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, wh

SUBMITTER: Ouyang S 

PROVIDER: S-EPMC12609176 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature