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Structural Prediction of Peptide–MHC Binding Modes

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Computational Peptide Science

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2405))

Abstract

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

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Acknowledgments

This work was supported by the University of Lausanne—Department of Oncology UNIL-CHUV, the Ludwig Institute for Cancer Research—Lausanne Branch, the SIB Swiss Institute of Bioinformatics, SNSF grants to VZ (#205321_192019, CRSII5_193749 and CRSK-3_190400) and OM (#31003A_176168), and funds from Research for Life to OM.

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  1. Marta A.S. Perez and Michel A. Cuendet contributed equally to this work.

Authors and Affiliations

  1. Computer-aided Molecular Engineering Group, Department of Oncology UNIL-CHUV, Lausanne University, Lausanne, Switzerland

    Marta A. S. Perez & Vincent Zoete

  2. Ludwig Institute for Cancer Research, Lausanne, Switzerland

    Marta A. S. Perez & Vincent Zoete

  3. Molecular Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Marta A. S. Perez, Michel A. Cuendet, Ute F. Röhrig, Olivier Michielin & Vincent Zoete

  4. Oncology Department, Centre Hospitalier Universitaire Vaudois (CHUV), Precision Oncology Center, Lausanne, Switzerland

    Michel A. Cuendet & Olivier Michielin

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  1. Marta A. S. Perez
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  3. Ute F. Röhrig
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  4. Olivier Michielin
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  5. Vincent Zoete
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Correspondence to Olivier Michielin or Vincent Zoete .

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  1. Lab de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France

    Thomas Simonson

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Perez, M.A.S., Cuendet, M.A., Röhrig, U.F., Michielin, O., Zoete, V. (2022). Structural Prediction of Peptide–MHC Binding Modes. In: Simonson, T. (eds) Computational Peptide Science. Methods in Molecular Biology, vol 2405. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1855-4_13

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  • DOI: https://doi.org/10.1007/978-1-0716-1855-4_13

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