LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction.

ABSTRACT: Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel generalizable deep learning framework, LM-GVP, composed of a protein Language Model (LM) and Graph Neural Network (GNN) to leverage information from both 1D amino acid sequences and 3D structures of proteins. Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and protein functions from Gene Ontology (GO). We also illustrated insights into how a GNN prediction head can inform the fine-tuning of protein LMs to better leverage structural information.

SUBMITTER: Wang Z 

PROVIDER: S-EPMC9046255 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature