PMID- 31204427 OWN - NLM STAT- MEDLINE DCOM- 20210923 LR - 20210923 IS - 1477-4054 (Electronic) IS - 1467-5463 (Print) IS - 1467-5463 (Linking) VI - 21 IP - 4 DP - 2020 Jul 15 TI - A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. PG - 1119-1135 LID - 10.1093/bib/bbz051 [doi] AB - 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 HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future. CI - (c) The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. FAU - Mei, Shutao AU - Mei S AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. FAU - Li, Fuyi AU - Li F AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. FAU - Leier, Andre AU - Leier A AD - Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA. FAU - Marquez-Lago, Tatiana T AU - Marquez-Lago TT AD - Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA. FAU - Giam, Kailin AU - Giam K AD - Department of Immunology, King's College London, London, UK. FAU - Croft, Nathan P AU - Croft NP AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. FAU - Akutsu, Tatsuya AU - Akutsu T AD - Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Kyoto, Japan. FAU - Smith, A Ian AU - Smith AI AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. AD - ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia. FAU - Li, Jian AU - Li J AD - Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia. FAU - Rossjohn, Jamie AU - Rossjohn J AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. AD - ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia. FAU - Purcell, Anthony W AU - Purcell AW AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. FAU - Song, Jiangning AU - Song J AD - Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia. AD - ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia. AD - Monash Centre for Data Science, Monash University, Melbourne, VIC, Australia. LA - eng GR - R01 AI111965/AI/NIAID NIH HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't PT - Review PL - England TA - Brief Bioinform JT - Briefings in bioinformatics JID - 100912837 RN - 0 (Histocompatibility Antigens Class I) SB - IM MH - Algorithms MH - Computational Biology/*methods MH - Datasets as Topic MH - Histocompatibility Antigens Class I/chemistry/*metabolism MH - Humans MH - Machine Learning MH - Reproducibility of Results PMC - PMC7373177 OTO - NOTNLM OT - HLA OT - bioinformatics OT - machine learning OT - peptide binding OT - performance benchmarking OT - prediction model OT - sequence analysis OT - web server EDAT- 2019/06/18 06:00 MHDA- 2021/09/24 06:00 PMCR- 2021/07/01 CRDT- 2019/06/18 06:00 PHST- 2019/02/11 00:00 [received] PHST- 2019/04/02 00:00 [revised] PHST- 2019/04/03 00:00 [accepted] PHST- 2019/06/18 06:00 [pubmed] PHST- 2021/09/24 06:00 [medline] PHST- 2019/06/18 06:00 [entrez] PHST- 2021/07/01 00:00 [pmc-release] AID - 5511798 [pii] AID - bbz051 [pii] AID - 10.1093/bib/bbz051 [doi] PST - ppublish SO - Brief Bioinform. 2020 Jul 15;21(4):1119-1135. doi: 10.1093/bib/bbz051.