DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.

ABSTRACT:

Motivation

Allostery, the process by which binding at one site perturbs a distant site, is being rendered as a key focus in the field of drug development with its substantial impact on protein function. The identification of allosteric pockets (sites) is a challenging task and several techniques have been developed, including Machine Learning to predict allosteric pockets that utilize both static and pocket features.

Results

Our work, DeepAllo, is the first study that combines fine-tuned protein language model (pLM) with FPocket features and shows an increase in prediction performance of allosteric sites over previous studies. The pLM model was fine-tuned on AlloSteric Database (ASD) in Multitask Learning setting and was further used as a feature extractor to train XGBoost a

SUBMITTER: Khokhar M 

PROVIDER: S-EPMC12145174 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature