Chen CY, Yang J-M, Lyu PC, Hwang J-K. Relationship between protein structures and disulfide-bonding patterns. Proteins 2003;53:1-5.
13. Muskal SM, Holbrook SR, Kim SH. Prediction of the disulfide-bonding state of cysteine in proteins. Protein Eng 1990;3:667-672.
14. Fiser A, Cserzo M, Tudos E, Simon I. Different sequence environments of cysteines and half cysteines in proteins: application to predict disulfide forming residues. FEBS Lett 1992;302:117-120.
15. Fiser A, Simon I. Predicting the oxidation state of cysteines by multiple sequence alignment. Bioinformatics 2000;16:251-256.
16. Martelli PL, Fariselli P, Malaguti L, Casadio R. Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks. Protein Eng 2002;15:951-953.
17. Muccielli-Giorgi MH, Hazout S, Tuffery P. Predicting the disulfide bonding state of cysteines using protein descriptors. Proteins 2002;46:243-249.
18. Fariselli P, Riccobelli P, Casadio R. Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins 1999;36:340-346.
19. Martelli PL, Fariselli P, Malaguti L, Casadio R. Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy. Protein Sci 2002;11:2735-2739.
20. Krogh A, Riis SK. Hidden neural networks. Neural Comput 1999;11:541-563.
21. Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.
22. Jaakkola T, Kiekhans M, Haussler D. Using the Fisher kernel method to detect remote protein homologies. Intel Syst Mol Biol 1999;149-158.
23. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares MJ, Haussler D. Knowledge-based analysis of microarray gene expression data by sing support vector machine. Proc Natl Acad Sci USA 2000;97:262-267.
24. Dingg CHQ, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001;17:349-358.
25. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 2001;308:397-407.
26. Yu C-S, Wang J-Y, Yang J-M, Lyu PC, Lin C-J, Hwang J-K. Fine grained protein fold assignment by support vector machines using generalized n peptide coding schemes and jury voting from multiple-parameter sets. Proteins 2003;50:531-536.
27. Yu C-S, Lin C-J, Hwang J-K. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide composition. Protein Science 2004 (in press).
28. Schneider R, Sander C. Database of homology-derived structures and the structural meaning of sequence alignment. Proteins 1991;9:56-58.
29. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993;232:584-599.
30. Meiler J, Muller M, Zeidler A, Schmaschke F. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. J Mol Model 2001;7:360-369.
31. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res 2000;28:235-242.
32. Durbin R, Eddy SR, Krogh A, Mitchison G. Biological sequence analysis. New York: Cambridge University Press; 1998.
33. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. 2001. Software available from http://www.csie.ntu. edu.tw/~cjlin/libsvm.
34. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 1987;4:406-425.
35. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975;405:442-451.
36. Harrison PM, Sternberg MJE. The disulfide beta-cross: from cystine geometry and clustering to classification of small disulfide-rich protein folds. J Mol Biol 1996;264:603-623.
37. Yokota A, Izutani K, Takai M, Kubo Y, Koumoto Y, Tachibana H, Segawa S. The transition state in the folding-unfolding reaction of four species of three-disulfide variant of hen lysozyme: the role of each disulfide bridge. J Mol Biol 2000;295:1275-1288.
38. Hinck AP, Truckses Dm, Markley JL. Engineered disulfide bonds in staphylococcal nuclease: effects on the stability and conformation of the folded protein. Biochemistry 1996;35:10328-10338.
39. Mansfeld J, Vriend G, Dijkstra BW, Veltman OR, Van den Burg B, Venema G, Ulbrich-Hoffmann R, Eijsink VG. Extreme stabilization of a thermolysin-like protease by an engineered disulfide bond. J Biol Chem 1997;272:11152-11156.
40. Shimaoka M, Lu C, Salas A, Xiao T, Takagi J, Springer TA. Stabilizing the integrin alpha M inserted domain in alternative conformations with a range of engineered disulfide bonds. Proc Natl Acad Sci USA 2002;99:16737-16741.