<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Trapl, Dalibor</dc:creator> <dc:creator>Cuerdo del Río, Carmen</dc:creator> <dc:creator>Kříž, Pavel</dc:creator> <dc:creator>Spiwok, Vojtěch</dc:creator> <dc:date>2020-05-03</dc:date> <dc:description>Alchemistic simulations are versatile tools for prediction of relative free energy differences. Accuracy of these methods depends critically on sampling of orthogonal (non-Alchemistic) degrees of freedom. Here we apply flying Gaussian method to accelerate such orthogonal degree of freedom – peptide bond cis/trans iso-merisation. The approach is demonstrated on prediction of pKa value of N-acetylproline. Isomerization of the amide bond was accelerated in this simulation by multiple orders of magnitude. Alchemistic free energy was obtained by reweighting. We also demonstrate that the accuracy of biased Alchemistic simulations can be significantly improved by a simple redefinition of the thermodynamic cycle using a flattening. Such redefinition can be applied a posteriori to improve the accuracy of biased Alchemistic simulations.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2020.0049/v1</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:2020.0049/v1</dc:identifier> <dc:identifier>mcid:2020.0049/v1</dc:identifier> <dc:identifier>oai:materialscloud.org:387</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Alchemistic simulations</dc:subject> <dc:subject>Biased simulations</dc:subject> <dc:subject>Reweighting</dc:subject> <dc:title>Improving accuracy of biased Alchemistic simulations</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>