scholarly journals Virtual screening of curcumin analogues as DYRK2 inhibitor: Pharmacophore analysis, molecular docking and dynamics, and ADME prediction

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 394
Author(s):  
La Ode Aman ◽  
Rahmana Emran Kartasasmita ◽  
Daryono Hadi Tjahjono
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Pharmacophore Model ◽  
Binding Pocket ◽  
26S Proteasome ◽  
Dynamics Simulation ◽  
Pharmacophore Modelling ◽  
Curcumin Analogues ◽  
Adme Prediction ◽  
Pharmacokinetic Properties

Background: Curcumin reduces the proliferation of cancer cells through inhibition of the DYRK2 enzyme, which is a positive regulator of the 26S proteasome. Methods: In the present work, curcumin analogues have been screened from the MolPort database using a pharmacophore model that comprised a ligand-based approach. The result of the screening was then evaluated by molecular docking and molecular dynamics based on binding the free energy of the interaction between each compound with the binding pocket of DYRK2. The hit compounds were then confirmed by absorption, distribution, metabolism, and excretion (ADME) prediction. Results: Screening of 7.4 million molecules from the MolPort database afforded six selected hit compounds. By considering the ADME prediction, three prospective curcumin analogues have been selected. These are:  2‐[2‐(1‐methylpyrazol‐4‐yl)ethyl]‐1H,5H,6H,7H,8H‐imidazo[4,5‐c]azepin‐4‐one (Molport-035-369-361), methyl 4‐(3‐hydroxy‐1,2‐oxazol‐5‐yl)piperidine‐1‐carboxylate (Molport-000-004-273) and (1S)‐1‐[5‐(furan‐3‐carbonyl)‐4H,6H,7H‐pyrazolo[1,5‐a]pyrazin‐2‐yl]ethanol (MolPort-035-585-822). Conclusion: Pharmacophore modelling, combined with molecular docking and molecular dynamics simulation, as well as ADME prediction were successfully applied to screen curcumin analogues from the MolPort database as DYRK2 inhibitors. All selected compounds that have better predicted pharmacokinetic properties than that of curcumin are considered for further study.

scholarly journals Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

2021 ◽  
Vol 9 ◽  
Author(s):  
Shailima Rampogu ◽  
Keun Woo Lee
Keyword(s):  
Effective Treatment ◽  
Pharmacophore Model ◽  
Drug Repurposing ◽  
Binding Pocket ◽  
Binding Mode ◽  
Computational Method ◽  
Dynamics Simulation ◽  
Potential Candidate ◽  
Pharmacophore Modelling ◽  
Discovery Studio

The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating effect globally with no effective treatment. The swift strategy to find effective treatment against coronavirus disease 2019 (COVID-19) is to repurpose the approved drugs. In this pursuit, an exhaustive computational method has been used on the DrugBank compounds targeting nsp16/nsp10 complex (PDB code: 6W4H). A structure-based pharmacophore model was generated, and the selected model was escalated to screen DrugBank database, resulting in three compounds. These compounds were subjected to molecular docking studies at the protein-binding pocket employing the CDOCKER module available with the Discovery Studio v18. In order to discover potential candidate compounds, the co-crystallized compound S-adenosyl methionine (SAM) was used as the reference compound. Additionally, the compounds remdesivir and hydroxycholoroquine were employed for comparative docking. The results have shown that the three compounds have demonstrated a higher dock score than the reference compounds and were upgraded to molecular dynamics simulation (MDS) studies. The MDS results demonstrated that the three compounds, framycetin, kanamycin, and tobramycin, are promising candidate compounds. They have represented a stable binding mode at the targets binding pocket with an average protein backbone root mean square deviation below 0.3 nm. Additionally, they have prompted the hydrogen bonds during the entire simulations, inferring that the compounds have occupied the active site firmly. Taken together, our findings propose framycetin, kanamycin, and tobramycin as potent putative inhibitors for COVID-19 therapeutics.

