scholarly journals In Silico Docking analysis of Mycobacterium tuberculosis potential targets AftB and EmbA with selected phytochemicals

2019 ◽  
Vol 7 (2) ◽  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
In Silico ◽  
Docking Analysis ◽  
In Silico Docking

scholarly journals In vitro toxicity and in silico docking analysis of two novel selective AH-receptor modulators

2018 ◽  
Vol 52 ◽  
pp. 178-188 ◽  
Author(s):  
Selma Mahiout ◽  
Sara Giani Tagliabue ◽  
Atefeh Nasri ◽  
Iyekhoetin Matthew Omoruyi ◽  
Lars Pettersson ◽  
...  
Keyword(s):  
In Silico ◽  
In Vitro Toxicity ◽  
Ah Receptor ◽  
Docking Analysis ◽  
In Silico Docking ◽  
Receptor Modulators

scholarly journals in Silico Docking and Molecular Dynamic Simulation of 3-Dehydroquinate Dehydratase from Mycobacterium Tuberculosis Through Virtual Screening and Pharmacokinetics Studies

2020 ◽  
Author(s):  
Mustafa Alhaji Isa ◽  
Muhammad M Ibrahim
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
Molecular Dynamic ◽  
In Silico ◽  
Md Simulation ◽  
Binding Energies ◽  
In Silico Docking ◽  
Pharmacokinetic Properties

The 3-hydroquinate synthase (DHQase) is an enzyme that catalyzes the third step of the shikimate pathway in <i>Mycobacterium tuberculosis</i> (MTB), by converting 3-dehydroquinate into 3-dehydroshikimate. In this study, the novel inhibitors of DHQase from MTB was identified using in silico approach. The crystal structure of DHQase bound to 1,3,4-trihydroxy-5-(3-phenoxypropyl)-cyclohexane-1-carboxylic acid (CA) obtained from the Protein Data Bank (PDB ID: 3N76). The structure prepared through energy minimization and structure optimization. A total of 9699 compounds obtained from Zinc and PubChem databases capable of binding to DHQase and subjected to virtual screening through Lipinski’s rule of five and molecular docking analysis. Eight (8) compounds with good binding energies, ranged between ─8.99 to ─8.39kcal/mol were selected, better than the binding energy of ─4.93kcal/mol for CA and further filtered for pharmacokinetic properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity or ADMET). Five compounds (ZINC14981770, ZINC14741224, ZINC14743698, ZINC13165465, and ZINC8442077) which had desirable pharmacokinetic properties selected for molecular dynamic (MD) simulation and molecular generalized born surface area (MM-GBSA) analyses. The results of the analyses showed that all the compounds formed stable and rigid complexes after the 50ns MD simulation and also had a lower binding as compared to CA. Therefore, these compounds considered as good inhibitors of MTB after in vitro and in vivo validation.”

scholarly journals ANTI-PARKINSON POTENTIAL OF PERSEA AMERICANA SEED EXTRACTS THROUGH IN-SILICO DOCKING STUDY

2020 ◽  
pp. 152-155
Author(s):  
RACHAEL EVANGELINE ◽  
NIHAL AHMED
Keyword(s):  
Binding Energy ◽  
Hydrogen Bonds ◽  
In Silico ◽  
Water Extract ◽  
Docking Study ◽  
Persea Americana ◽  
Ligand Docking ◽  
Docking Analysis ◽  
In Silico Docking ◽  
Free Binding Energy

Objective: The aim of this study is to investigate the potential of Persea americana extracts for their Anti-Parkinson application through an in-silico docking study. Methods: PubChem and protein data bank databases were used to retrieve 3D structures. AutoDock4 was used to perform protein-ligand docking analysis. PyMOL was used to visualize the docking results. Results: Among the 30 ligand, the highest affinity was demonstrated by Hesperidin with a free binding energy of −6.8 kcal/mol and formation of five hydrogen bonds. The second highest significance was demonstrated by Biphenyl 4-(4-diethylaminobenzylidenamino) with a free binding energy of −5.9 kcal/mol with the formation of 2 hydrogen bonds. Among the three sets of phytochemicals from different solvent extracts, water extract demonstrated the highest potential as Anti-Parkinson active. Conclusion: P. americana extracts were analyzed for their Anti-Parkinson potential, and among the three extracts, the aqueous extract was predicted to have significant Anti-Parkinson potential, based on in silico docking analysis, due to the presence of active phytochemicals such as Hesperidin and others.

Simultaneous detection of drug-resistant mutations in Mycobacterium tuberculosis and determining their role through in silico docking

2020 ◽  
Vol 20 ◽  
Author(s):  
Somanna Nachappa ◽  
Sumana Neelambike ◽  
Ahmad Sarikhani ◽  
Nallur Ramachandra
Keyword(s):  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Dna Sequencing ◽  
In Silico ◽  
Sequence Data ◽  
Simultaneous Detection ◽  
Fluoroquinolone Resistance ◽  
Drug Resistant ◽  
In Silico Docking

: A molecular method for diagnosis of drug-resistant Tuberculosis is Multiplex allele-specific PCR (MAS-PCR), which is more time-efficient. Also, understanding the role of mutations when translated to protein, in causing resistance helps better drug designing. Aims: To study MAS-PCR in the detection of drug resistance in comparison to DNA sequencing, and understand the mechanism of interaction of drugs with mutant proteins in Mycobacterium tuberculosis. Methods: Detection of drug-resistant mutations using MAS-PCR and validation through DNA sequencing. MAS-PCR targeted four genes, iniA for the drug Ethambutol, rpsL and rrs for Streptomycin, and gyrA for Fluoroquinolone resistance, respectively. Further, the sequence data was analysed and modelled to study the effect on interaction of the anti-TB drug molecule with the target protein using in silico docking. Results: We identified drug-resistant mutations in four out of 95 isolates with one of them carrying a mutation at codon iniA501, two at gyrA94, and one for both iniA501 and gyrA94 using MAS-PCR. DNA sequencing confirmed drug-resistant mutations in only two isolates, whereas two others had mutation adjacent to the target allele. Molecular docking showed Estimated Free Energy of Binding (ΔG) being higher for Fluoroquinolone binding with GyrA D94V mutant. Both, wild and mutant IniA interact with EMB but had no significant effect on binding energy. Conclusions: DNA sequencing-based drug resistance detection of TB is more accurate than MAS-PCR. Understanding the role of mutations in influencing the drug-protein interaction will help in designing effective drug alternatives.

scholarly journals Cholesterol Interaction with the MAGUK Protein Family Member, MPP1, via CRAC and CRAC-Like Motifs: An In Silico Docking Analysis

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0133141 ◽  
Author(s):  
Marcin A. Listowski ◽  
Jacek Leluk ◽  
Sebastian Kraszewski ◽  
Aleksander F. Sikorski
Keyword(s):  
Family Member ◽  
In Silico ◽  
Protein Family ◽  
Docking Analysis ◽  
In Silico Docking ◽  
Maguk Protein
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