PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models
Abstract Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06123-2 |
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| author | Jun Xie Youli Zhang Ziyang Wang Xiaocheng Jin Xiaoli Lu Shengxiang Ge Xiaoping Min |
| author_facet | Jun Xie Youli Zhang Ziyang Wang Xiaocheng Jin Xiaoli Lu Shengxiang Ge Xiaoping Min |
| author_sort | Jun Xie |
| collection | DOAJ |
| description | Abstract Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities. |
| format | Article |
| id | doaj-art-11532669cd364d68b60d8236b93ae5d0 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-11532669cd364d68b60d8236b93ae5d02025-08-20T02:55:32ZengBMCBMC Bioinformatics1471-21052025-04-0126111510.1186/s12859-025-06123-2PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer modelsJun Xie0Youli Zhang1Ziyang Wang2Xiaocheng Jin3Xiaoli Lu4Shengxiang Ge5Xiaoping Min6Institute of Artificial Intelligence, School of Informatic, Xiamen UniversityNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, School of Public Health, Xiamen UniversityInstitute of Artificial Intelligence, School of Informatic, Xiamen UniversityNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, School of Public Health, Xiamen UniversityInformation and Networking Center, Xiamen UniversityNational Institute of Diagnostics and Vaccine Development in Infectious Diseases, School of Public Health, Xiamen UniversityInstitute of Artificial Intelligence, School of Informatic, Xiamen UniversityAbstract Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities.https://doi.org/10.1186/s12859-025-06123-2Bind affinity predictionESMGraph transformer |
| spellingShingle | Jun Xie Youli Zhang Ziyang Wang Xiaocheng Jin Xiaoli Lu Shengxiang Ge Xiaoping Min PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models BMC Bioinformatics Bind affinity prediction ESM Graph transformer |
| title | PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models |
| title_full | PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models |
| title_fullStr | PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models |
| title_full_unstemmed | PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models |
| title_short | PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models |
| title_sort | ppi graphomer enhanced protein protein affinity prediction using pretrained and graph transformer models |
| topic | Bind affinity prediction ESM Graph transformer |
| url | https://doi.org/10.1186/s12859-025-06123-2 |
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