Using deep learning and large protein language models to predict protein-membrane interfaces of peripheral membrane proteins.

ABSTRACT:

Motivation

Characterizing interactions at the protein-membrane interface is crucial as abnormal peripheral protein-membrane attachment is involved in the onset of many diseases. However, a limiting factor in studying and understanding protein-membrane interactions is that the membrane-binding domains of peripheral membrane proteins (PMPs) are typically unknown. By applying artificial intelligence techniques in the context of natural language processing (NLP), the accuracy and prediction time for protein-membrane interface analysis can be significantly improved compared to existing methods. Here, we assess whether NLP and protein language models (pLMs) can be used to predict membrane-interacting amino acids for PMPs.

Results

We utilize available experimental data and generate

SUBMITTER: Paranou D 

PROVIDER: S-EPMC11572487 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature