Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings.

ABSTRACT: Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The mo

SUBMITTER: Liu F 

PROVIDER: S-EPMC10828400 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature