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Screening of World Approved Drugs against Highly Dynamical Spike Glycoprotein SARS-CoV-2 using CaverDock and Machine Learning

preprint
submitted on 30.10.2020, 13:55 and posted on 02.11.2020, 05:05 by Gaspar Pinto, Ondrej Vavra, Sérgio M. Marques, Jiri Filipovic, David Bednar, Jiri Damborsky
The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulations. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer’s three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate molecules. This new protocol for rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) required a total of 26,148 calculations and showed high robustness. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community

Funding

Czech Ministry of Education (02.1.01/0.0/0.0/18_046/0015975, CZ.02.1.01/0.0/0.0/16_026/0008451)

Grant Agency of the Czech Republic (20-15915Y)

European Union (857560, 720776 and 814418)

Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140)

History

Email Address of Submitting Author

josegasparpinto@gmail.com

Institution

International Clinical Research Center St. Anne's University Hospital in Brno

Country

Czech Republic

ORCID For Submitting Author

0000-0002-8917-1169

Declaration of Conflict of Interest

The authors declare no conflict of interests.

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