Protein structure prediction via deep learning: an in-depth review
The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray cryst...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1498662/full |
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| author | Yajie Meng Zhuang Zhang Chang Zhou Xianfang Tang Xinrong Hu Geng Tian Jialiang Yang Yuhua Yao Yuhua Yao Yuhua Yao |
| author_facet | Yajie Meng Zhuang Zhang Chang Zhou Xianfang Tang Xinrong Hu Geng Tian Jialiang Yang Yuhua Yao Yuhua Yao Yuhua Yao |
| author_sort | Yajie Meng |
| collection | DOAJ |
| description | The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities. |
| format | Article |
| id | doaj-art-60c98d0c858d4ec5b68710ea2bc10d29 |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-60c98d0c858d4ec5b68710ea2bc10d292025-08-20T03:07:20ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-04-011610.3389/fphar.2025.14986621498662Protein structure prediction via deep learning: an in-depth reviewYajie Meng0Zhuang Zhang1Chang Zhou2Xianfang Tang3Xinrong Hu4Geng Tian5Jialiang Yang6Yuhua Yao7Yuhua Yao8Yuhua Yao9College of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, ChinaCollege of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, ChinaCollege of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, ChinaCollege of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, ChinaCollege of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, ChinaGeneis Beijing Co, Beijing, ChinaGeneis Beijing Co, Beijing, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaKey Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, ChinaKey Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, ChinaThe application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.https://www.frontiersin.org/articles/10.3389/fphar.2025.1498662/fullprotein structure predictiondeep learninglarge language modelprotein structure databasesevaluation index |
| spellingShingle | Yajie Meng Zhuang Zhang Chang Zhou Xianfang Tang Xinrong Hu Geng Tian Jialiang Yang Yuhua Yao Yuhua Yao Yuhua Yao Protein structure prediction via deep learning: an in-depth review Frontiers in Pharmacology protein structure prediction deep learning large language model protein structure databases evaluation index |
| title | Protein structure prediction via deep learning: an in-depth review |
| title_full | Protein structure prediction via deep learning: an in-depth review |
| title_fullStr | Protein structure prediction via deep learning: an in-depth review |
| title_full_unstemmed | Protein structure prediction via deep learning: an in-depth review |
| title_short | Protein structure prediction via deep learning: an in-depth review |
| title_sort | protein structure prediction via deep learning an in depth review |
| topic | protein structure prediction deep learning large language model protein structure databases evaluation index |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1498662/full |
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