Epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

ABSTRACT: The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information. Our

SUBMITTER: da Silva BM 

PROVIDER: S-EPMC10199762 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature