ProteinF3S: Boosting Enzyme Function Prediction by Fusing Protein Sequence, Structure, and Surface

Mingzhi Yuan *, Ao Shen *, Yingfan Ma, Jie Du, Bohan An, Manning Wang†

Briefings in Bioinformatics (IF:6.8)

Abstract

Proteins can be represented in different data forms, including sequence, structure, and surface, each of which has unique advantages and certain limitations. It is promising to fuse the complementary information among them. In this work, we propose a framework called ProteinF3S for enzyme function prediction that fuses the complementary information across protein sequence, structure, and surface. To achieve more effective fusion, we propose a multi-scale bidirectional fusion strategy between protein structure and surface, in which the hierarchical features of a surface encoder and a structure encoder interact with each other bidirectionally. Based on these interactions, more distinctive features can be obtained. After that, we achieve further fusion by concatenating the sequence features with the features containing structure and surface information, so that better performance can be achieved. To validate our method, we conduct extensive experiments on tasks including enzyme reaction classification and enzyme commission number prediction. Our method achieves new state-of-the-art performance and shows that fusing different forms of data is effective in enzyme function prediction.