PatchProt: hydrophobic patch prediction using protein foundation models.

ABSTRACT:

Motivation

Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods.

Results

In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently develop

SUBMITTER: Gogishvili D 

PROVIDER: S-EPMC11525051 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature