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10.1093/molbev/msx137 http://scihub22266oqcxt.onion/10.1093/molbev/msx137 C5850488!5850488!28453724     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mol+Biol+Evol 2017 ; 34 (8): 2085-100 Nephropedia Template TP {{tp|p=28453724|t=2017. A Generative Angular Model of Protein Structure Evolution |pdf=|usr=}}{{28453724}} gab.com Text A Generative Angular Model of Protein Structure Evolution JO: Mol Biol Evol http://www.kidney.de/pget.php?&tw=1&pmid=28453724 #FOAvip Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both ?smooth? conformational changes and ?catastrophic? conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence?structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof. Twit Text FOAVip A Generative Angular Model of Protein Structure Evolution ????Mol Biol Evol http://www.kidney.de/pget.php?&tw=1&pmid=28453724 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28453724 #FOAvip English Wikipedia <ref>{{Cite pmid|28453724}}</ref> A Generative Angular Model of Protein Structure Evolution #MMPMID28453724 Golden M; García-Portugués E; Sørensen M; Mardia KV; Hamelryck T; Hein J Mol Biol Evol 2017[Aug]; 34 (8): 2085-100 PMID28453724show ga Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both ?smooth? conformational changes and ?catastrophic? conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence?structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof. äA Generative Angular Model of Protein Structure Evolution (epmc).pdf DeepDyve Pubget Overpricing