A scalable equivariant graph network framework for precise protein function prediction.

ABSTRACT:

Background

Protein function research helps in understanding the complex biological processes that occur within cells. However, the intricate nature of protein structures and functions, along with the rapid growth of protein sequence data, presents a pressing challenge to develop efficient computational methods for accurate protein annotation.

Results

In this study, we propose ENGINE, a multi-channel deep learning framework designed for robust protein function prediction. ENGINE integrates an equivariant graph convolutional network model to capture geometric features from protein 3D structures, leverages the large language model ESM-C to encode evolutionary and sequence-derived information, and combines an innovative 3D sequence representation that unifies spatial and sequent

SUBMITTER: Ran Z 

PROVIDER: S-EPMC12665208 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature