Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/26/9/2581 |
| _version_ | 1797535860358381568 |
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| author | Matthew L. Hudson Ram Samudrala |
| author_facet | Matthew L. Hudson Ram Samudrala |
| author_sort | Matthew L. Hudson |
| collection | DOAJ |
| description | Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform. |
| first_indexed | 2024-03-10T11:51:20Z |
| format | Article |
| id | doaj.art-9c451eae53224814b81fdd765ee7bf94 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T11:51:20Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-9c451eae53224814b81fdd765ee7bf942023-11-21T17:40:11ZengMDPI AGMolecules1420-30492021-04-01269258110.3390/molecules26092581Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO PlatformMatthew L. Hudson0Ram Samudrala1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USADepartment of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USADrug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.https://www.mdpi.com/1420-3049/26/9/2581drug repurposingvirtual screeningmultiscalemultitargetingpolypharmacologycomputational biology |
| spellingShingle | Matthew L. Hudson Ram Samudrala Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform Molecules drug repurposing virtual screening multiscale multitargeting polypharmacology computational biology |
| title | Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform |
| title_full | Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform |
| title_fullStr | Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform |
| title_full_unstemmed | Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform |
| title_short | Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform |
| title_sort | multiscale virtual screening optimization for shotgun drug repurposing using the cando platform |
| topic | drug repurposing virtual screening multiscale multitargeting polypharmacology computational biology |
| url | https://www.mdpi.com/1420-3049/26/9/2581 |
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