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

Molecules, vol. 26, pp. 797

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.

Authors 11

1. Candida Manelfi

   Dompé (Italy)

   Affiliation as printed

   Dompé Farmaceutici SpA, Via Campo di Pile, 67100 L’Aquila, Italy

2. Jonas Goßen Aachen Faculty of Mathematics, Computer Science and Natural Sciences

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute for Neuroscience and Medicine (INM-9) and Institute for Advanced Simulations (IAS-5), Forschungszentrum Jülich, 52425 Jülich, Germany

   Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52062 Aachen, Germany

3. Silvia I. Gervasoni

   University of Milan

   Affiliation as printed

   Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy

4. Carmine Talarico

   Dompé (Italy)

   Affiliation as printed

   Dompé Farmaceutici SpA, Via Campo di Pile, 67100 L’Aquila, Italy

5. Simone Albani Aachen Faculty of Mathematics, Computer Science and Natural Sciences

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute for Neuroscience and Medicine (INM-9) and Institute for Advanced Simulations (IAS-5), Forschungszentrum Jülich, 52425 Jülich, Germany

   Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52062 Aachen, Germany

6. Benjamin Philipp Joseph Aachen Faculty of Mathematics, Computer Science and Natural Sciences

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute for Neuroscience and Medicine (INM-9) and Institute for Advanced Simulations (IAS-5), Forschungszentrum Jülich, 52425 Jülich, Germany

   Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, 52062 Aachen, Germany

7. Francesco Musiani

   University of Bologna

   Affiliation as printed

   Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, 40127 Bologna, Italy

8. Giulio Vistoli

   University of Milan

   Affiliation as printed

   Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy

9. Giulia Rossetti Aachen Department of Hematology, Oncology

   RWTH Aachen University · Universitätsklinikum Aachen · Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Computational Biomedicine, Institute for Neuroscience and Medicine (INM-9) and Institute for Advanced Simulations (IAS-5), Forschungszentrum Jülich, 52425 Jülich, Germany

   Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital Aachen, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany

   Jülich Supercomputing Center (JSC), Forschungszentrum Jülich, 52425 Jülich, Germany

10. Andrea R. Beccari

    Dompé (Italy)

    Affiliation as printed

    Dompé Farmaceutici SpA, Via Campo di Pile, 67100 L’Aquila, Italy

11. Alessandro Pedretti corresponding

    University of Milan

    Affiliation as printed

    Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli, 25, I-20133 Milano, Italy