scholarly journals Potential inhibitors of SARS-cov-2 RNA dependent RNA polymerase protein: molecular docking, molecular dynamics simulations and MM-PBSA analyses

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Zouhair Elkarhat ◽  
Hicham Charoute ◽  
Lamiae Elkhattabi ◽  
Abdelhamid Barakat ◽  
Hassan Rouba
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Rna Polymerase ◽  
Molecular Dynamics Simulations ◽  
Rna Dependent Rna Polymerase ◽  
Dynamics Simulations ◽  
Potential Inhibitors

scholarly journals Identification of Potential inhibitors for Hematopoietic Prostaglandin D2 Synthase: Computational Modeling and Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Satyajit Beura ◽  
Prabhakar Chetti
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
Binding Free Energy ◽  
Pharmacophore Model ◽  
Prostaglandin D2 ◽  
Prostaglandin D2 Synthase ◽  
Dynamics Simulations ◽  
Potential Inhibitors

To design a new therapeutic agent for Hematopoietic Prostaglandin D2 synthase (hPGDS), a set of 60 molecules with different molecular scaffolds were (range of pIC50 values are from 8.301 to 3.932) considered to create a pharmacophore model. Further, identification of potential hPGDS inhibitors were carried out by using virtual screening with different databases (from 15,74,182 molecules). The Molecular screening was performed using different sequential methods right from Pharmacophore based virtual screening, molecular docking, MM-GBSAstudies, ADME property analysis and molecular dynamics simulations using Maestro11.9 software. Based on the best pharmacophore model (ADRR_1), the resultant set of 18,492 molecules were screened. The preliminarily screened molecules were subjected to molecular docking (PDB_ID: 2CVD) methods. A set of 27 molecules was screened from the resultant molecular docking outcomes (360 molecules) based on binding free energy (ΔGbind) and Lipinskis rule of five. Out of 27 molecules, 4 were selected visual data analysis and further subjected to molecular dynamics (MD) simulation study. Outcomes of the present study conclude with three new proposed molecules (SP1, SP2 and SP10) which show a good range of interaction with human hPGDS enzyme in comparison to the marketed compounds i.e., HQL-79, TFC-007, HPGDS inhibitor I and TAS-204.

scholarly journals Binding Mechanism of Remdesivir to SARS-CoV-2 RNA Dependent RNA Polymerase

2020 ◽  
Author(s):  
Leili Zhang ◽  
Ruhong Zhou
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Rna Polymerase ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
World Health ◽  
Inhibition Mechanism ◽  
Rna Dependent Rna Polymerase ◽  
Dynamics Simulations ◽  
Key Residues

Starting from December 2019, coronavirus disease 2019 (COVID-19) has emerged as a once-in-a-century pandemic with deadly consequences, which urgently calls for new treatments, cures and supporting apparatuses. Remdesivir was reported by World Health Organization (WHO) as the most promising drug currently available for the treatment of COVID-19. Here, we use molecular dynamics simulations and free energy perturbation methods to study the inhibition mechanism of remdesivir to its target SARS-CoV-2 virus RNA-dependent RNA polymerase (RdRp). In the absence of a crystal structure of the SARS-CoV-2 RdRp, we first construct the homology model of this polymerase based on a previously available structure of SARS-CoV NSP12 RdRp (with a sequence identify of 95.8%). We then build the putative binding mode by aligning the remdesivir + RdRp complex to the ATP bound poliovirus RdRp. The putative binding structure is further optimized with molecular dynamics simulations and demonstrated to be stable, indicating a reasonable binding mode for remdesivir. The relative binding free energy of remdesivir is calculated to be -8.28 ± 0.65 kcal/mol, much stronger than the natural substrate ATP (-4.14 ± 0.89 kcal/mol) which is needed for the polymerization. The ~800-fold improvement in the Kd from remdesivir over ATP indicates an effective replacement of APT in blocking of the RdRp binding pocket. Key residues D618, S549 and R555 are found to be the contributors to the binding affinity of remdesivir. These findings demonstrate that remdesivir can potentially act as a SARS-CoV-2 RNA-chain terminator, effectively stopping its RNA reproduction, with key residues also identified for future lead optimization and/or drug resistance studies.

Promising inhibitors of nsp2 of CHIKV using molecular docking and temperature-dependent molecular dynamics simulations

2021 ◽  
pp. 1-9
Author(s):  
Mahendera Kumar Meena ◽  
Durgesh Kumar ◽  
Kamlesh Kumari ◽  
Nagendra Kumar Kaushik ◽  
Rammapa Venkatesh Kumar ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulations ◽  
Temperature Dependent ◽  
Dynamics Simulations

scholarly journals In silico chemical profiling and identification of neuromodulators from Curcuma amada targeting Acetylcholinesterase

2020 ◽  
Author(s):  
Md. Chayan Ali ◽  
Yeasmin Akter Munni ◽  
Raju Das ◽  
Marium sultana ◽  
Nasrin Akter ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulations ◽  
In Silico ◽  
Network Pharmacology ◽  
Chemical Profiling ◽  
Ache Inhibitors ◽  
Curcuma Amada ◽  
In Silico Admet ◽  
Dynamics Simulations

AbstractCurcuma amada or Mango ginger, a member of the Zingiberaceae family, has been revealed as a beneficiary medicinal plant having diverse pharmacological activities against a wide range of diseases. Due to having neuromodulation properties of this plant, the present study characterized the secondary metabolites of Curcuma amada for their drug-likeness properties, identified potent hits by targeting Acetylcholinesterase (AChE) and revealed neuromodulatory potentiality by network pharmacology approaches. Here in silico ADMET analysis was performed for chemical profiling, and molecular docking and molecular dynamics simulations were used to hit selection and binding characterizations. Accordingly, ADMET prediction showed that around 87.59% of compounds processed drug-likeness activity, where four compounds have been screened out by molecular docking. Guided from induced-fit docking, molecular dynamics simulations revealed phytosterol and curcumin derivatives as the most favorable AChE inhibitors with the highest binding energy, as resulted from MM-PBSA analysis. Furthermore, all of the four hits were appeared to modulate several signaling molecules and intrinsic cellular pathways in network pharmacology analysis, which are associated with neuronal growth survival, inflammation, and immune response, supporting their capacity to revert the condition of neuro-pathobiology. Together, the present in silico based characterization and system pharmacology based findings demonstrate Curcuma amada, as a great source of neuromodulating compounds, which brings about new development for complementary and alternative medicine for the prevention and treatment of neurodegenerative disorders.

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