Prediction of antibiotic resistance mechanisms using a protein language model.

ABSTRACT:

Motivation

Antibiotic resistance has emerged as a major global health threat, with an increasing number of bacterial infections becoming difficult to treat. Predicting the underlying resistance mechanisms of antibiotic resistance genes (ARGs) is crucial for understanding and combating this problem. However, existing methods struggle to accurately predict resistance mechanisms for ARGs with low similarity to known sequences and lack sufficient interpretability of the prediction models.

Results

In this study, we present a novel approach for predicting ARG resistance mechanisms using ProteinBERT, a protein language model (pLM) based on deep learning. Our method outperforms state-of-the-art techniques on diverse ARG datasets, including those with low homology to the training dat

SUBMITTER: Yagimoto K 

PROVIDER: S-EPMC11464418 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature