Trans-allelic model for prediction of peptide:MHC-II interactions

Abdoelnaser M. Degoot, Faraimunashe Chirove, Wilfred Ndifon

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4+ T cells mount an immune in mounting response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide-MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide-MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide-MHC interactions.

Original languageEnglish
Article number1410
JournalFrontiers in Immunology
Issue numberJUN
Publication statusPublished - 20 Jun 2018
Externally publishedYes


  • Antigen presentation
  • Inverse statistical mechanics
  • Machine learning
  • Major histocompatibility complex (MHC)
  • Modeling peptide-MHC-II interactions

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology


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