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10.1093/glycob/cww020 http://scihub22266oqcxt.onion/10.1093/glycob/cww020 C4976521!4976521 !26911287     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26911287 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Glycobiology 2016 ; 26 (7 ): 772-783 Nephropedia Template TP {{tp|p=26911287 |t=2016. Combining 3D structure with glycan array data provides insight into the origin of glycan specificity |pdf=|usr=}}{{26911287 }} gab.com Text Combining 3D structure with glycan array data provides insight into the origin of glycan specificity JO: Glycobiology http://www.kidney.de/pget.php?&tw=1&pmid=26911287 #FOAvip Defining how a glycan-binding protein (GBP) specifically selects its cognate glycan from among the ensemble of glycans within the cellular glycome is an area of intense study. Powerful insight into recognition mechanisms can be gained from 3D structures of GBPs complexed to glycans; however, such structures remain difficult to obtain experimentally. Here an automated 3D structure generation technique, called computational carbohydrate grafting, is combined with the wealth of specificity information available from glycan array screening. Integration of the array data with modeling and crystallography allows generation of putative co-complex structures that can be objectively assessed and iteratively altered until a high level of agreement with experiment is achieved. Given an accurate model of the co-complexes, grafting is also able to discern which binding determinants are active when multiple potential determinants are present within a glycan. In some cases, induced fit in the protein or glycan was necessary to explain the observed specificity, while in other examples a revised definition of the minimal binding determinants was required. When applied to a collection of 10 GBP-glycan complexes, for which crystallographic and array data have been reported, grafting provided a structural rationalization for the binding specificity of >90% of 1223 arrayed glycans. A webtool that enables researchers to perform computational carbohydrate grafting is available at www.glycam.org/gr (accessed 03 March 2016). Twit Text FOAVip Combining 3D structure with glycan array data provides insight into the origin of glycan specificity ????Glycobiology http://www.kidney.de/pget.php?&tw=1&pmid=26911287 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26911287 #FOAvip English Wikipedia <ref>{{Cite pmid|26911287 }}</ref> Combining 3D structure with glycan array data provides insight into the origin of glycan specificity #MMPMID26911287 Grant OC ; Tessier MB ; Meche L ; Mahal LK ; Foley BL ; Woods RJ Glycobiology 2016[Jul]; 26 (7 ): 772-783 PMID26911287 show ga Defining how a glycan-binding protein (GBP) specifically selects its cognate glycan from among the ensemble of glycans within the cellular glycome is an area of intense study. Powerful insight into recognition mechanisms can be gained from 3D structures of GBPs complexed to glycans; however, such structures remain difficult to obtain experimentally. Here an automated 3D structure generation technique, called computational carbohydrate grafting, is combined with the wealth of specificity information available from glycan array screening. Integration of the array data with modeling and crystallography allows generation of putative co-complex structures that can be objectively assessed and iteratively altered until a high level of agreement with experiment is achieved. Given an accurate model of the co-complexes, grafting is also able to discern which binding determinants are active when multiple potential determinants are present within a glycan. In some cases, induced fit in the protein or glycan was necessary to explain the observed specificity, while in other examples a revised definition of the minimal binding determinants was required. When applied to a collection of 10 GBP-glycan complexes, for which crystallographic and array data have been reported, grafting provided a structural rationalization for the binding specificity of >90% of 1223 arrayed glycans. A webtool that enables researchers to perform computational carbohydrate grafting is available at www.glycam.org/gr (accessed 03 March 2016). |*Molecular Conformation [MESH] |Carbohydrates/chemistry [MESH] |Carrier Proteins [MESH] |Computational Biology [MESH] |Polysaccharides/*chemistry/classification/metabolism [MESH]Combining 3D structure with glycan array data provides insight into th(epmc).pdf DeepDyve Pubget Overpricing