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10.1093/bib/bbw129 http://scihub22266oqcxt.onion/10.1093/bib/bbw129 C5952956!5952956!28040746     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2018 ; 19 (3): 482-94 Nephropedia Template TP {{tp|p=28040746|t=2018. Sixty-five years of the long march in protein secondary structure prediction: the final stretch?|pdf=|usr=}}{{28040746}} gab.com Text Sixty-five years of the long march in protein secondary structure prediction: the final stretch JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=28040746 #FOAvip Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82?84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88?90%), alternative to secondary structure prediction (prediction of backbone torsion angles and C?-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction. Twit Text FOAVip Sixty-five years of the long march in protein secondary structure prediction: the final stretch ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=28040746 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28040746 #FOAvip English Wikipedia <ref>{{Cite pmid|28040746}}</ref> Sixty-five years of the long march in protein secondary structure prediction: the final stretch? #MMPMID28040746 Yang Y; Gao J; Wang J; Heffernan R; Hanson J; Paliwal K; Zhou Y Brief Bioinform 2018[May]; 19 (3): 482-94 PMID28040746show ga Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82?84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88?90%), alternative to secondary structure prediction (prediction of backbone torsion angles and C?-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction. äSixty-five years of the long march in protein secondary structure pred(epmc).pdf DeepDyve Pubget Overpricing