Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Immunome Research

Volumn 3, Issue 1, 2007, Pages

  Ray, Surajit ^a   Kepler, Thomas B ^b  

^a Boston University   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38049150954     PISSN: None     EISSN: 17457580     Source Type: Journal    
DOI: 10.1186/1745-7580-3-9     Document Type: Article

References (44)

1. J Klein, Natural history of the major histocompatibility complex Wiley New York 1986
2. Methods for prediction of peptide binding to MHC molecules: a comparative study. K Yu N Petrovsky C Schonbach JYL Koh V Brusic, Mol Med 2002 8 3 137 148 12142545
3. Profile hidden Markov models (review). SR Eddy, Bioinformatics 1998 14 9 755 763 [citeseer.csail.mit. edu/eddy98profile.html] 10.1093/bioinformatics/14. 9.755 9918945
4. R Durbin S Eddy A Krogh G Mitchison, Biological sequence analysis Cambridge University Press 1998
5. VN Vapnik, The nature of statistical learning theory New York: Springer-Verlag 1995
6. L Breiman J Friedman R Olshen C Stone, Classification and Regression Trees Monterey, CA: Wadsworth and Brooks 1984
7. Bagging predictors. L Breiman, Mach Learn 1996 24 2 123 140
8. Random Forests. L Breiman, Mach Learn 2001 45 5 32 10.1023/A: 1010933404324
9. Identifiying human MHC supertypes using bioinformatic methods. IA Doytchinova P Guan DR Flower, J Immunol 2004 172 7 4314 4323 15034046
10. Towards in silico prediction of immunogenic epitopes. DR Flower, Trends Immunol 2003 24 12 667 674 10.1016/j.it.2003.10.006 14644141
11. A simple method for displaying the hydropathic character of a protein. J Kyte RF Doolittle, J Mol Biol 1982 157 105 132 10.1016/0022-2836(82)90515-0 7108955
12. Genomewide conserved epitope profiles of HIV-1 predicted by biophysical properties of MHC binding peptides. MH Sung R Simon, J Comput Biol 2004 11 125 145 10.1089/106652704773416920 15072692
13. AAindex: Amino Acid Index Database. S Kawashima H Ogata M Kanehisa, Nucleic Acids Res 1999 27 368 369 http://www.genome.ad.jp/dbget/aaindex.html 9847231 10.1093/nar/27.1.368
14. Prediction of MHC Class I Binding Peptides Using an Ensemble Learning Approach. N Majeux K Udaka H Mamitsuka, Genome Informatics 2003 687 688
15. Analysis of MHC-Peptide Binding Using Amino Acid Property-Based Decision Rules. J Supper P Donnes O Kohlbacher, Springer Lecture Notes in Computer Science (LNCS) 3686 2005 446 453
16. JR Quinlan, C4.5: Programs for Machine Learning Morgan Kaufmann 1993
17. Data Mining Tools See5 and C5.0. JR Quinlan, 2004 http://www.rulequest. com/see5-info.html, Http://www.rulequest.com/see5-info.html
18. Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201. L Zhihua W Yuzhang Z Bo N Bing W Li, J Comput Biol 2004 11 4 683 694 10.1089/cmb.2004.11.683 15579238
19. Epitope Prediction Algorithms for Peptide based Vaccine Design. L Florea B Halldorsson O Kohlbacher R Schwartz S Hoffman S Istrail, Proceedings of the IEEE Computer Society Conference on Bioinformatics, IEEE Computer Society 2003 17 26
20. Prediction of genomewide conserved epitope profiles of HIV-1: classifier choice and peptide representation. Y Xiao MR Segal, Stat Appl Genet Mol Biol 2005 4 Article25 16646843
21. Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties. S Li X Yao H Liu J Li B Fan, Anal Chim Acta 2007 584 37 42 10.1016/j.aca.2006.11.037 17386582
22. PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. ZR Li HH Lin LY Han L Jiang X Chen YZ Chen, Nucleic Acids Res 2006 34 Web Server 32 37 16397295 10.1093/nar/gkl305
23. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. J Cui LY Han HH Lin ZQ Tang L Jiang ZW Cao YZ Chen, Immunogenetics 2006 58 8 607 613 10.1007/s00251-006-0117-2 16832638
24. Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201. P Guan IA Doytchinova VA Walshe P Borrow DR Flower, J Med Chem 2005 48 23 7418 7425 10.1021/jm0505258 16279801
25. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. J Cui LY Han HH Lin HL Zhang ZQ Tang CJ Zheng ZW Cao YZ Chen, Mol Immunol 2007 44 5 866 877 10.1016/j.molimm.2006.04.001 16806474
26. Peptide reagent design based on physical and chemical properties of amino acid residues. QS Du RB Huang YT Wei CH Wang KC Chou, Journal of Computational Chemistry 2007 28 2043 2050 10.1002/jcc.20732 17450553
27. Is it possible to analyze DNA and protein sequences by the methods of digital signal processing? V Veljkovic I Cosic B Dimitrijevic D Lalovic, IEEE Trans Biomed Eng 1985 32 5 337 341 10.1109/TBME.1985.325549 2581884
28. Macromolecular bioactivity: is it resonant interaction between macromolecules?-Theory and applications. I Cosic, IEEE Trans Biomed Eng 1994 41 12 1101 1114 10.1109/10.335859 7851912
29. The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. DR Madden DN Garboczi DC Wiley, Cell 1993 75 4 693 708 10.1016/0092-8674(93)90490-H 7694806
30. Emerging principles for the recognition of peptide antigens by MHC class I molecules. M Matsumura DH Fremont PA Peterson IA Wilson, Science 1992 257 5072 927 934 10.1126/science.1323878 1323878
31. Crystal structure of the major histocompatibility complex class I H-2Kb molecule containing a single viral peptide: implications for peptide binding and T-cell receptor recognition. W Zhang AC Young M Imarai SG Nathenson JC Sacchettini, Proc Natl Acad Sci USA 1992 89 17 8403 8407 1325657 10.1073/pnas.89.17.8403
32. Crystal structure of an H-2Kb-ovalbumin peptide complex reveals the interplay of primary and secondary anchor positions in the major histocompatibility complex binding groove. DH Fremont EA Stura M Matsumura PA Peterson IA Wilson, Proc Natl Acad Sci USA 1995 92 7 2479 2483 7708669 10.1073/pnas.92.7.2479
33. The three-dimensional structure of peptide-MHC complexes. DR Madden, Annu Rev Immunol 1995 13 587 622 10.1146/annurev.iy.13.040195.003103 7612235
34. Atomic structure of a human MHC molecule presenting an influenza virus peptide. ML Silver HC Guo JL Strominger DC Wiley, Nature 1992 360 6402 367 369 10.1038/360367a0 1448154
35. Crystal structures of two viral peptides in complex with murine MHC class I H-2Kb. DH Fremont M Matsumura EA Stura PA Peterson IA Wilson, Science 1992 257 5072 919 927 10.1126/science.1323877 1323877
36. Structural principles that govern the peptide-binding motifs of class I MHC molecules. C Zhang A Anderson C DeLisi, J Mol Biol 1998 281 5 929 947 10.1006/jmbi.1998.1982 9719645
37. Computational determination of side chain specificity for pockets in class I MHC molecules. G Vasmatzis C Zhang JL Cornette C DeLisi, Mol Immunol 1996 33 16 1231 1239 10.1016/S0161-5890(96)00090-9 9129159
38. Free energy mapping of class I MHC molecules and structural determination of bound peptides. U Sezerman S Vajda C DeLisi, Protein Sci 1996 5 7 1272 1281 8819160
39. Modeling the structure of bound peptide ligands to major histo-compatibility complex. JC Tong TW Tan S Ranganathan, Protein Sci 2004 13 9 2523 2532 10.1110/ps.04631204 15322290
40. MHCPEP a database of MHC-binding peptides: update 1997. V Brusic G Rudy L Harrison, Nucleic Acids Res 1998 26 368 371 9399876 10.1093/nar/26.1.368
41. R Development Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 2003 http://www.R-project.org [ISBN 3-900051-00-3]
42. Robust Classification for Imprecise Environments. F Provost T Fawcett, Machine Learning 2001 42 203 231 10.1023/A:1007601015854
43. Protein volume in solution. A Zamyatnin, Prog Bio-phys Mol Biol 1972 24 107 23 10.1016/0079-6107(72)90005-3
44. The characterization of amino acid sequences in proteins by statistical methods. JM Zimmerman N Eliezer R Simha, J Theor Biol 1968 21 2 170 201 10.1016/0022-5193(68)90069-6 5700434