A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction
Abstract Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unav...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-025-01506-1 |
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| _version_ | 1849737845557690368 |
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| author | Ming-Hsiu Wu Ziqian Xie Degui Zhi |
| author_facet | Ming-Hsiu Wu Ziqian Xie Degui Zhi |
| author_sort | Ming-Hsiu Wu |
| collection | DOAJ |
| description | Abstract Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction. |
| format | Article |
| id | doaj-art-90ba04d8a87544ea9400fc14519677a7 |
| institution | DOAJ |
| issn | 2399-3669 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Chemistry |
| spelling | doaj-art-90ba04d8a87544ea9400fc14519677a72025-08-20T03:06:48ZengNature PortfolioCommunications Chemistry2399-36692025-04-01811910.1038/s42004-025-01506-1A Folding-Docking-Affinity framework for protein-ligand binding affinity predictionMing-Hsiu Wu0Ziqian Xie1Degui Zhi2McWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonAbstract Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction.https://doi.org/10.1038/s42004-025-01506-1 |
| spellingShingle | Ming-Hsiu Wu Ziqian Xie Degui Zhi A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction Communications Chemistry |
| title | A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction |
| title_full | A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction |
| title_fullStr | A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction |
| title_full_unstemmed | A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction |
| title_short | A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction |
| title_sort | folding docking affinity framework for protein ligand binding affinity prediction |
| url | https://doi.org/10.1038/s42004-025-01506-1 |
| work_keys_str_mv | AT minghsiuwu afoldingdockingaffinityframeworkforproteinligandbindingaffinityprediction AT ziqianxie afoldingdockingaffinityframeworkforproteinligandbindingaffinityprediction AT deguizhi afoldingdockingaffinityframeworkforproteinligandbindingaffinityprediction AT minghsiuwu foldingdockingaffinityframeworkforproteinligandbindingaffinityprediction AT ziqianxie foldingdockingaffinityframeworkforproteinligandbindingaffinityprediction AT deguizhi foldingdockingaffinityframeworkforproteinligandbindingaffinityprediction |