This server provides an integrated tool for MHC peptide binding prediction. MetaMHCI is for MHC-I and MetaMHCII is for MHC-II. Single prediction methods including TEPITOPEpan, MHC2SKpan, MHC2MIL, LApan are used for integration[9]. An average method combines them together and may perform better[8].

Major Histocompatibility Complex (MHC) molecules play important roles in adaptive immune response.[10] An important function of MHC molecules is to bind peptide fragments derived from pathogens and to display them on the cell surface for being recognized by the appropriate T cells [1]. This stimulates subsequent immune response in order to fight against these pathogens. The MHC gene family is mainly divided into two subgroups: class I (MHC-I) and class II (MHC-II).

Computational methods are used to quickly select a small number of promising peptides for further biochemical experimental verification. They can be divided into two groups: allele-specific and pan-specific methods [2]. In the allele-specific methods, both training and test peptides are from the same MHC molecule. In contrast, pan-specific methods can predict the binding peptides of MHC molecules that have very few or even no training data. According to underlying techniques used, the allele-specific methods can be roughly divided into four groups: position specific score matrix (PSSM) based methods, artificial neural network (ANN) based methods, kernel based methods, and multiple instance learning based methods.

TEPITOPEpan [3] is a PSSM method developed by extrapolating from the binding speci?cities of HLA-DR molecules characterized by TEPITOPE [4] to those uncharacterized.

MHC2SKpan [5] is a kernel based method. The string kernel MHC2SK(MHC-II String Kernel) used by it measures the similarities among peptides with variable lengths. LApan is a method we newly develop by extend the LA [6]. LA is a allele-specific and kernel based (local alignment kernel) method. LApan is a pan-specific method.

MHC2MIL [7] is a multiple instance learning based method by considering peptide flanking region and residue positions. It is an allele-specific method and now we provide 35 alleles for prediction.


  1. Janeway J CA, Travers P, Walport M, et al Immunobiology: The Immune System in Health and Disease. Garland Science Publishing, New York., 5 edition 2001.
  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(3):350-64.
  3. 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):e30483.
  4. Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti M P, Sinigaglia F, Hammer J. 1999. Generation of tissue-specific and promiscuous HLA ligand database using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol. 17:555-561.
  5. Guo L, Luo C, Zhu S. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction[J]. BMC genomics, 2013, 14(Suppl 5): S11.
  6. Salomon J, Flower D R. Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores[J]. BMC bioinformatics, 2006, 7(1): 501.
  7. Xu Y, Luo C, Qian M, et al. MHC2MIL: a novel multiple instance learning based method for MHC II peptide binding prediction by considering peptide flanking region and residue positions[J].BMC Genomics, 2014.
  8. Hu X, Mamitsuka H, Zhu S. Ensemble approaches for improving HLA Class I-peptide binding prediction[J]. Journal of immunological methods, 2011, 374(1): 47-52.
  9. Hu X, Zhou W, Udaka K, et al. MetaMHC: a meta approach to predict peptides binding to MHC molecules[J]. Nucleic acids research, 2010: gkq407.
  10. Zhu S, Udaka K, Sidney J, et al. Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules[J]. Bioinformatics, 2006, 22(13): 1648-1655.

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