Vaccine Design pp 753-760 | Cite as

MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction

  • Yichang Xu
  • Cheng Luo
  • Hiroshi Mamitsuka
  • Shanfeng ZhuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1404)


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


MetaMHCpan MHC-I MHC-II Binding peptides TEPITOPEpan MHC2SKpan MHC2MIL 



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.


  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Guo L, Luo C, Zhu S (2013) MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction. BMC Genomics 14(Suppl 5):S11CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Salomon J, Flower DR (2006) Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinform 7:501CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Hu X, Mamitsuka H, Zhu S (2011) Ensemble approaches for improving HLA class I-peptide binding prediction. J Immunol Methods 374:47–52CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yichang Xu
    • 1
    • 2
  • Cheng Luo
    • 1
    • 2
  • Hiroshi Mamitsuka
    • 3
  • Shanfeng Zhu
    • 1
    • 2
    Email author
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  3. 3.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityKyotoJapan

Personalised recommendations