Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info.
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This work has been partially supported by National Natural Science Foundation of China (Grant No.: 61170097), and Scientific Research Starting Foundation for Returned Overseas Chinese Scholars, Ministry of Education, China.
Janeway JCA, Travers P, Walport M et al (2001) Immunobiology: the immune system in health and disease, 5th edn. Garland Science Publishing, New YorkGoogle Scholar
Zhang L, Udaka K, Mamitsuka H, Zhu S (2012) Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Brief Bioinform 13:350–364CrossRefPubMedGoogle Scholar
Zhu S, Udaka K, Sidney J, Sette A, Aoki-Kinoshita KF, Mamitsuka H (2006) Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules. Bioinformatics 22:1648–1655CrossRefPubMedGoogle Scholar
Salomon J, Flower DR (2006) Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinform 7:501CrossRefGoogle Scholar
Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S (2012) TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One 7(2), e30483CrossRefPubMedPubMedCentralGoogle Scholar
Xu Y, Luo C, Qian M, Huang X, Zhu S (2014) MHC2MIL: a novel multiple instance learning based method for MHC II peptide binding prediction by considering peptide flanking region and residue positions. BMC Genomics 15(Suppl 9):S9CrossRefPubMedPubMedCentralGoogle Scholar
Peters B, Bui HH, Frankild S, Nielsen M et al (2006) A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2(6), e65CrossRefPubMedPubMedCentralGoogle Scholar
Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, Nielsen M (2013) NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics 65:711–724CrossRefPubMedGoogle Scholar
Sturniolo T, Bono E, Ding J, Raddrizzani L et al (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561CrossRefPubMedGoogle Scholar
Hu X, Zhou W, Udaka K, Mamitsuka H, Zhu S (2010) MetaMHC: a meta approach to predict peptides binding to MHC molecules. Nucleic Acids Res 38W:474–479CrossRefGoogle Scholar
Hu X, Mamitsuka H, Zhu S (2011) Ensemble approaches for improving HLA class I-peptide binding prediction. J Immunol Methods 374:47–52CrossRefPubMedGoogle Scholar