Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Abstract Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become availa...
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| Format: | Article |
| Language: | English |
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Springer Nature
2022-09-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.15252/msb.202211081 |
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| author | Felix Wong Aarti Krishnan Erica J Zheng Hannes Stärk Abigail L Manson Ashlee M Earl Tommi Jaakkola James J Collins |
| author_facet | Felix Wong Aarti Krishnan Erica J Zheng Hannes Stärk Abigail L Manson Ashlee M Earl Tommi Jaakkola James J Collins |
| author_sort | Felix Wong |
| collection | DOAJ |
| description | Abstract Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery. |
| format | Article |
| id | doaj-art-ad22b6add92747ceac4e104098c84d2d |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-ad22b6add92747ceac4e104098c84d2d2025-08-20T04:02:49ZengSpringer NatureMolecular Systems Biology1744-42922022-09-0118912010.15252/msb.202211081Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discoveryFelix Wong0Aarti Krishnan1Erica J Zheng2Hannes Stärk3Abigail L Manson4Ashlee M Earl5Tommi Jaakkola6James J Collins7Institute for Medical Engineering & Science, Massachusetts Institute of TechnologyInstitute for Medical Engineering & Science, Massachusetts Institute of TechnologyInfectious Disease and Microbiome Program, Broad Institute of MIT and HarvardComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyInfectious Disease and Microbiome Program, Broad Institute of MIT and HarvardInfectious Disease and Microbiome Program, Broad Institute of MIT and HarvardComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyInstitute for Medical Engineering & Science, Massachusetts Institute of TechnologyAbstract Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.https://doi.org/10.15252/msb.202211081AlphaFold2enzymatic activitymachine learningmolecular dockingprotein‐ligand interactions |
| spellingShingle | Felix Wong Aarti Krishnan Erica J Zheng Hannes Stärk Abigail L Manson Ashlee M Earl Tommi Jaakkola James J Collins Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery Molecular Systems Biology AlphaFold2 enzymatic activity machine learning molecular docking protein‐ligand interactions |
| title | Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery |
| title_full | Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery |
| title_fullStr | Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery |
| title_full_unstemmed | Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery |
| title_short | Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery |
| title_sort | benchmarking alphafold enabled molecular docking predictions for antibiotic discovery |
| topic | AlphaFold2 enzymatic activity machine learning molecular docking protein‐ligand interactions |
| url | https://doi.org/10.15252/msb.202211081 |
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