EurNet: Efficient Multi-Range Relational Modeling of Protein Structure

Minghao Xu, Yuanfan Guo, Yi Xu, Jian Tang, Xinlei Chen, Yuandong Tian

Keywords: Protein Structure Modeling, Multi-Relational Data Modeling, Protein Function Prediction

TL;DR: This work proposes the EurNet to efficiently model the multi-range relational interactions within protein structures.

Abstract: Modeling the 3D structures of proteins is critical for obtaining effective protein structure representations, which further boosts protein function understanding. Existing protein structure encoders mainly focus on modeling short-range interactions within protein structures, while they neglect modeling the interactions at multiple length scales that are actually complete interactive patterns in protein structures. To attain complete interaction modeling with efficient computation, we introduce the EurNet for Efficient multi-range relational modeling. In EurNet, we represent the protein structure as a multi-relational residue-level graph with different types of edges for modeling short-range, medium-range and long-range interactions. To efficiently process these different interactive relations, we propose a novel modeling layer, called Gated Relational Message Passing (GRMP), as the basic building block of EurNet. GRMP can capture multiple interactive relations in protein structures with little extra computational cost. We verify the state-of-the-art performance of EurNet on EC and GO protein function prediction benchmarks, and the proposed GRMP layer is proved to achieve better efficiency-performance trade-off than the widely-used relational graph convolution.