MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models
Abstract 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 pro...
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BMC
2025-08-01
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| Online Access: | https://doi.org/10.1186/s12915-025-02356-y |
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| author | Feng Wang Jinming Chu Liyan Shen Shan Chang |
| author_facet | Feng Wang Jinming Chu Liyan Shen Shan Chang |
| author_sort | Feng Wang |
| collection | DOAJ |
| description | Abstract 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 information. Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. Moreover, we build seven independent graphs from the overall PPI network graph to specifically learn the features of each PPI type, thereby enhancing the model’s learning ability for different types of interactions. Conclusions Compared to the state-of-the-art methods, MESM achieved improvements of 8.77%, 4.98%, 7.48%, and 6.08% on SHS27k, SHS148k, SYS30k, and SYS60k, respectively. The experimental results demonstrate that MESM exhibits significant improvements in PPI prediction performance. |
| format | Article |
| id | doaj-art-3cb364d5a3314c80840f8dff71833bd9 |
| institution | Kabale University |
| issn | 1741-7007 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-3cb364d5a3314c80840f8dff71833bd92025-08-20T04:02:44ZengBMCBMC Biology1741-70072025-08-0123112410.1186/s12915-025-02356-yMESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language modelsFeng Wang0Jinming Chu1Liyan Shen2Shan Chang3School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou UniversitySchool of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou UniversitySchool of Computer Engineering, Suzhou Vocational UniversityInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of TechnologyAbstract 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 information. Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. Moreover, we build seven independent graphs from the overall PPI network graph to specifically learn the features of each PPI type, thereby enhancing the model’s learning ability for different types of interactions. Conclusions Compared to the state-of-the-art methods, MESM achieved improvements of 8.77%, 4.98%, 7.48%, and 6.08% on SHS27k, SHS148k, SYS30k, and SYS60k, respectively. The experimental results demonstrate that MESM exhibits significant improvements in PPI prediction performance.https://doi.org/10.1186/s12915-025-02356-yProtein–protein interactionMultimodal protein feature pre-trainingGraph neural network |
| spellingShingle | Feng Wang Jinming Chu Liyan Shen Shan Chang MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models BMC Biology Protein–protein interaction Multimodal protein feature pre-training Graph neural network |
| title | MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models |
| title_full | MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models |
| title_fullStr | MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models |
| title_full_unstemmed | MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models |
| title_short | MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models |
| title_sort | mesm integrating multi source data for high accuracy protein protein interactions prediction through multimodal language models |
| topic | Protein–protein interaction Multimodal protein feature pre-training Graph neural network |
| url | https://doi.org/10.1186/s12915-025-02356-y |
| work_keys_str_mv | AT fengwang mesmintegratingmultisourcedataforhighaccuracyproteinproteininteractionspredictionthroughmultimodallanguagemodels AT jinmingchu mesmintegratingmultisourcedataforhighaccuracyproteinproteininteractionspredictionthroughmultimodallanguagemodels AT liyanshen mesmintegratingmultisourcedataforhighaccuracyproteinproteininteractionspredictionthroughmultimodallanguagemodels AT shanchang mesmintegratingmultisourcedataforhighaccuracyproteinproteininteractionspredictionthroughmultimodallanguagemodels |