Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks

2025-02-22

AI Summary

Predicting protein metal-binding sites and associated metal types remains challenging due to insufficient modeling of evolutionary coevolutionary patterns. To address this, we propose the first graph neural network (GNN) method that jointly integrates the full coevolutionary residue network—derived from multiple sequence alignments—with 3D structural dependencies. Specifically, we explicitly represent the coevolutionary network as a graph and fuse it with a protein structure graph, enabling the GNN to simultaneously capture long-range evolutionary couplings and spatial interactions. Furthermore, we formulate metal-binding residue prediction as a multi-label classification task, jointly predicting both binding residues and their cognate metal types. Our method achieves significant improvements over existing coevolution-based approaches on public benchmarks and matches the performance of state-of-the-art sequence-based models. The implementation is publicly available.

Technology Category

Machine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityGame Theory and Economic Paradigms: Cooperative Game Theory

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsWeb Mining and Content Analysis: Models for Web evolutionSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure-based prediction approaches cannot capture the complex evolutionary relationships driving these interactions to facilitate understanding, while recent co-evolution-based approaches do not fully consider the entire structure of the co-evolved residue network. In this paper, we introduce MBGNN (Metal-Binding Graph Neural Network) that utilizes the entire co-evolved residue network and effectively captures the complex dependencies within protein structures via graph neural networks to enhance the prediction of co-evolved metal-binding residues and their associated metal types. Experimental results on a public dataset show that MBGNN outperforms existing co-evolution-based metal-binding prediction methods, and it is also competitive against recent sequence-based methods, showing the potential of integrating co-evolutionary insights with advanced machine learning to deepen our understanding of protein-metal interactions. The MBGNN code is publicly available at https://github.com/SRastegari/MBGNN.

Problem

• Predicting metal-binding sites in proteins
• Identifying associated metal types
• Enhancing co-evolution-based predictions with GNNs

Innovation

• Graph Neural Networks
• Co-evolved Residue Network
• Metal-Binding Prediction