Discovery of potential negative allosteric modulators of mGluR5 from natural products using pharmacophore modeling, molecular docking, and molecular dynamics simulation studies

2015 ◽  
Vol 93 (11) ◽  
pp. 1199-1206 ◽  
Author(s):  
Ludi Jiang ◽  
Yong Li ◽  
Liansheng Qiao ◽  
Xi Chen ◽  
Yusu He ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Natural Products ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
Pharmacophore Model ◽  
Pharmacophore Modeling ◽  
Dynamics Simulation ◽  
Allosteric Modulators ◽  
Subtype Selectivity ◽  
Wide Range

mGluR5, which belongs to the G-protein-coupled receptor superfamily, is believed to be associated with many human diseases, such as a wide range of neurological disorders, gastroesophageal reflux disease, and cancer. Comparing with compounds that target on the orthosteric binding site, significant roles have been established for mGluR5 negative allosteric modulators (NAMs) due to their higher subtype selectivity and more suitable pharmacokinetic profiles. Nevertheless, to date, none of them have come to market for various reasons. In this study, a 3D quantitative pharmacophore model was generated by using the HypoGen module in Discovery Studio 4.0. With several validation methods ultilized, the optimal pharmacophore model Hypo2 was selected to discover potential mGluR5 NAMs from natural products. Two hundred and seventeen potential NAMs were obtained after being filtered by Lipinski’s rule (≥4). Then, molecular docking was used to refine the pharmacophore-based screening results and analyze the binding mode of NAMs and mGluR5. Three compounds, aglaiduline, 5-O-ethyl-hirsutanonol, and yakuchinone A, with good ADMET properties, acceptable Fit value and estimated value, and high docking score, were reserved for a molecular dynamics simulation study. All of them have stability of ligand binding. From our computational results, there might exhibit drug-like negative allosteric moderating effects on mGluR5 in these natural products. This work provides a reliable method for discovering mGluR5 NAMs from natural products.

scholarly journals Discovery of New Hydroxyethylamine Analogs Against 3CLpro Protein Target of SARS-CoV-2: Molecular Docking, Molecular Dynamics Simulation and Structure-Activity Relationship Studies

2020 ◽  
Author(s):  
Sumit Kumar ◽  
Prem Prakash Sharma ◽  
Uma Shankar ◽  
Dhruv Kumar ◽  
Sanjeev K Joshi ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Cysteine Protease ◽  
Binding Pocket ◽  
Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Activity Relationship ◽  
Docking Score ◽  
Structure Activity ◽  
Novel Coronavirus

<p><br></p> <p>A novel coronavirus, SARS-CoV-2 has caused a recent pandemic called COVID-19 and a severe health threat around the world. In the current situation, the virus is rapidly spreading worldwide, and the discovery of vaccine and potential therapeutics are critically essential. The crystal structure for main protease (M<sup>pro</sup>) of SARS-CoV-2, 3-chymotrypsin-like cysteine protease (3CL<sup>pro</sup>) was recently made available and is considerably similar to previously reported SARS-CoV. Due to its essentiality in viral replication, it represents a potential drug target. Herein, computer-aided drug design (CADD) approach was implemented for the initial screening of 13 approved antiviral drugs. Molecular docking of 13 antivirals against 3-chymotrypsin-like cysteine protease (3CL<sup>pro</sup>) enzyme was accomplished and indinavir was described as a lead drug with a docking score of -8.824 and a XP Gscore of -9.466 kcal/mol. Indinavir possesses an important pharmacophore, hydroxyethylamine (HEA), and thus a new library of HEA compounds (>2500) was subjected to virtual screening that led to 25 hits with a docking score more than indinavir. Exclusively, compound <b>16</b> with docking score of -8.955 adhered to drug like parameters, and the Structure-Activity Relationship (SAR) analysis was demonstrated to highlight the importance of chemical scaffolds therein. Molecular Dynamics (MD) simulation studies carried out at 100ns supported the stability of <b>16</b> within the binding pocket. Largly, our results supported that this novel compound <b>16</b> binds to the domain I & II, and domain II-III linker of 3CL<sup>pro</sup> protein, suggesting its suitablity as strong candidate for therapeutic discovery against COVID-19. Lead compound <b>16</b> could pave incredible directions for the design of novel 3CL<sup>pro</sup> inhibitors and ultimately therapeutics against COVID-19 disease.</p> <p><br></p> <p> </p>

scholarly journals Discovery of New Hydroxyethylamine Analogs Against 3CLpro Protein Target of SARS-CoV-2: Molecular Docking, Molecular Dynamics Simulation and Structure-Activity Relationship Studies

2020 ◽  
Author(s):  
Sumit Kumar ◽  
Prem Prakash Sharma ◽  
Uma Shankar ◽  
Dhruv Kumar ◽  
Sanjeev K Joshi ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Cysteine Protease ◽  
Binding Pocket ◽  
Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Activity Relationship ◽  
Docking Score ◽  
Structure Activity ◽  
Novel Coronavirus

<p><br></p> <p>A novel coronavirus, SARS-CoV-2 has caused a recent pandemic called COVID-19 and a severe health threat around the world. In the current situation, the virus is rapidly spreading worldwide, and the discovery of vaccine and potential therapeutics are critically essential. The crystal structure for main protease (M<sup>pro</sup>) of SARS-CoV-2, 3-chymotrypsin-like cysteine protease (3CL<sup>pro</sup>) was recently made available and is considerably similar to previously reported SARS-CoV. Due to its essentiality in viral replication, it represents a potential drug target. Herein, computer-aided drug design (CADD) approach was implemented for the initial screening of 13 approved antiviral drugs. Molecular docking of 13 antivirals against 3-chymotrypsin-like cysteine protease (3CL<sup>pro</sup>) enzyme was accomplished and indinavir was described as a lead drug with a docking score of -8.824 and a XP Gscore of -9.466 kcal/mol. Indinavir possesses an important pharmacophore, hydroxyethylamine (HEA), and thus a new library of HEA compounds (>2500) was subjected to virtual screening that led to 25 hits with a docking score more than indinavir. Exclusively, compound <b>16</b> with docking score of -8.955 adhered to drug like parameters, and the Structure-Activity Relationship (SAR) analysis was demonstrated to highlight the importance of chemical scaffolds therein. Molecular Dynamics (MD) simulation studies carried out at 100ns supported the stability of <b>16</b> within the binding pocket. Largly, our results supported that this novel compound <b>16</b> binds to the domain I & II, and domain II-III linker of 3CL<sup>pro</sup> protein, suggesting its suitablity as strong candidate for therapeutic discovery against COVID-19. Lead compound <b>16</b> could pave incredible directions for the design of novel 3CL<sup>pro</sup> inhibitors and ultimately therapeutics against COVID-19 disease.</p> <p><br></p> <p> </p>

scholarly journals In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: a Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation

2021 ◽  
Vol 12 (4) ◽  
pp. 5100-5115
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Qsar Study ◽  
Determination Coefficient ◽  
Pharmacokinetic Properties

The Chymotrypsin-like protease (3CLpro) is a drug target in the coronavirus because of its role in processing the polyproteins that are translated from the viral RNA. This study applied 3D quantitative structure-activity relationship (3D-QSAR), molecular docking, and ADMET prediction on a series of SARS-CoV 3CLpro inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q2) values of 0.64 and 0.80, the determination coefficient (R2) values of 0.998 and 0.993 and the standard error of the estimate (SEE) values of 0.046 and 0.091, respectively. The acceptable values of the determination coefficient (R2 test) to CoMFA and CoMSIA respectively corresponding to values of 0.725 and 0.690 utilizing a test set of seven molecules prove the high predictive ability of this model. Molecular docking analysis was utilized to validate 3D-QSAR methods and explain the binding site interactions and affinity between the most active ligands and the SARS-CoV 3CLpro receptor. Based on these results, a novel series of compounds were predicted, and their pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, the best-docked candidate molecules were subjected to molecular dynamics (MD) simulation to affirm their dynamic behavior and stability.

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