Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network
Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limit...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2022-01-01
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| Series: | Research |
| Online Access: | http://dx.doi.org/10.34133/2022/9873564 |
| _version_ | 1797282630881771520 |
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| author | Hongyan Du Dejun Jiang Junbo Gao Xujun Zhang Lingxiao Jiang Yundian Zeng Zhenxing Wu Chao Shen Lei Xu Dongsheng Cao Tingjun Hou Peichen Pan |
| author_facet | Hongyan Du Dejun Jiang Junbo Gao Xujun Zhang Lingxiao Jiang Yundian Zeng Zhenxing Wu Chao Shen Lei Xu Dongsheng Cao Tingjun Hou Peichen Pan |
| author_sort | Hongyan Du |
| collection | DOAJ |
| description | Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website. |
| first_indexed | 2024-03-07T17:15:52Z |
| format | Article |
| id | doaj.art-7a8c83e7711949bfab022b4c7ee5d545 |
| institution | Directory Open Access Journal |
| issn | 2639-5274 |
| language | English |
| last_indexed | 2024-03-07T17:15:52Z |
| publishDate | 2022-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj.art-7a8c83e7711949bfab022b4c7ee5d5452024-03-02T22:19:32ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742022-01-01202210.34133/2022/9873564Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning NetworkHongyan Du0Dejun Jiang1Junbo Gao2Xujun Zhang3Lingxiao Jiang4Yundian Zeng5Zhenxing Wu6Chao Shen7Lei Xu8Dongsheng Cao9Tingjun Hou10Peichen Pan11Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInstitute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, ChinaXiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410004 Hunan, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China; State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, ChinaCovalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.http://dx.doi.org/10.34133/2022/9873564 |
| spellingShingle | Hongyan Du Dejun Jiang Junbo Gao Xujun Zhang Lingxiao Jiang Yundian Zeng Zhenxing Wu Chao Shen Lei Xu Dongsheng Cao Tingjun Hou Peichen Pan Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network Research |
| title | Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network |
| title_full | Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network |
| title_fullStr | Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network |
| title_full_unstemmed | Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network |
| title_short | Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network |
| title_sort | proteome wide profiling of the covalent druggable cysteines with a structure based deep graph learning network |
| url | http://dx.doi.org/10.34133/2022/9873564 |
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