scholarly journals Screening of Chloroquine, Hydroxychloroquine and Its Derivatives for Their Binding Affinity to Multiple SARS-CoV-2 Protein Drug Targets

2020 ◽  
Author(s):  
Mallikarjuna Nimgampalle ◽  
Vasudharani Devanthan ◽  
Ambrish Saxena
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Viral Proteins ◽  
Protein Drug ◽  
Potential Treatment ◽  
Health Authorities ◽  
Derivatives Of

Recently Chloroquine and its derivative Hydroxychloroquine have garnered enormous interest amongst the clinicians and health authorities’ world over as a potential treatment to contain COVID-19 pandemic. The present research aims at investigating the therapeutic potential of Chloroquine and its potent derivative Hydroxychloroquine against SARS-CoV-2 viral proteins. At the same time we have screened some chemically synthesized derivatives of Chloroquine and compared their binding efficacy with chemically synthesized Chloroquine derivatives through <i>in silico</i>approaches. For the purpose of the study, we have selected some essential viral proteins and enzymes implicated in SARS-CoV-2 replication and multiplication as putative drug targets.<br>

scholarly journals Screening of Chloroquine, Hydroxychloroquine and Its Derivatives for Their Binding Affinity to Multiple SARS-CoV-2 Protein Drug Targets

2020 ◽  
Author(s):  
Mallikarjuna Nimgampalle ◽  
Vasudharani Devanthan ◽  
Ambrish Saxena
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Viral Proteins ◽  
Protein Drug ◽  
Potential Treatment ◽  
Health Authorities ◽  
Derivatives Of

Recently Chloroquine and its derivative Hydroxychloroquine have garnered enormous interest amongst the clinicians and health authorities’ world over as a potential treatment to contain COVID-19 pandemic. The present research aims at investigating the therapeutic potential of Chloroquine and its potent derivative Hydroxychloroquine against SARS-CoV-2 viral proteins. At the same time we have screened some chemically synthesized derivatives of Chloroquine and compared their binding efficacy with chemically synthesized Chloroquine derivatives through <i>in silico</i>approaches. For the purpose of the study, we have selected some essential viral proteins and enzymes implicated in SARS-CoV-2 replication and multiplication as putative drug targets.<br>

In-silico prediction of the beta carboline alkaloids Harmine and Harmaline as potent drug candidates for the treatment of Parkinson’s disease

2020 ◽  
Vol 19 ◽  
Author(s):  
Rumpa Banerjee ◽  
Mukesh Kumar ◽  
Isha Gaurav ◽  
Sudha Thakur ◽  
Abhimanyu Thakur ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Molecular Docking ◽  
Dopamine Receptors ◽  
Binding Affinity ◽  
In Silico ◽  
Therapeutic Potential ◽  
Treatment Strategies ◽  
Toxicity Profile ◽  
Drug Candidates

Background:: Parkinson’s disease (PD) is a progressive neurodegenerative disease manifested by core symptoms of loss of motor control and postural instability. Loss of dopaminergic neurons is the cause of PD, thus enhancing dopamine level by pharmacological treatment is one of the key treatment strategies for PD. However, limitations of current treatment strategies open the possibility of novel drug candidates for the treatment of PD. Objective:: To investigate the anti-PD potential of Harmine and Harmaline. We aim to evaluate the therapeutic potential of Harmine and Harmaline by in-silico approaches; molecular docking, pharmacokinetic and Prediction of Activity Spectra for Substances (PASS) analysis were used for evaluating the therapeutic potential of Harmine and Harmaline and standard drug levodopa (L-DOPA). Methods:: Auto dock vina was used for molecular docking of all three compounds against D2- and D3- dopamine receptors. The pharmacokinetics (PKs) and toxicity profile were predicted by pkCSM and the pharmacological activity was predicted by PASS analysis. Results:: Molecular docking showed a higher binding affinity of Harmine and Harmaline as compared to L-DOPA, and these results were supported by in-silico pharmacokinetic and toxicity profiling. Moreover, PASS analysis showed anti-PD activi-ty of Harmine and Harmaline. Conclusion:: Harmine and Harmaline exhibit higher binding affinity towards D2- and D3- dopamine receptors compared to L-DOPA, and PKs and toxicity profile support their potential as drug candidates for PD therapy.

scholarly journals Computational Study of the Therapeutic Potential of Novel Heterocyclic Derivatives against SARS-CoV-2

COVID ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 757-774
Author(s):  
Benjamin Ayodipupo Babalola ◽  
Tosin Emmanuel Adetobi ◽  
Oluwamayowa Samuel Akinsuyi ◽  
Otunba Ahmed Adebisi ◽  
Elizabeth Oreoluwa Folajimi
Keyword(s):  
Binding Affinity ◽  
Drug Targets ◽  
Therapeutic Potential ◽  
Computational Study ◽  
Docking Simulation ◽  
Therapeutic Interventions ◽  
Binding Motif ◽  
Therapeutic Benefits ◽  
Heterocyclic Derivatives ◽  
Inhibitory Potential

