A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design

نویسندگان

چکیده

Human leukocyte antigen (HLA) can recognize and bind foreign peptides to present them specialized immune cells, then initiate an response. Computational prediction of the peptide HLA (pHLA) binding speed up immunogenic screening facilitate vaccine design. However, there is a lack automatic program optimize mutated with higher affinity target allele. Here, fill this gap, we develop TransMut framework—composed TransPHLA for pHLA automatically optimized (AOMP) program—which be generalized any mutation task biomolecules. First, developed by constructing transformer-based model predict binding, which superior 14 previous methods on neoantigen human papilloma virus identification. For design, AOMP exploiting attention scores generated allele high homology source peptide. The proposed framework may generate potential vaccines experimentalists. complex plays important role in building response, but it hard will it. Chu et al. approach identify have HLA, that also translated other problems.

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ژورنال

عنوان ژورنال: Nature Machine Intelligence

سال: 2022

ISSN: ['2522-5839']

DOI: https://doi.org/10.1038/s42256-022-00459-7