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10.1038/srep14874 http://scihub22266oqcxt.onion/10.1038/srep14874 C4597221!4597221 !26445900     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26445900 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26445900 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2015 ; 5 (ä): 14874 Nephropedia Template TP {{tp|p=26445900 |t=2015. Cutoff lensing: predicting catalytic sites in enzymes |pdf=|usr=}}{{26445900 }} gab.com Text Cutoff lensing: predicting catalytic sites in enzymes JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26445900 #FOAvip Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings. Twit Text FOAVip Cutoff lensing: predicting catalytic sites in enzymes ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26445900 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26445900 #FOAvip English Wikipedia <ref>{{Cite pmid|26445900 }}</ref> Cutoff lensing: predicting catalytic sites in enzymes #MMPMID26445900 Aubailly S ; Piazza F Sci Rep 2015[Oct]; 5 (ä): 14874 PMID26445900 show ga Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings. |*Models, Statistical [MESH] |Allosteric Site [MESH] |Amidinotransferases/*chemistry [MESH] |Amino Acids/*chemistry [MESH] |Arginine Kinase/*chemistry [MESH] |Catalytic Domain [MESH] |Computer Simulation [MESH] |Databases, Protein [MESH] |HIV Protease/*chemistry [MESH] |HIV-1/chemistry [MESH]Cutoff lensing: predicting catalytic sites in enzymes (epmc).pdf DeepDyve Pubget Overpricing