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 sequence is an antibody epitope or not without relying on 3D protein structures. BLMPred has been shown to outperform other comparable tools when tested on multiple independent datasets. It is freely available at https://github.com/bdbarnalidas/BLMPred.git.