Severe Acute Respiratory Syndrome Coronavirus- 2 (SARS-CoV-2), including the recently reported severe variant B.1.617.2, has been reported to attack the respiratory tract with symptoms that may ultimately lead to death. While studies have been conducted to evaluate therapeutic interventions against the virus, this study evaluated the inhibitory potential of virtually screened novel derivatives and structurally similar compounds towards SARS-CoV-2 via a computational approach. A molecular docking simulation of the inhibitory potentials of the compounds against the SARS-CoV-2 drug targets—main protease (Mpro), spike protein (Spro), and RNA-dependent RNA polymerase (RdRp)—were evaluated and achieved utilizing AutoDock Vina in PyRx workspace. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of these compounds were assessed using SwissADME and ADMETLab servers. All the compounds displayed high binding affinities for the SARS-CoV-2 drug targets. However, the C13 exhibited the highest binding affinity for the drug targets, Spro and RdRp, while C15 exhibited the highest binding affinity for Mpro. The compounds interacted with the LEU A:271, LEU A:287, ASP A:289, and LEU A:272 of Mpro and the HIS A:540, PRO A:415, PHE A:486, and LEU A:370 of the Spro receptor binding motif and some active site amino acids of RdRp. The compounds also possess a favourable ADMET profile and showed no tendency towards hERG inhibition, hepatotoxicity, carcinogenicity, mutagenicity, or drug-liver injury. These novel compounds could offer therapeutic benefits against SARS-CoV-2, and wet laboratory experiments are necessary to further validate the results of this computational study.

scholarly journals In Silico Mutagenesis Based Remodeling of SARs-CoV-1 Peptide (ATLQAIAS) to Inhibit SARs-CoV-2: Structural-dynamics and Free Energy Calculations

2020 ◽  
Author(s):  
Abbas Khan ◽  
Shaheena Umbreen ◽  
Asma Hameed ◽  
Rida Fatima ◽  
Ujala Zahoor ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Affinity ◽  
In Silico ◽  
Therapeutic Potential ◽  
Decomposition Analysis ◽  
Free Energy Calculations ◽  
Energy Decomposition Analysis ◽  
Energy Calculation ◽  
Binding Affinities ◽  
The World

Abstract Background:The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. To date efforts are continuing to design small molecule inhibitor, vaccines and many other therapeutic options are practiced but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedure. Results:Hence, here we have repurposed a small peptide (ATLQAIAS) from the previous study which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residues energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at residue level. Conclusion:Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARs-CoV-2-borne pneumonia. Our analysis strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak.

http://www.biotech-asia.org/vol18no2/in-silico-modelling-of-1-3-3-substituted-phenyl-prop-2-enoyl-phenyl-thiourea-against-anti-inflammatory-drug-targets/

2021 ◽  
Vol 18 (2) ◽  
pp. 413-421
Author(s):  
Sounok Sengupta ◽  
Ratul Bhowmik ◽  
Satarupa Acharjee ◽  
Suchandra Sen
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Drug Targets ◽  
Interleukin 1 ◽  
Md Simulations ◽  
Data Bank ◽  
Ligand Molecule ◽  
Toxicity Analysis ◽  
Anti Inflammatory ◽  
Oxide Synthase

The main objective of this present study was to analyze the anti-inflammatory activity of the compound 1- 3- [3-(substituted phenyl) prop-2-enoyl) phenyl thiourea against inflammation receptors Secretory Phospholipase A2 (sPLA2-X), Cyclooxygenase-2 (COX-2), Interleukin-1 Receptor-associated Kinase 4 (IRAK4), Tumor Necrosis Factor (TNF-alpha) and Inducible Nitric Oxide Synthase 4 using various in-silico techniques. The 3D structures of the receptors were retrieved from Protein Data Bank in PDB format. The ligand molecule was sketched in Chemdraw Ultra v 10.0. The proteins and the ligand molecule were then individually prepared for docking using AutoDock Tools. Docking was performed using AutoDock Vina. Swiss-ADME and Pre-ADMET web servers were used for ADME, drug-likeness, and toxicity analysis. The receptor showing the best binding affinity with our ligand molecule was further analyzed via Molecular Dynamics (MD) Simulations using iMODS web server. The docking results revealed that our ligand molecule showed the best binding affinity with receptor sPLA2-X. The ADME analysis results of our ligand molecule were also good. MD Simulations study showed good results with our ligand- sPLA2-X receptor docked complex. This study revealed that our ligand molecule is a significant inhibitor sPLA2-X and can be further used as a potential therapy against inflammatory disorders.

Critical Review on the Chemical Aspects of Cannabidiol (CBD) and Harmonization of Computational Bioactivity Data

2020 ◽  
Vol 28 (2) ◽  
pp. 213-237 ◽  
Author(s):  
Andrea Mastinu ◽  
Giovanni Ribaudo ◽  
Alberto Ongaro ◽  
Sara Anna Bonini ◽  
Maurizio Memo ◽  
...  
Keyword(s):  
In Silico ◽  
Cannabis Sativa ◽  
Therapeutic Potential ◽  
The Novel ◽  
In Silico Studies ◽  
Computational Data ◽  
Synthetic Approaches ◽  
Analytical Approaches ◽  
Yield Sensitivity ◽  
Therapeutic Profile

: Cannabidiol (CBD) is a non-psychotropic phytocannabinoid which represents one of the constituents of the “phytocomplex” of Cannabis sativa. This natural compound is attracting growing interest since when CBD-based remedies and commercial products were marketed. This review aims to exhaustively address the extractive and analytical approaches that have been developed for the isolation and quantification of CBD. Recent updates on cutting-edge technologies were critically examined in terms of yield, sensitivity, flexibility and performances in general, and are reviewed alongside original representative results. As an add-on to currently available contributions in the literature, the evolution of the novel, efficient synthetic approaches for the preparation of CBD, a procedure which is appealing for the pharmaceutical industry, is also discussed. Moreover, with the increasing interest on the therapeutic potential of CBD and the limited understanding of the undergoing biochemical pathways, the reader will be updated about recent in silico studies on the molecular interactions of CBD towards several different targets attempting to fill this gap. Computational data retrieved from the literature have been integrated with novel in silico experiments, critically discussed to provide a comprehensive and updated overview on the undebatable potential of CBD and its therapeutic profile.

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

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