BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.

ABSTRACT: B-cells get activated through interaction with B-cell epitopes, a specific portion of the antigen. Identification of B-cell epitopes is crucial for a wide range of clinical applications, including disease diagnostics, vaccine and antibody development, and immunotherapy. While experimental B-cell epitope identification is expensive and time-consuming, computational tools are starting to emerge that can generate lists of high-confidence epitopes for experimental trials. In this paper, we present BLMPred, a sequence-based linear B-cell epitope prediction tool, which exploits pre-trained protein language model embeddings for deriving local and global protein structural features from the protein primary structure. BLMPred is a binary classifier which can predict whether an input peptide sequenc

SUBMITTER: Das B 

PROVIDER: S-EPMC12795683 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature