Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

Emmanuel Israel Edache; Adamu Uzairu; Paul Andrew Mamza; Gideon Adamu Shallangwa; Fatma Hilal Yagin; Nagwan Abdel Samee; Noha F Mahmoud

doi:10.3389/fmolb.2023.1254230

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

Front Mol Biosci. 2023 Sep 1:10:1254230. doi: 10.3389/fmolb.2023.1254230. eCollection 2023.

Authors

Emmanuel Israel Edache¹, Adamu Uzairu², Paul Andrew Mamza², Gideon Adamu Shallangwa², Fatma Hilal Yagin³, Nagwan Abdel Samee⁴, Noha F Mahmoud⁵

Affiliations

¹ Department of Pure and Applied Chemistry, University of Maiduguri, Maiduguri, Nigeria.
² Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria.
³ Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
⁴ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁵ Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Abstract

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo^∧2 = 0.5548, Q_lto^∧2 = 0.5278, R^∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo^∧2 = 0.7033, Q_lto^∧2 = 0.6827, Q_lmo^∧2 = 0.6305, R^∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Keywords: CoMFA; MD simulations; SARS-CoV-2; autoimmune disorder; docking; rheumatoid arthritis; type 1 diabetes.

Grants and funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP 2023R206), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.