DPFunc: accurately predicting protein function via deep learning with domain-guided structure information
Abstract Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, making it difficult to understand the relati...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54816-8 |
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| _version_ | 1850075558707200000 |
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| author | Wenkang Wang Yunyan Shuai Min Zeng Wei Fan Min Li |
| author_facet | Wenkang Wang Yunyan Shuai Min Zeng Wei Fan Min Li |
| author_sort | Wenkang Wang |
| collection | DOAJ |
| description | Abstract Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, making it difficult to understand the relations between protein structures and functions. In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. DPFunc can detect significant regions in protein structures and accurately predict corresponding functions under the guidance of domain information. It outperforms current state-of-the-art methods and achieves a significant improvement over existing structure-based methods. Detailed analyses demonstrate that the guidance of domain information contributes to DPFunc for protein function prediction, enabling our method to detect key residues or regions in protein structures, which are closely related to their functions. In summary, DPFunc serves as an effective tool for large-scale protein function prediction, which pushes the border of protein understanding in biological systems. |
| format | Article |
| id | doaj-art-cf33922d5f7e4cd7b6690326c86eabf8 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-cf33922d5f7e4cd7b6690326c86eabf82025-08-20T02:46:16ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-024-54816-8DPFunc: accurately predicting protein function via deep learning with domain-guided structure informationWenkang Wang0Yunyan Shuai1Min Zeng2Wei Fan3Min Li4School of Computer Science and Engineering, Central South UniversitySchool of Computer Science and Engineering, Central South UniversitySchool of Computer Science and Engineering, Central South UniversityNuffield Department of Women’s and Reproductive Health, University of OxfordSchool of Computer Science and Engineering, Central South UniversityAbstract Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, making it difficult to understand the relations between protein structures and functions. In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. DPFunc can detect significant regions in protein structures and accurately predict corresponding functions under the guidance of domain information. It outperforms current state-of-the-art methods and achieves a significant improvement over existing structure-based methods. Detailed analyses demonstrate that the guidance of domain information contributes to DPFunc for protein function prediction, enabling our method to detect key residues or regions in protein structures, which are closely related to their functions. In summary, DPFunc serves as an effective tool for large-scale protein function prediction, which pushes the border of protein understanding in biological systems.https://doi.org/10.1038/s41467-024-54816-8 |
| spellingShingle | Wenkang Wang Yunyan Shuai Min Zeng Wei Fan Min Li DPFunc: accurately predicting protein function via deep learning with domain-guided structure information Nature Communications |
| title | DPFunc: accurately predicting protein function via deep learning with domain-guided structure information |
| title_full | DPFunc: accurately predicting protein function via deep learning with domain-guided structure information |
| title_fullStr | DPFunc: accurately predicting protein function via deep learning with domain-guided structure information |
| title_full_unstemmed | DPFunc: accurately predicting protein function via deep learning with domain-guided structure information |
| title_short | DPFunc: accurately predicting protein function via deep learning with domain-guided structure information |
| title_sort | dpfunc accurately predicting protein function via deep learning with domain guided structure information |
| url | https://doi.org/10.1038/s41467-024-54816-8 |
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