Predicting enzymatic function of protein sequences with attention.

ABSTRACT:

Motivation

There is a growing number of available protein sequences, but only a limited amount has been manually annotated. For example, only 0.25% of all entries of UniProtKB are reviewed by human annotators. Further developing automatic tools to infer protein function from sequence alone can alleviate part of this gap. In this article, we investigate the potential of Transformer deep neural networks on a specific case of functional sequence annotation: the prediction of enzymatic classes.

Results

We show that our EnzBert transformer models, trained to predict Enzyme Commission (EC) numbers by specialization of a protein language model, outperforms state-of-the-art tools for monofunctional enzyme class prediction based on sequences only. Accuracy is improved from 84% to 95%

SUBMITTER: Buton N 

PROVIDER: S-EPMC10612403 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature