In silico molecular docking and molecular dynamic simulation of potential inhibitors of 3C-like main proteinase (3CLpro) from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using selected african medicinal plants

Abstract

The Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) is an infectious virus that causes mild to severe life-threatening upper respiratory tract infection. The virus emerged in Wuhan, China in 2019, and later spread across the globe. Its genome has been completely sequenced and based on the genomic information, the virus possessed 3C-Like Main Protease (3CLpro), an essential multifunctional enzyme that plays a vital role in the replication and transcription of the virus by cleaving polyprotein at eleven various sites to produce different non-structural proteins. This makes the protein an important target for drug design and discovery. Herein, we analyzed the interaction between the 3CLpro and potential inhibitory compounds identified from the extracts of Zingiber offinale and Anacardium occidentale using in silico docking and Molecular Dynamics (MD) Simulation. The crystal structure of SARS-CoV-2 main protease in complex with 02J (5-Methylisoxazole-3-carboxylic acid) and PEJ (composite ligand) (PDB Code: 6LU7, 2.16 Å) retrieved from Protein Data Bank (PDB) and subject to structure optimization and energy minimization. A total of twenty-nine compounds were obtained from the extracts of Z. offinale and the leaves of A. occidentale. These compounds were screened for physicochemical (Lipinski rule of five, Veber rule, and Egan filter), Pan-Assay Interference Structure, and pharmacokinetic properties to determine the Pharmaceutical Active Ingredients. Of the 29 compounds, only nineteen (19) possessed drug-likeness properties with efficient oral bioavailability and less toxicity. These compounds subjected to molecular docking analysis to determine their binding energies with the 3CLpro. The result of the analysis indicated that the free binding energies of the compounds ranged between − 5.08 and − 10.24 kcal/mol, better than the binding energies of 02j (− 4.10 kcal/mol) and PJE (− 5.07 kcal/ mol). Six compounds (CID_99615 = − 10.24 kcal/mol, CID_3981360 = 9.75 kcal/mol, CID_9910474 = − 9.14 kcal/mol, CID_11697907 = − 9.10 kcal/mol, CID_10503282 = − 9.09 kcal/mol and CID_620012 = − 8.53 kcal/mol) with good binding energies further selected and subjected to MD Simulation to determine the stability of the protein-ligand complex. The results of the analysis indicated that all the ligands form stable complexes with the protein, although, CID_9910474 and CID_10503282 had a better stability when compared to other selected phytochemicals (CID_99615, CID_3981360, CID_620012, and CID_11697907).

Metabolic profiling, ADME pharmacokinetics, molecular docking studies and antibacterial potential of Phyllantus muellerianus leaves

“…The grid box was set at 74 Å × 78 Å × 56 Å and with an exhaustiveness of 8. The free binding energy (∆G bind) was calculated using the sum of van der Waals energy (∆G vdw), the sum of electrostatic energy (∆G elect), the sum of hydrogen bond and desolvation energy (∆G hbond), the sum of final total internal energy (∆G conform), the sum of torsional free energy (∆G tor) and the sum unbound system energy (∆G solv) (Isa et al 2020). The compounds were then ranked by their binding affinity scores.…”

Metabolic Profiling, ADME Pharmacokinetics, Molecular Docking Studies and Antibacterial Potential of Phyllanthus muellerianus Leaves

“…The grid box was set at 74 Å × 78 Å × 56 Å and with an exhaustiveness of 8. The free binding energy (∆G bind ) was calculated using the sum of van der Waals energy (∆G vdw ), the sum of electrostatic energy (∆G elect ), the sum of hydrogen bond and desolvation energy (∆G hbond ), the sum of final total internal energy (∆G conform ), the sum of torsional free energy (∆G tor ) and the sum unbound system energy (∆G solv ) [23]. The compounds were then ranked by their binding affinity scores.…”

Cheminformatics and Pharmacological Network Analysis for the Prediction of Molecular Mechanism of Herbal Drug Ligands

“…There is a huge demand for newly discovered phytochemicals for performing preclinical and epidemiological studies to identify their molecular signatures [8]. There is a fair possibility of identi cation of more potent herbal analogs for curing arthritis, diabetes, and cancer through the application of in silico approach [9]. Although a myriad of literature is available on target-based approach and speci c target sites were investigated for the different ligands which did not pave the way for exploring new ligands and their scope for drug repurposing is left behind.…”

“…The present discerns that the least investigated and explored aspects of in silico drug designing through innovatively and curatively fabricating drug-like small molecules. Further, the speci c molecular docking studies comprising interaction analysis (protein-ligand), replica exchange molecular dynamics (REMD), MM-GBSA, and APBS electro statistics which is validated by Ramachandran torsion plots, this approach may be applied to calculate forces, energies, and binding a nity [9]. These scienti c efforts will not only improve the biological e cacy of the current drug but also provide a roadmap for innovative drug development or a fair potential for drug reuse after pathways are revealed at the genomic level [10].…”

“…Herbal-derived ligands and targeted therapeutic approaches may be successfully worked on using in silico approaches [7]. The ligands (bioactive component, scaffold analog) of Cyperus rotundus L. were evaluated to nd its molecular activities at different target sites by maintaining a curative space for drug discovery, drug reuse[8], development, advancement, and innovative results [9]. These efforts include an in silico approach to cheminformatics [10], and proteogenomic analysis [11] including physicochemical analysis, pharmacokinetics, pharmacodynamics, medicinal chemistry, drug similarities, designing of novel scaffolding analog, target predictions, molecular docking, identi cation of genes, genomic interactions, cellular signaling, and their biological insights (top ten diseases/ disorders regulated by these interactive genes via identifying the target sites of ligands) through representative overexpression analysis (ORA) [12], and Network Topology-based Analysis (NTA) [13].…”