RoBep: a region-oriented deep learning model for B-cell epitope prediction.

ABSTRACT:

Motivation

Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent protein language models and structure-aware methods can capture spatial information of tertiary structure when generating residue embeddings, most existing epitope predictors use these embeddings to perform classification for individual residues one by one, without enforcing spatial continuity for reported epitope residues. Such methods often result in biologically implausible predictions because B-cell epitope residues always cluster together on the antigen surface.

Results

We present RoBep, a region-oriented B-cell epitope predictor that explicitly models the spatial clustering of epitope residues. RoBep introduces

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12848824 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature