A probabilistic meta-predictor for the MHC class II binding peptides
- 124 Downloads
Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.
KeywordsMHC class II binding Epitope prediction Meta-predictor PM predictor
This research is supported in part by the NIH under Grant 1 R03 AI069391-01.
- Altiparmak F, Akalin A, Ferhatosmanoglu H (2006) Predicting the binding affinity of MHC class II peptides. In: Computational Systems Bioinformatics: Proceedings of the Conference CSB, pp 331–334Google Scholar
- Flower DR (2004) Vaccines in silico—the growth and power of immunoinformatics. The Biochemist 26:17–20Google Scholar
- Flower DR, Doytchinova IA, Paine KPT, Blythe MJ, Lamponi D, Zygouri C, Guan P, McSparron H, Kirkbride H (2002) Computational vaccine design. In: Flower DR (ed) Drug design: cutting edge approaches. RSC, London, pp 136–180Google Scholar
- Hattotuwagama CK, Toseland CP, Guan P, Taylor DJ, Hemsley SL, Doytchinova IA, Flower DR (2006) Toward prediction of class II mouse major histocompatibility complex peptide binding Affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique. J Chem Inf Model 46:1491–1502PubMedCrossRefGoogle Scholar
- Huang L, Karpenko O, Murugan N, Dai Y (2006) A meta-predictor for MHC class II binding peptides based on naive Bayesian approach. In: Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS)Google Scholar
- Huang L, Karpenko O, Murugan N, Dai Y (2007) Building a meta-predictor for MHC class II-binding peptides. In: Flower DR (ed) Immunoinformatics: predicting immunogenicity in silico. Humana, Totowa, NJ, pp 355–364Google Scholar
- Parham P (2005) The immune system. Garland Science, New York, NYGoogle Scholar
- Peters B, Sidney J, Bourne P, Bui H-H, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger SP, Stewart S, Surko P, Way S, Wilson S, Sette A (2005) The design and implementation of the immune epitope database and analysis resource. Immunogenetics 57:326PubMedCrossRefGoogle Scholar
- Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561PubMedCrossRefGoogle Scholar