Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion

TL;DR

This paper introduces MAPred, a novel multi-modal, multi-scale autoregressive model that integrates sequence and structural data to predict enzyme functions hierarchically, outperforming existing methods.

Contribution

The paper presents MAPred, a new model that combines sequence and 3D structural data with an autoregressive approach for hierarchical enzyme function prediction.

Findings

Outperforms existing models on benchmark datasets.

Effectively captures hierarchical EC number structure.

Integrates multi-modal data for improved accuracy.

Abstract

Accurate prediction of enzyme function is crucial for elucidating biological mechanisms and driving innovation across various sectors. Existing deep learning methods tend to rely solely on either sequence data or structural data and predict the EC number as a whole, neglecting the intrinsic hierarchical structure of EC numbers. To address these limitations, we introduce MAPred, a novel multi-modality and multi-scale model designed to autoregressively predict the EC number of proteins. MAPred integrates both the primary amino acid sequence and the 3D tokens of proteins, employing a dual-pathway approach to capture comprehensive protein characteristics and essential local functional sites. Additionally, MAPred utilizes an autoregressive prediction network to sequentially predict the digits of the EC number, leveraging the hierarchical organization of EC classifications. Evaluations on benchmark datasets, including New-392, Price, and New-815, demonstrate that our method outperforms existing models, marking a significant advance in the reliability and granularity of protein function prediction within bioinformatics.