M3Site: multiclass multimodal learning for protein active site identification and classification.

Abstract

Accurately identifying and classifying protein active sites is crucial for understanding protein mechanisms, drug design, and synthetic biology. Current methods often rely on binary classification and single-modal data, limiting their scope. To address these limitations, we propose M$^{3}$Site, a multimodal framework that integrates protein sequence embeddings, structural graph representations, and functional text annotations for residue-level, multiclass active site prediction. Built upon a curated dataset of 25 883 proteins sourced from UniProt and AlphaFold2, M$^{3}$Site leverages pretrained protein language models, equivariant graph neural networks, and biomedical language models for feature extraction. The function informed cross-attention module enables cross-modal feature fusion, while the adaptive weighted fusion mechanism balances modality contributions. A compound loss function tackles class imbalance, ensuring robust performance. Experimental results show M$^{3}$Site significantly outperforms existing models, and an interactive application has been developed to enhance its practical utility for predictions and visualizations. The dataset, source code for experiments, and interactive application are publicly available at https://github.com/Gift-OYS/M3Site.