EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks
Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained pro...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024004380 |
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| author | Md Hossain Shuvo Debswapna Bhattacharya |
| author_facet | Md Hossain Shuvo Debswapna Bhattacharya |
| author_sort | Md Hossain Shuvo |
| collection | DOAJ |
| description | Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. Our method estimates the quality of the protein-protein interface through an effective graph-based representation of interacting residue pairs, incorporating a diverse set of features, including ESM-2 embeddings, and then by learning the representation using symmetry-aware EGNNs. Our experimental results demonstrate improved ranking performance on diverse datasets over existing latest protein complex quality estimation methods including the top-performing CASP15 protein complex quality estimation method VoroIF_GNN and the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring and across different performance evaluation metrics. Additionally, our ablation studies demonstrate the contributions of both pLMs and the equivariant nature of EGNN for improved protein-protein interface quality estimation performance. EquiRank is freely available at https://github.com/mhshuvo1/EquiRank. |
| format | Article |
| id | doaj-art-efbd7b944ebb41299a90ad51f2b322dc |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-efbd7b944ebb41299a90ad51f2b322dc2025-01-04T04:56:15ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127160170EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networksMd Hossain Shuvo0Debswapna Bhattacharya1Department of Computer Science, Prairie View A&M University, Prairie View, 77446, TX, USADepartment of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA; Corresponding author.Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. Our method estimates the quality of the protein-protein interface through an effective graph-based representation of interacting residue pairs, incorporating a diverse set of features, including ESM-2 embeddings, and then by learning the representation using symmetry-aware EGNNs. Our experimental results demonstrate improved ranking performance on diverse datasets over existing latest protein complex quality estimation methods including the top-performing CASP15 protein complex quality estimation method VoroIF_GNN and the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring and across different performance evaluation metrics. Additionally, our ablation studies demonstrate the contributions of both pLMs and the equivariant nature of EGNN for improved protein-protein interface quality estimation performance. EquiRank is freely available at https://github.com/mhshuvo1/EquiRank.http://www.sciencedirect.com/science/article/pii/S2001037024004380Protein-protein interactionProtein complex quality estimationProtein language modelsGraph neural networksDeep learning |
| spellingShingle | Md Hossain Shuvo Debswapna Bhattacharya EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks Computational and Structural Biotechnology Journal Protein-protein interaction Protein complex quality estimation Protein language models Graph neural networks Deep learning |
| title | EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks |
| title_full | EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks |
| title_fullStr | EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks |
| title_full_unstemmed | EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks |
| title_short | EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks |
| title_sort | equirank improved protein protein interface quality estimation using protein language model informed equivariant graph neural networks |
| topic | Protein-protein interaction Protein complex quality estimation Protein language models Graph neural networks Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024004380 |
| work_keys_str_mv | AT mdhossainshuvo equirankimprovedproteinproteininterfacequalityestimationusingproteinlanguagemodelinformedequivariantgraphneuralnetworks AT debswapnabhattacharya equirankimprovedproteinproteininterfacequalityestimationusingproteinlanguagemodelinformedequivariantgraphneuralnetworks |