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10.1007/s12551-017-0288-0 http://scihub22266oqcxt.onion/10.1007/s12551-017-0288-0 C5578931!5578931!28799089     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28799089&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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biophys+Rev 2017 ; 9 (4): 339-44 Nephropedia Template TP {{tp|p=28799089|t=2017. Searching for the Pareto frontier in multi-objective protein design |pdf=|usr=}}{{28799089}} gab.com Text Searching for the Pareto frontier in multi-objective protein design JO: Biophys Rev http://www.kidney.de/pget.php?&tw=1&pmid=28799089 #FOAvip The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence?structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set?designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides. Twit Text FOAVip Searching for the Pareto frontier in multi-objective protein design ????Biophys Rev http://www.kidney.de/pget.php?&tw=1&pmid=28799089 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28799089 #FOAvip English Wikipedia <ref>{{Cite pmid|28799089}}</ref> Searching for the Pareto frontier in multi-objective protein design #MMPMID28799089 Nanda V; Belure SV; Shir OM Biophys Rev 2017[Aug]; 9 (4): 339-44 PMID28799089show ga The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence?structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set?designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides. äSearching for the Pareto frontier in multi-objective protein design (epmc).pdf DeepDyve Pubget Overpricing