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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.14269 |
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| _version_ | 1866915398339788800 |
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| author | Li, Kunyang Lou, Hongfu Peng, Dinan |
| author_facet | Li, Kunyang Lou, Hongfu Peng, Dinan |
| contents | Membrane protein classification is a fundamental task in structural bioinformatics, critical to understanding protein functions and accelerating drug discovery. In this study, we propose MP-GCAN, a novel graph-based classification model that leverages both spatial and sequential features of proteins. MP-GCAN combines GCN, GAT, and GIN layers to capture hierarchical structural representations from 3D protein graphs, constructed from high-resolution PDB files with $α$-carbon coordinates and residue types. To evaluate performance, we curated a high-quality dataset of 500 membrane and 500 non-membrane proteins, and compared MP-GCAN with two baselines: a structure-confidence-based SGD classifier utilizing AlphaFold's pLDDT scores, and DeepTMHMM, a sequence-based deep learning model. Our experiments demonstrate that MP-GCAN significantly outperforms baselines, achieving an accuracy of 96% and strong F1-scores on both classes. The results highlight the importance of integrating pretrained GNN architectures with domain-specific structural data to enhance membrane protein classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MP-GCAN: a highly accurate classifier for $α$-helical membrane proteins and $β$-barrel proteins Li, Kunyang Lou, Hongfu Peng, Dinan Quantitative Methods Membrane protein classification is a fundamental task in structural bioinformatics, critical to understanding protein functions and accelerating drug discovery. In this study, we propose MP-GCAN, a novel graph-based classification model that leverages both spatial and sequential features of proteins. MP-GCAN combines GCN, GAT, and GIN layers to capture hierarchical structural representations from 3D protein graphs, constructed from high-resolution PDB files with $α$-carbon coordinates and residue types. To evaluate performance, we curated a high-quality dataset of 500 membrane and 500 non-membrane proteins, and compared MP-GCAN with two baselines: a structure-confidence-based SGD classifier utilizing AlphaFold's pLDDT scores, and DeepTMHMM, a sequence-based deep learning model. Our experiments demonstrate that MP-GCAN significantly outperforms baselines, achieving an accuracy of 96% and strong F1-scores on both classes. The results highlight the importance of integrating pretrained GNN architectures with domain-specific structural data to enhance membrane protein classification. |
| title | MP-GCAN: a highly accurate classifier for $α$-helical membrane proteins and $β$-barrel proteins |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2507.14269 |