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10.1371/journal.pcbi.1004343 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1004343 C4619893!4619893!26496371     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2015 ; 11 (10): ä Nephropedia Template TP {{tp|p=26496371|t=2015. Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling |pdf=|usr=}}{{26496371}} gab.com Text Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26496371 #FOAvip Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins? atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite. Twit Text FOAVip Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26496371 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26496371 #FOAvip English Wikipedia <ref>{{Cite pmid|26496371}}</ref> Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling #MMPMID26496371 Meier A; Söding J PLoS Comput Biol 2015[Oct]; 11 (10): ä PMID26496371show ga Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins? atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite. äAutomatic Prediction of Protein 3D Structures by Probabilistic Multi-t(epmc).pdf DeepDyve Pubget Overpricing