Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods. (May 2023)

• Record Type: Journal Article
• Authors: Hossain, Alomgir; Rahman, Md Ekhtiar; Rahman, Md Siddiqur; Nasirujjaman, Khondokar; Matin, Mohammad Nurul; Faruqe, Md Omar; Rabbee, Muhammad Fazle
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: Deep learning -- Catechin gallate -- Main protease -- Molecular docking -- SARS-CoV-2; ADMET Absorption, distribution, metabolism, excretion and toxicity -- COVID-19 Coronavirus disease-2019 -- SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 -- Mpro Main protease -- RNN Recurrent Neural Networks -- RMSD Root mean square deviation -- RMSF Root mean square fluctuation -- RG Radius of gyration -- SASA Solvent accessible surface area -- MD Molecular Dynamics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106785
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (M pro ) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of M pro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus M pro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from −8.0 to −8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against M pro . Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients. Graphical abstract: Image 1 Highlights: The SARS-CoV-2 main protease (M pro ) was used for drug target for its polyprotein processing role in translation. Phytochemical library preserved 2431 phytochemicals from medicinal plants exhibited medicinal and antioxidant properties. Molecular docking, deep learning, and ADMET tools predicted 12 compounds as a potential inhibitor against target protein. Catechin gallate or its derivatives was identified as best possible drug candidate to treat SARS-CoV-2 infection.