A Bayesian regression approach to the prediction of MHC-II binding affinity

Computer Methods and Programs in Biomedicine

Volumn 92, Issue 1, 2008, Pages 1-7

  Zhang, Wen ^a,b   Liu, Juan ^a   Niu, Yan Qing ^a   Wang, Lian ^a   Hu, Xihao ^a  

^a WUHAN UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

MHC II binding affinity; Peptide flanking residues length; Peptide length; Quantitative prediction; Sparse Bayesian regression

Indexed keywords

BAYESIAN NETWORKS; BINDERS; BINDING ENERGY; CHLORINE COMPOUNDS; DATABASE SYSTEMS; FOOD PROCESSING; FORECASTING; HEALTH; LEARNING ALGORITHMS; PEPTIDES; PROTEINS;

BAYESIAN REGRESSION; BINDING AFFINITIES; DATA-SETS; IMMUNE MECHANISM; IMMUNE RESPONSES; MAJOR HISTOCOMPATIBILITY COMPLEX; MHC MOLECULES; MHC-II BINDING AFFINITY; PEPTIDE FLANKING RESIDUES LENGTH; PEPTIDE LENGTH; PREDICTION PERFORMANCE; QUANTITATIVE METHODS; QUANTITATIVE PREDICTION; RELEVANCE VECTOR MACHINE; SPARSE BAYESIAN LEARNING; SPARSE BAYESIAN REGRESSION;

AMINES;

AMINO ACID; MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 2; PEPTIDE;

ALGORITHM; ALLELE; ARTICLE; BAYES THEOREM; BINDING AFFINITY; DATA BASE; MODEL; NORMAL DISTRIBUTION; PREDICTION; REGRESSION ANALYSIS;

AMINO ACID SEQUENCE; BAYES THEOREM; BINDING SITES; COMPUTER SIMULATION; HISTOCOMPATIBILITY ANTIGENS CLASS II; MODELS, CHEMICAL; MOLECULAR SEQUENCE DATA; OLIGOPEPTIDES; PROTEIN BINDING; PROTEIN INTERACTION MAPPING; REGRESSION ANALYSIS; SEQUENCE ANALYSIS, PROTEIN;

EID: 49949115547     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.05.002     Document Type: Article

References (28)

1. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. (2001)
2. Donnes P., and Elofsson A. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3 (2002) 25
3. Jones E.Y. MHC class I and class II structures. Curr. Opin. Immunol. 9 1 (1997) 75-79
4. Brusic V., Rudy G., Honeyman G., et al. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14 2 (1998) 121-130
5. Nielsen M., Lundegaard C., Worning P., et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20 9 (2004) 1388-1397
6. Karpenko O., Shi J., and Dai Y. Prediction of MHC class II binders using the ant colony search strategy. Artif. Intell. Med. 35 1-2 (2005) 147-156
7. Murugan N., and Dai Y. Prediction of MHC class II binding peptides based on an iterative learning model. Immunome Res. 1 (2005) 6
8. Rajapakse M., Schmidt B., Feng L., and Brusic V. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms. BMC Bioinformatics 8 (2007) 459
9. Cui J., Han L.Y., Lin H.H., et al. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical attributes. Mol. Immunol. 44 5 (2007) 866-877
10. Salomon J., and Flower D.R. Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinformatics 7 (2006) 501
11. Doytchinova I.A., and Flower D.R. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics 19 17 (2003) 2263-2270
12. Wan J., Liu W., Xu Q., et al. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 7 (2006) 463
13. Singh H., and Raghava G.P.S. ProPred: prediction of HLA-DR binding sites. Bioinformatics 17 12 (2001) 1236-1237
14. Bui H.H., Sidney J., Peters B., et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57 5 (2005) 304-314
15. Nielsen M., Lundegaard C., and Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8 (2007) 459
16. Rogers S., and Girolami M. A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics 21 14 (2005) 3131-3137
17. Liyang W., Yongyi Y., Nishikawa R.M., et al. A Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Trans. Med. Imaging 24 10 (2005) 1278-1285
18. Carson R.T., Vignali K.M., Woodland D.L., et al. T cell receptor recognition of MHC class II-bound peptide flanking residues enhances immunogenicity and results in altered TCR V region usage. Immunity 7 3 (1997) 387-399
19. Bonomi G., Moschella F., Ombra M.N., et al. Modulation of TCR recognition of MHC class II/peptide by processed remote N- and carboxy-terminal epitope extensions. Hum. Immunol. 61 8 (2000) 753-763
20. Godkin A.J., Smith K.J., Willis A., et al. Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions. J. Immunol. 166 11 (2001) 6720-6727
21. Chang S., Ghosh D., Kirschner D., et al. Peptide length-based prediction of peptide-MHC class II binding. Bioinformatics 22 22 (2006) 2761-2767
22. http://www.jenner.ac.uk/AntiJen/
23. http://www.immuneepitope.org/
24. http://www.jenner.ac.uk/MHCPred
25. http://www.imtech.res.in/raghava/propred
26. http://SVRMHC.umn.edu/SVRMHC
27. http://tools.immuneepitope.org/tools/matrix/iedb_input?matrixClass=II
28. http://tools.immuneepitope.org/analyze/html/mhc_II_binding.html