Advancing virulence factor prediction using protein language models.

ABSTRACT:

Background

Bacterial infections rank as the second leading cause of death globally, with virulence factors (VFs) being crucial to their pathogenicity. Predicting VFs accurately can uncover mechanisms of bacterial diseases and suggest new treatments. Current machine learning (ML) methods face challenges, such as outdated feature extraction, simplistic forecasting frameworks, and lack of differentiation between gram-positive (G +) and gram-negative (G -) bacteria.

Results

In this study, we introduced pLM4VF, a predictive framework that utilized ESM protein language models to extract VF characteristics of G + and G - bacteria separately, and further integrated the models using the stacking strategy. Extensive benchmarking experiments on the independent test demonstrated that pL

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12522538 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature