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10.1080/07391102.2021.1993342 http://scihub22266oqcxt.onion/10.1080/07391102.2021.1993342 34699337!ä!34699337 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34699337&cmd=llinks): Failed to open stream: HTTP request failed! 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Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease |pdf=|usr=}}{{34699337}} gab.com Text Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease JO: J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=34699337 #FOAvip Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue - develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called 'm10', comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called 'phy', supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M(pro)) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M(pro) docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that crawled to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools.Communicated by Ramaswamy H. Sarma. Twit Text FOAVip Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease ????J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=34699337 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34699337 #FOAvip English Wikipedia <ref>{{Cite pmid|34699337}}</ref> Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease #MMPMID34699337 Sanjeev BS; Chitara D; Madhumalar A J Biomol Struct Dyn 2022[]; 40 (24): 13564-13580 PMID34699337show ga Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue - develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called 'm10', comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called 'phy', supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M(pro)) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M(pro) docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that crawled to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools.Communicated by Ramaswamy H. Sarma. |*COVID-19[MESH] |Coronavirus 3C Proteases[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Peptide Hydrolases[MESH] |Protease Inhibitors[MESH]Physiological models to study the effect of molecular crowding on mult(epmc).pdf DeepDyve Pubget Overpricing