Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.

ABSTRACT:

Motivation

Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

Results

We developed TransFun - a method using a transformer-based protein language model and 3D-equiva

SUBMITTER: Boadu F 

PROVIDER: S-EPMC9882282 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature