Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology

• Published in: Methods (San Diego, Calif.) Vol. 224; pp. 1 - 9
• Main Authors: Giziński, Stanisław, Preibisch, Grzegorz, Kucharski, Piotr, Tyrolski, Michał, Rembalski, Michał, Grzegorczyk, Piotr, Gambin, Anna
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-04-2024
• Subjects: Benchmark dataset; BERT; Humans; Major histocompatibility complex; Major Histocompatibility Complex - genetics; Peptide presentation prediction; Peptides - chemistry; Protein Binding; Proteins - metabolism; Reproducibility of Results; Benchmark dataset; BERT; Major histocompatibility complex; Peptide presentation prediction
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2024.01.016

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.
•Study reviews limitations in current methods and benchmarks for pan-specific MHC-I presentation prediction.•Novel pan-specific MHC-I prediction model rigorously benchmarked against existing approaches.•HLABERT excels on benchmarks, with enhanced generalization. Key contributions: limitations ID, new benchmark, high-performing model.