Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis.

Magdi E A Zaki,Sami A Al-Hussain,Sumit O Bajaj,Arabinda Ghosh,Israa Lewaa,Nahed N E El-Sayed,Siddhartha Akasapu,Vijay H Masand

doi:10.3390/ph14040357

Abstract

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Highlights

The current epidemic involving the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has completely disturbed the global health system and the world economy
The Quantitative Structure−Activity Relationship (QSAR) model was build using interpretable molecular descriptors to correlate them with structural features
In the present analysis, a straight evaluation of Ki values of the molecules of the dataset was performed to explain the effect of a specific descriptor, it is important to note that the combined or converse effect of unknown factors or other molecular descriptors could have a substantial effect on the Ki value of a molecule

Summary

Introduction

The current epidemic involving the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has completely disturbed the global health system and the world economy. The first patient of COVID-19 was reported in Wuhan, China, in December, 2019 [1], and the virus has reached. 215 countries in the world [2] In some countries, it is in stage one or stage two, but in some countries, it has reached stage three, i.e., community spread [2]. Due to its high spreading rate and mortality, WHO (World Health Organization) has declared it a Public Health. SARS-CoV-2 belongs to the family of Coronaviridae and the genus Betacoronavirus [3]. These are enveloped (enclosed) viruses having a single positive-stranded RNA genome

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Discussion

Conclusion