Homology Modeling and Protein-Protein Molecular Docking analyses elucidate the Potential Binding Pockets of ATP7B: A Candidate Wilson’s disease

There has been progressive improvement in computational drug design from last decade. Numerous computer aided compounds have been reported against neurodegenerative disorders. Wilson’s disease is a common neurodegenerative disease in humans associated with ATP7B that encodes a transmembrane copper-transporting ATPase which induces the copper export from hepatic cells into bile and supplies copper for the functional synthesis of Ceruloplasmin. Almost, 150 mutations of ATP7B have been identified lead to cause Wilson's disease having symptoms of cancers, loss of memory and postural instability. In this research article, 3D structure of ATP7B was predicted by using comparative modelling approaches. The predicted structures were evaluated by utilizing numerous evaluation tools and 98.50% of overall quality factor was observed for the final selected structure. ATOX1 was predicted as the interacting partner of ATP7B and molecular docking analyses of ATP7B and ATOX1 were conducted by using PatchDock. The least global energy of -35.45 Kcal/mol was observed having the interacting residues in the binding pocket. The reported interacting residues may help to target the specific drug development against ATP7B. This research article can be a major initiative to predict the therapeutic drug targets against Wilson’s disease.

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In Silico identification of potential drug targets by subtractive genome analysis of Enterococcus faecium DO

10.1101/2020.02.14.948232 ◽

2020 ◽

Author(s):

Marwah Karim ◽

MD Nazrul Islam ◽

G. M. Nurnabi Azad Jewel

Keyword(s):

Alcohol Dehydrogenase ◽

Enterococcus Faecium ◽

In Silico ◽

Drug Targets ◽

3D Structure ◽

Comparative Genomic ◽

Therapeutic Drug ◽

Potential Drug ◽

Novel Drug ◽

Potential Drug Targets

AbstractOnce believed to be a commensal bacteria, Enterococcus faecium has recently emerged as an important nosocomial pathogen worldwide. A recent outbreak of E. faecium unrevealed natural and in vitro resistance against a myriad of antibiotics namely ampicillin, gentamicin and vancomycin due to over-exposure of the pathogen to these antibiotics. This fact combined with the ongoing threat demands the identification of new therapeutic targets to combat E. faecium infections.In this present study, comparative proteome analysis, subtractive genomic approach, metabolic pathway analysis and additional drug prioritizing parameters were used to propose a potential novel drug targets for E. faecium strain DO. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified a total of 207 putative target proteins in E. faecium DO that showed no similarity to human proteins. Among them 105 proteins were identified as essential novel proteins that could serve as potential drug targets through further bioinformatic approaches; such as-prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characterization. Eventually 19 non-homologous essential proteins of E. faecium DO were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets. Among these targets aldehyde-alcohol dehydrogenase was found to be involved in maximum pathways, and therefore, was chosen as novel drug target. Interestingly, aldehyde-alcohol dehydrogenase enzyme contains two domains namely acetaldehyde dehydrogenase and alcohol dehydrogenase, on which a 3D structure homology modeling and in silico molecular docking were performed. Finally, eight molecules were confirmed as the most suitable ligands for aldehyde-alcohol dehydrogenase and hence proposed as the potential inhibitors of this target.In conclusion, being human non-homologous, aldehyde-alcohol dehydrogenase protein can be targeted for potential therapeutic drug development in future. However, laboratory based experimental research should be performed to validate our findings in vivo.

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Druggability Assessment in TRAPP using Machine Learning Approaches

10.1101/2019.12.19.882340 ◽

2019 ◽

Author(s):

Jui-Hung Yuan ◽

Sungho Bosco Han ◽

Stefan Richter ◽

Rebecca C. Wade ◽

Daria B. Kokh

Keyword(s):

Conformational Changes ◽

Drug Targets ◽

Binding Pocket ◽

Superior Performance ◽

Protein Crystal ◽

Data Sets ◽

Learning Approaches ◽

Factors Affecting ◽

Binding Pockets ◽

Dynamics Simulations

AbstractAccurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Due to the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. We have therefore developed two statistical models, a logistic regression model (TRAPP-LR) and a convolutional neural network model (TRAPP-CNN), for predicting druggability and how it varies with changes in the spatial and physicochemical properties of a binding pocket. These models are integrated into TRAPP (TRAnsient Pockets in Proteins), a tool for the analysis of binding pocket variations along a protein motion trajectory. The models, which were trained on publicly available and self-augmented data sets, show equivalent or superior performance to existing methods on test sets of protein crystal structures, and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. Visualization of the evidence for the decisions of the models in TRAPP facilitates identification of the factors affecting the druggability of protein binding pockets.

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In silico study: Assessment of the inhibition of cyclo-oxygenase 2 by ibuprofen by validating molecular docking and cardiovascular effects reported during the COVID 19 pandemic

International Journal of Pharma Sciences and Research ◽

10.21817/ijpsr/2020/v11i10/201110009 ◽

2020 ◽

Vol 11 (10) ◽

pp. 232-239

Author(s):

Hamza Nadjib Merad-boudia ◽

Majda Dali-Sahi ◽

Baya Guermouche ◽

Nouria Dennoun-Medjati

Keyword(s):

Molecular Docking ◽

In Silico ◽

Drug Targets ◽

3D Structure ◽

Cardiovascular Effects ◽

Crystallographic Structure ◽

Discovery Studio ◽

In Silico Study ◽

Inflammatory Drugs ◽

Anti Inflammatory

Introduction The Covid 19 pandemic has put the cardiovascular risk incurred when using nonsteroidal anti-inflammatory drugs at the heart of the discussion. Based on the information currently available, WHO does not recommend the use of ibuprofen. the objective is to evaluate the inhibition of cyclo-oxygenase 2 by ibuprofen by validating molecular docking. Method The crystallographic structure of ibuprofen bound to cyclooxygenase-2 was obtained from the Protein Data Bank (PDB) at a resolution <3.00 Å. The receiver was visualized using Discovery Studio Visualizer version 2.5.5. It was efficiently prepared using AutoDock / Vina software. The 3D structure of Ligand (Ibuprofen) was downloaded from the Drugbak database (https://www.drugbank.ca/): Accession number DB01050 Results Molecular docking was chosen as the first-line discrimination of the ibuprofen-COX2 intercation for the in silico study of putative competitors. The complex formed by Ibuprofen-COX 2 from the experimental model gives a docking score (Affinity: -7.3 (kcal / mol) with a mean square deviation of (RMSD = 23.884). Conclusion The evaluation of the inhibition of cyclo-oxygenase 2 by ibuprofen was validated by molecular docking. Cardiovascular effects already reported in patients treated with traditional non-steroidal anti-inflammatory drugs and coxibs have been observed in patients with COVID 19. Molecular docking becomes an essential step in drug discovery to explore other drug targets

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