Preprint Article Version 1 This version is not peer-reviewed

Revealing SARS-CoV-2 Functional Druggability Through Multi-Target Cadd Screening of Repurposable Drugs

Version 1 : Received: 9 May 2020 / Approved: 11 May 2020 / Online: 11 May 2020 (13:14:18 CEST)

How to cite: Gupta, Y.; Maciorowski, D.; Mathur, R.; Pearce, C.M.; Ilc, D.J.; Husein, H.; Bharti, A.; Becker, D.; Brijesh, R.; Bradfute, S.B.; Durvasula, R.; Kempaiah, P. Revealing SARS-CoV-2 Functional Druggability Through Multi-Target Cadd Screening of Repurposable Drugs. Preprints 2020, 2020050199 (doi: 10.20944/preprints202005.0199.v1). Gupta, Y.; Maciorowski, D.; Mathur, R.; Pearce, C.M.; Ilc, D.J.; Husein, H.; Bharti, A.; Becker, D.; Brijesh, R.; Bradfute, S.B.; Durvasula, R.; Kempaiah, P. Revealing SARS-CoV-2 Functional Druggability Through Multi-Target Cadd Screening of Repurposable Drugs. Preprints 2020, 2020050199 (doi: 10.20944/preprints202005.0199.v1).

Abstract

The emergence of SARS/MERS drug resistant COVID-19 with high transmission and mortality has recently been declared a deadly pandemic causing economic chaos and significant health problems. Like all coronaviruses, SARS-CoV-2 is a large virus that has many druggable components within its proteome. In this study, we focused on repurposing approved and investigational drugs by identifying potential drugs that are predicted to effectively inhibit critical enzymes within SARS-CoV-2. We shortlisted seven target proteins with enzymatic activities known to be essential at different stages of the virus life cycle. For virtual screening, the energy minimization of a crystal structure or modeled protein was carried out using Protein Preparation Wizard (Schrödinger LLC, 2020-1). Following active site selection based on data mining and COACH predictions, we performed a high-throughput virtual screen of drugs (n=5903) that are already approved by worldwide regulatory bodies including the FDA, using the ZINC database. Screening was performed against viral targets using three sequential docking modes (i.e. HTVS, SP and XP). Our in-silico virtual screening identified ~290 potential drugs based on the criteria of energy, docking parameters, ligand and binding site strain and score. Drugs specific to each target protein were further analyzed for binding free energy perturbation by molecular mechanics (prime MM-GBSA) and pruning the hits to the top 32 candidates. A top lead from each target group was further subjected to molecular dynamics simulation (MDS) using the Desmond module to validate the efficacy of the screening pipeline. All of the simulated hit-target complexes were predicted to strongly interact and with highly stable binding. Thus, we have identified a number of approved and investigational drugs with high likelihood of inhibiting a variety of key SARS-CoV-2 proteins. Follow-up studies will continue to identify inhibitors suitable for combination therapy based on drug-drug synergy to thwart resistance. In addition, the screening hits that we have identified provide excellent probes for understanding the binding properties of the active sites of all seven targets, further enabling us to derive consensus molecules through computer-aided drug design (CADD). While infections are expanding at a rampant pace, it must be recognized that resistance will grow commensurately through either genetic shift and/or genetic drift to all small molecule drugs identified. Vaccines should provide a more permanent solution through prevention, but resistivity is still a possible scenario. Nevertheless, a persistent multi-target drug development program is essential to curb this ongoing pandemic and to keep reemergence in check.

Subject Areas

SARS-CoV-2; COVID-19; CADD; virtual screening; approved drugs; drug repurposing; essential targets; molecular docking; molecular dynamics simulations

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