BepiPred-3.0: Improved B-cell epitope prediction using protein language models.

ABSTRACT: B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred-3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy

SUBMITTER: Clifford JN 

PROVIDER: S-EPMC9679979 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature