A mutation-induced drug resistance database (MdrDB)
Abstract Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. Howev...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-023-00920-7 |
| _version_ | 1797801489224171520 |
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| author | Ziyi Yang Zhaofeng Ye Jiezhong Qiu Rongjun Feng Danyu Li Changyu Hsieh Jonathan Allcock Shengyu Zhang |
| author_facet | Ziyi Yang Zhaofeng Ye Jiezhong Qiu Rongjun Feng Danyu Li Changyu Hsieh Jonathan Allcock Shengyu Zhang |
| author_sort | Ziyi Yang |
| collection | DOAJ |
| description | Abstract Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals. |
| first_indexed | 2024-03-13T04:52:12Z |
| format | Article |
| id | doaj.art-293238c6461d442eab5c3ca8a39fd10c |
| institution | Directory Open Access Journal |
| issn | 2399-3669 |
| language | English |
| last_indexed | 2024-03-13T04:52:12Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Chemistry |
| spelling | doaj.art-293238c6461d442eab5c3ca8a39fd10c2023-06-18T11:08:43ZengNature PortfolioCommunications Chemistry2399-36692023-06-01611910.1038/s42004-023-00920-7A mutation-induced drug resistance database (MdrDB)Ziyi Yang0Zhaofeng Ye1Jiezhong Qiu2Rongjun Feng3Danyu Li4Changyu Hsieh5Jonathan Allcock6Shengyu Zhang7Tencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryTencent Quantum LaboratoryAbstract Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.https://doi.org/10.1038/s42004-023-00920-7 |
| spellingShingle | Ziyi Yang Zhaofeng Ye Jiezhong Qiu Rongjun Feng Danyu Li Changyu Hsieh Jonathan Allcock Shengyu Zhang A mutation-induced drug resistance database (MdrDB) Communications Chemistry |
| title | A mutation-induced drug resistance database (MdrDB) |
| title_full | A mutation-induced drug resistance database (MdrDB) |
| title_fullStr | A mutation-induced drug resistance database (MdrDB) |
| title_full_unstemmed | A mutation-induced drug resistance database (MdrDB) |
| title_short | A mutation-induced drug resistance database (MdrDB) |
| title_sort | mutation induced drug resistance database mdrdb |
| url | https://doi.org/10.1038/s42004-023-00920-7 |
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