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10.1021/acs.jctc.6b00339 http://scihub22266oqcxt.onion/10.1021/acs.jctc.6b00339 C5022777!5022777!27340835     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 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 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       J+Chem+Theory+Comput 2016 ; 12 (8): 3473-81 Nephropedia Template TP {{tp|p=27340835|t=2016. Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models |pdf=|usr=}}{{27340835}} gab.com Text Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models JO: J Chem Theory Comput http://www.kidney.de/pget.php?&tw=1&pmid=27340835 #FOAvip Because standard molecular dynamics (MD) simulations are unable to access time scales of interest in complex biomolecular systems, it is common to ?stitch together? information from multiple shorter trajectories using approximate Markov state model (MSM) analysis. However, MSMs may require significant tuning and can yield biased results. Here, by analyzing some of the longest protein MD data sets available (>100 ?s per protein), we show that estimators constructed based on exact non-Markovian (NM) principles can yield significantly improved mean first-passage times (MFPTs) for protein folding and unfolding. In some cases, MSM bias of more than an order of magnitude can be corrected when identical trajectory data are reanalyzed by non-Markovian approaches. The NM analysis includes ?history? information, higher order time correlations compared to MSMs, that is available in every MD trajectory. The NM strategy is insensitive to fine details of the states used and works well when a fine time-discretization (i.e., small ?lag time?) is used. Twit Text FOAVip Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models ????J Chem Theory Comput http://www.kidney.de/pget.php?&tw=1&pmid=27340835 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27340835 #FOAvip English Wikipedia <ref>{{Cite pmid|27340835}}</ref> Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models #MMPMID27340835 Suárez E; Adelman JL; Zuckerman DM J Chem Theory Comput 2016[Aug]; 12 (8): 3473-81 PMID27340835show ga Because standard molecular dynamics (MD) simulations are unable to access time scales of interest in complex biomolecular systems, it is common to ?stitch together? information from multiple shorter trajectories using approximate Markov state model (MSM) analysis. However, MSMs may require significant tuning and can yield biased results. Here, by analyzing some of the longest protein MD data sets available (>100 ?s per protein), we show that estimators constructed based on exact non-Markovian (NM) principles can yield significantly improved mean first-passage times (MFPTs) for protein folding and unfolding. In some cases, MSM bias of more than an order of magnitude can be corrected when identical trajectory data are reanalyzed by non-Markovian approaches. The NM analysis includes ?history? information, higher order time correlations compared to MSMs, that is available in every MD trajectory. The NM strategy is insensitive to fine details of the states used and works well when a fine time-discretization (i.e., small ?lag time?) is used. äAccurate Estimation of Protein Folding and Unfolding Times: Beyond Mar(epmc).pdf DeepDyve Pubget Overpricing