Identification of and Mechanistic Insights into SARS-CoV-2 Main Protease Non-Covalent Inhibitors: An In-Silico Study.

The indispensable role of the SARS-CoV-2 main protease (Mpro) in the viral replication cycle and its dissimilarity to human proteases make Mpro a promising drug target. In order to identify the non-covalent Mpro inhibitors, we performed a comprehensive study using a combined computational strategy. We first screened the ZINC purchasable compound database using the pharmacophore model generated from the reference crystal structure of Mpro complexed with the inhibitor ML188. The hit compounds were then filtered by molecular docking and predicted parameters of drug-likeness and pharmacokinetics. The final molecular dynamics (MD) simulations identified three effective candidate inhibitors (ECIs) capable of maintaining binding within the substrate-binding cavity of Mpro. We further performed comparative analyses of the reference and effective complexes in terms of dynamics, thermodynamics, binding free energy (BFE), and interaction energies and modes. The results reveal that, when compared to the inter-molecular electrostatic forces/interactions, the inter-molecular van der Waals (vdW) forces/interactions are far more important in maintaining the association and determining the high affinity. Given the un-favorable effects of the inter-molecular electrostatic interactions-association destabilization by the competitive hydrogen bond (HB) interactions and the reduced binding affinity arising from the un-compensable increase in the electrostatic desolvation penalty-we suggest that enhancing the inter-molecular vdW interactions while avoiding introducing the deeply buried HBs may be a promising strategy in future inhibitor optimization.

Artificial Intelligence Derived Artemisinin Drug Compound Acts as an Effective Candidate against SARS-CoV-2 Receptors: An In-Silico Study to Combat COVID-19

Background: Global outburst of coronavirus has challenged the whole world to discover drugs to combat the current pandemic. Repurposing drugs is a promising approach as it provides new openings to challenge the emerging COVID-19. However, in the epoch of big data, artificial intelligence (AI) technology offers to leverage computational methods for finding new candidate drugs through an In-silico approach. Aim and Objectives: The aim and objectives of our present work basically are the designing of a plant-derived compound against the COVID-19 receptors which might act as effective therapy along with predicting the outcome of the disease with a deep learning program language that is python (anaconda) 2.7 version. Methodology: Artificial Intelligence technology helps in understanding the interactions of coronavirus with receptors through the computer-aided drug designing process (CADD). The ligand-protein interactions were prepared with the Maestro (Schrödinger) program which aids to study the docking pose of artemisinin compound with SARS-CoV-2 receptors like 7CTT, a nonstructural protein (NSP) and 7MY3 Spike glycoprotein. Thus, Artificial Intelligence technology examines the drug-target interaction with Neural Networking built with a deep learning machine algorithm and predicts the outcome of the disease with python program language. Results: Artemisinin exhibited the highest antiviral activity against the SARS-CoV-2 receptors like 7CTT and 7MY3. The three-dimensional structures of the ligands and SARS-CoV-2 receptors were retrieved from the PubChem Open Chemistry Database. The ligand-protein interactions were performed with the help of the Maestro (Schrödinger) program, which revealed MM/GBSA values of 7CTT interaction with derivative ligands of antimalarial compounds such as D95 (-45.424), artemisinin (-35.222), MPD (-31,021), MRD (-21.952) and 6FGC (-34.089), whereas with 7MY3 spike glycoprotein interactions MMGBSA values for D95 (-26.304), MPD(-18.658), MRD(-28.03) and 6FGC (-13.47) binding affinities have followed Lipinski rule of 5 and further predicted the outcome with random forest decision tree with an accuracy of about 75% with python program. Conclusion: Repurposing of the drug through an In-silico approach against the SARS-CoV-2 virus revealed its antiviral actions. The docking studies approach has shown the XP score, gliding energy, and MMGBSA values which were predicted with a deep learning program built with Artificial Intelligence technology. © 2022,Jordan Journal of Biological Sciences. All Rights Reserved.

In silico investigation of oxindole alkaloids from Uncaria perrottetii and Uncaria lanosa f. philippinensis for COVID-19 therapy: Binding site prediction and molecular docking

On January 31, 2020, WHO declared the global outbreak of novel Coronavirus as a public health emergency of international concern. The biological origin of COVID-19 is caused by three primary pathophysiological conditions: immunosuppression, viral infection, and inflammation. In the Philippines, nine alkaloids with potential antiviral and anti-inflammatory effects have been isolated from two plants, Uncaria perrottetii and Uncaria lanosa f. philippinensis. The binding site of A2AR was proven to be pocket 0, which is similar to the literature. Two drug candidates showed the best result for molecular docking: mitraphylline and rauniticine-allo-oxindole A for A2AR and 3CLpro receptors. Mitraphylline candidate showed the lowest free energy scores and RMSD scores. This study is extended to other in silico tests to prove the impact of the alkaloid against COVID-19. © 2022 ACM.

COVID-19: an In Silico Analysis on Potential Therapeutic Uses of Trikadu as Immune System Boosters.

Corona virus pandemic outbreak also known as COVID-19 has created an imbalance in this world. Scientists have adopted the use of natural or alternative medicines which are consumed mostly as dietary supplements to boost the immune system as herbal remedies. India is famous for traditional medicinal formulations which includes 'Trikadu'-a combination of three acrids, namely Zingiber officinale, Piper nigrum and Piper longum which have antioxidant properties that boost our immune system hence acting as a strong preventive measure. In this study, AutoDock 4.0 was used to study interaction between the phytocompounds of Trikadu with RNA-dependent polymerase protein and enveloped protein of the SARS-CoV-2 virus. Analysis of the results showed that coumarin, coumaperine and bisdemethoxycurcumin showed strong bonding interactions with both the proteins. We can conclude that Trikadu has the potential molecules; hence, it can be incorporated in the diet to boost the immune system as a preventive measure against the virus.

Structure-based virtual screening, molecular dynamics and binding affinity calculations of some potential phytocompounds against SARS-CoV-2.

COVID-19 caused by a positive-sense single stranded RNA virus named as severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) triggered the global pandemic. This virus has infected about 10.37 Crores and taken lives of 2.24 Crores people of 213 countries to date. To cope-up this emergency clinical trials are undergoing with some existing drugs like remdesivir, flavipiravir, lopinavir-ritonavir, nafamostat, doxycycline, hydroxy-chloroquine, dexamethasone, etc., despite their severe toxicity and health hazards among diabetics, hypertensive, cardiac patients or normal individuals. The lack of safe and approved treatment for COVID-19 has forced the scientific community to find novel and safe compounds with potential efficacy. This study evaluates a few selective herbal compounds like glucoraphanin, vitexin, niazinin, etc., as a potential inhibitor of the spike protein and 3-chymotrypsin-like protease (3CLpro) or main protease (Mpro) of SARS-COV-2 through in-silico virtual studies such as molecular docking, target analysis, toxicity prediction and ADME prediction and supported by a Molecular-Dynamic simulation. Selective phytocompounds were docked successfully in the binding site of spike glycoprotein and 3CLpro (Mpro) of SARS-CoV-2. In-silico approaches also predict this molecule to have good solubility, pharmacodynamic property and target accuracy through MD simulation and ADME studies. These hit molecules niazinin, vitexin, glucoraphanin also obey Lipinski's rule along with their stable binding towards target protein of the virus, which makes them suitable for further biochemical and cell-based assays followed by clinical investigations to highlight their potential use in COVID-19 treatment.Communicated by Ramaswamy H. Sarma.