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Article

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

by
Candida Manelfi
1,
Jonas Gossen
2,3,
Silvia Gervasoni
4,
Carmine Talarico
1,
Simone Albani
2,3,
Benjamin Joseph Philipp
2,3,
Francesco Musiani
5,
Giulio Vistoli
4,
Giulia Rossetti
2,6,7,
Andrea Rosario Beccari
1 and
Alessandro Pedretti
4,*
1
Dompé Farmaceutici SpA, Via Campo di Pile, 67100 L’Aquila, Italy
2
Computational Biomedicine, Institute for Neuroscience and Medicine (INM-9) and Institute for Advanced Simulations (IAS-5), Forschungszentrum Jülich, 52425 Jülich, Germany
3
Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52062 Aachen, Germany
4
Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy
5
Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, 40127 Bologna, Italy
6
Jülich Supercomputing Center (JSC), Forschungszentrum Jülich, 52425 Jülich, Germany
7
Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital Aachen, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Molecules 2021, 26(4), 797; https://doi.org/10.3390/molecules26040797
Submission received: 13 November 2020 / Revised: 20 January 2021 / Accepted: 26 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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Abstract

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.
Keywords: SARS-CoV-2; 3CL-Pro; antivirals; virtual screening; docking simulations; drug repurposing; consensus models; binding space; isomeric space SARS-CoV-2; 3CL-Pro; antivirals; virtual screening; docking simulations; drug repurposing; consensus models; binding space; isomeric space

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MDPI and ACS Style

Manelfi, C.; Gossen, J.; Gervasoni, S.; Talarico, C.; Albani, S.; Philipp, B.J.; Musiani, F.; Vistoli, G.; Rossetti, G.; Beccari, A.R.; et al. Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease. Molecules 2021, 26, 797. https://doi.org/10.3390/molecules26040797

AMA Style

Manelfi C, Gossen J, Gervasoni S, Talarico C, Albani S, Philipp BJ, Musiani F, Vistoli G, Rossetti G, Beccari AR, et al. Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease. Molecules. 2021; 26(4):797. https://doi.org/10.3390/molecules26040797

Chicago/Turabian Style

Manelfi, Candida, Jonas Gossen, Silvia Gervasoni, Carmine Talarico, Simone Albani, Benjamin Joseph Philipp, Francesco Musiani, Giulio Vistoli, Giulia Rossetti, Andrea Rosario Beccari, and et al. 2021. "Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease" Molecules 26, no. 4: 797. https://doi.org/10.3390/molecules26040797

APA Style

Manelfi, C., Gossen, J., Gervasoni, S., Talarico, C., Albani, S., Philipp, B. J., Musiani, F., Vistoli, G., Rossetti, G., Beccari, A. R., & Pedretti, A. (2021). Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease. Molecules, 26(4), 797. https://doi.org/10.3390/molecules26040797

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