MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models

作者：Feng Wang, J.Y. Chu, Liyan Shen, Shan Chang · 发表于：BMC Biology · 年份：2025 · DOI：10.1186/s12915-025-02356-y · 被引用次数：3 · 研究领域：Bioinformatics and Genomic Networks、Biomedical Text Mining and Ontologies、Machine Learning in Bioinformatics

BACKGROUND: Protein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation. However, current methods mainly focus on extracting features from protein sequences and using graph neural network (GNN) to acquire interaction information from the PPI network graph. This limits the model's ability to learn richer and more effective interaction information, thereby affecting prediction performance. RESULTS: In this study, we propose a novel deep learning method, MESM, for effectively predicting PPI. The datasets used for the PPI prediction task were primarily constructed from the STRING database, including two Homo sapiens PPI datasets, SHS27k and SHS148k, and two Saccharomyces cerevisiae PPI datasets, SYS30k and SYS60k. MESM consists of three key modules, as follows: First, MESM extracts multimodal representations from protein sequence information, protein structure information, and point cloud features through Sequence Variational Autoencoder (SVAE), Variational Graph Autoencoder (VGAE), and PointNet Autoencoder (PAE). Then, Fusion Autoencoder (FAE) is used to integrate these multimodal features, generating rich and balanced protein representations. Next, MESM leverages GraphGPS to learn structural information from the PPI network graph structure and combines Graph Attention Network (GAT) to further capture protein interaction informatio...