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10.1021/acs.jcim.1c00204 http://scihub22266oqcxt.onion/10.1021/acs.jcim.1c00204 34096704!8631419!34096704     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34096704.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Chem+Inf+Model 2021 ; 61 (6): 2897-2910 Nephropedia Template TP {{tp|p=34096704|t=2021. Finding Druggable Sites in Proteins Using TACTICS |pdf=|usr=}}{{34096704}} gab.com Text Finding Druggable Sites in Proteins Using TACTICS JO: J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34096704 #FOAvip Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis. Twit Text FOAVip Finding Druggable Sites in Proteins Using TACTICS ????J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34096704 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34096704 #FOAvip English Wikipedia <ref>{{Cite pmid|34096704}}</ref> Finding Druggable Sites in Proteins Using TACTICS #MMPMID34096704 Evans DJ; Yovanno RA; Rahman S; Cao DW; Beckett MQ; Patel MH; Bandak AF; Lau AY J Chem Inf Model 2021[Jun]; 61 (6): 2897-2910 PMID34096704show ga Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis. |*COVID-19[MESH] |*SARS-CoV-2[MESH] |Binding Sites[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Protein Conformation[MESH]Finding Druggable Sites in Proteins Using TACTICS (epmc).pdf DeepDyve Pubget Overpricing