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Wang Z, Belecciu T, Eaves J, Reimers M, Bachmann MH, Woldring D. Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction. J Biomol Struct Dyn 2022:1-21. [PMID: 35993534 DOI: 10.1080/07391102.2022.2112976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
The COVID-19 pandemic has resulted in millions of deaths around the world. Multiple vaccines are in use, but there are many underserved locations that do not have adequate access to them. Variants may emerge that are highly resistant to existing vaccines, and therefore cheap and readily obtainable therapeutics are needed. Phytochemicals, or plant chemicals, can possibly be such therapeutics. Phytochemicals can be used in a polypharmacological approach, where multiple viral proteins are inhibited and escape mutations are made less likely. Finding the right phytochemicals for viral protein inhibition is challenging, but in-silico screening methods can make this a more tractable problem. In this study, we screen a wide range of natural drug products against a comprehensive set of SARS-CoV-2 proteins using a high-resolution computational workflow. This workflow consists of a structure-based virtual screening (SBVS), where an initial phytochemical library was docked against all selected protein structures. Subsequently, ligand-based virtual screening (LBVS) was employed, where chemical features of 34 lead compounds obtained from the SBVS were used to predict 53 lead compounds from a larger phytochemical library via supervised learning. A computational docking validation of the 53 predicted leads obtained from LBVS revealed that 28 of them elicit strong binding interactions with SARS-CoV-2 proteins. Thus, the inclusion of LBVS resulted in a 4-fold increase in the lead discovery rate. Of the total 62 leads, 18 showed promising pharmacokinetic properties in a computational ADME screening. Collectively, this study demonstrates the advantage of incorporating machine learning elements into a virtual screening workflow.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zirui Wang
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
| | - Theodore Belecciu
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
| | - Joelle Eaves
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
| | - Mark Reimers
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
| | - Michael H Bachmann
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA
| | - Daniel Woldring
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
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