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10.3389/fmolb.2017.00015 http://scihub22266oqcxt.onion/10.3389/fmolb.2017.00015 C5360716!5360716!28382301     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       Front+Mol+Biosci 2017 ; 4 (ä): ä Nephropedia Template TP {{tp|p=28382301|t=2017. Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps |pdf=|usr=}}{{28382301}} gab.com Text Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps JO: Front Mol Biosci http://www.kidney.de/pget.php?&tw=1&pmid=28382301 #FOAvip A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling. Twit Text FOAVip Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps ????Front Mol Biosci http://www.kidney.de/pget.php?&tw=1&pmid=28382301 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28382301 #FOAvip English Wikipedia <ref>{{Cite pmid|28382301}}</ref> Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps #MMPMID28382301 Habeck M Front Mol Biosci 2017[]; 4 (ä): ä PMID28382301show ga A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling. äBayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps (epmc).pdf DeepDyve Pubget Overpricing