Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Issue 5 (18th January 2021)

• Record Type: Journal Article
• Authors: Mei, Shutao; Li, Fuyi; Xiang, Dongxu; Ayala, Rochelle; Faridi, Pouya; Webb, Geoffrey I; Illing, Patricia T; Rossjohn, Jamie; Akutsu, Tatsuya; Croft, Nathan P; Purcell, Anthony W; Song, Jiangning
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-01-18
• Subjects: HLA-I peptide binding prediction -- machine learning -- scoring function -- web server -- model customisation; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa415
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8 + T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.