Giziński S, Preibisch G, Kucharski P, Tyrolski M, Rembalski M, Grzegorczyk P, Gambin A. Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology.
Methods 2024;
224:1-9. [PMID:
38295891 DOI:
10.1016/j.ymeth.2024.01.016]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/30/2023] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
The Major Histocompatibility Complex (MHC) is a critical element of the vertebrate cellular immune system, responsible for presenting peptides derived from intracellular proteins. MHC-I presentation is pivotal in the immune response and holds considerable potential in the realms of vaccine development and cancer immunotherapy. This study delves into the limitations of current methods and benchmarks for MHC-I presentation. We introduce a novel benchmark designed to assess generalization properties and the reliability of models on unseen MHC molecules and peptides, with a focus on the Human Leukocyte Antigen (HLA)-a specific subset of MHC genes present in humans. Finally, we introduce HLABERT, a pretrained language model that outperforms previous methods significantly on our benchmark and establishes a new state-of-the-art on existing benchmarks.
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