Hierarchical graph transformer with contrastive learning for protein function prediction.

ABSTRACT:

Motivation

In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process. Graph neural networks have shown significant progress in protein research, but capturing long-distance structural correlations and identifying key residues in protein graphs remains challenging.

Results

In the present study, we propose a novel deep learning model named Hierarchical graph transformEr with contrAstive Learning (HEAL) for protein function prediction. The core feature of HEAL is its ability to capture structural semantics using a hierarchical graph Transfo

SUBMITTER: Gu Z 

PROVIDER: S-EPMC10338137 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature