Journal article

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Shutao Mei, Fuyi Li, Andre Leier, Tatiana T Marquez-Lago, Kailin Giam, Nathan P Croft, Tatsuya Akutsu, A Ian Smith, Jian Li, Jamie Rossjohn, Anthony W Purcell, Jiangning Song

Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2020

Abstract

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of H..

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Grants

Awarded by National Health and Medical Research Council of Australia (NHMRC)


Awarded by Australian Research Council (ARC)


Awarded by Collaborative Research Program of Institute for Chemical Research, Kyoto University


Awarded by National Institute of Allergy and Infectious Diseases of the National Institutes of Health


Funding Acknowledgements

This work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC) (APP1085018, APP1127948, APP1144652 and APP1084283), the Australian Research Council (ARC) (DP120104460, DP150104503) and the Collaborative Research Program of Institute for Chemical Research, Kyoto University (201828). J.L., A.W.P. and J.N.S. are supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965). J.R. is an ARC Australia Laureate fellow. J.L. and A.W.P are NHMRC principal research fellows. T.T.M.L. and A.L.'s work was supported in part by the Informatics Institute of the School of Medicine at UAB.