Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning

While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse compared to those for proteins and nucleic acids. Cu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Makoto Ikejo, Hirofumi Watanabe, Kohei Shimamura, Shigenori Tanaka
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	force field glucose machine learning molecular dynamics GLYCAM Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/c40d61b4b8744672a6bd528b30abbf99
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c40d61b4b8744672a6bd528b30abbf99
record_format	dspace
spelling	oai:doaj.org-article:c40d61b4b8744672a6bd528b30abbf992021-11-11T18:38:47ZImprovement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning10.3390/molecules262166911420-3049https://doaj.org/article/c40d61b4b8744672a6bd528b30abbf992021-11-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/21/6691https://doaj.org/toc/1420-3049While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse compared to those for proteins and nucleic acids. Currently, the use of GLYCAM06 force field is the most popular, but there have been a number of concerns about its accuracy in the systematic description of structural changes. In the present work, we focus on the improvement of the GLYCAM06 force field for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-<span style="font-variant: small-caps;">d</span>-glucose, a simple and the most abundant monosaccharide molecule, with the aid of machine learning techniques implemented with the TensorFlow library. Following the pre-sampling over a wide range of configuration space generated by MD simulation, the atomic charge and dihedral angle parameters in the GLYCAM06 force field were re-optimized to accurately reproduce the relative energies of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-<span style="font-variant: small-caps;">d</span>-glucose obtained by the density functional theory (DFT) calculations according to the structural changes. The validation for the newly proposed force-field parameters was then carried out by verifying that the relative energy errors compared to the DFT value were significantly reduced and that some inconsistencies with experimental (e.g., NMR) results observed in the GLYCAM06 force field were resolved relevantly.Makoto IkejoHirofumi WatanabeKohei ShimamuraShigenori TanakaMDPI AGarticleforce fieldglucosemachine learningmolecular dynamicsGLYCAMOrganic chemistryQD241-441ENMolecules, Vol 26, Iss 6691, p 6691 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	force field glucose machine learning molecular dynamics GLYCAM Organic chemistry QD241-441
spellingShingle	force field glucose machine learning molecular dynamics GLYCAM Organic chemistry QD241-441 Makoto Ikejo Hirofumi Watanabe Kohei Shimamura Shigenori Tanaka Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
description	While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse compared to those for proteins and nucleic acids. Currently, the use of GLYCAM06 force field is the most popular, but there have been a number of concerns about its accuracy in the systematic description of structural changes. In the present work, we focus on the improvement of the GLYCAM06 force field for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-<span style="font-variant: small-caps;">d</span>-glucose, a simple and the most abundant monosaccharide molecule, with the aid of machine learning techniques implemented with the TensorFlow library. Following the pre-sampling over a wide range of configuration space generated by MD simulation, the atomic charge and dihedral angle parameters in the GLYCAM06 force field were re-optimized to accurately reproduce the relative energies of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-<span style="font-variant: small-caps;">d</span>-glucose obtained by the density functional theory (DFT) calculations according to the structural changes. The validation for the newly proposed force-field parameters was then carried out by verifying that the relative energy errors compared to the DFT value were significantly reduced and that some inconsistencies with experimental (e.g., NMR) results observed in the GLYCAM06 force field were resolved relevantly.
format	article
author	Makoto Ikejo Hirofumi Watanabe Kohei Shimamura Shigenori Tanaka
author_facet	Makoto Ikejo Hirofumi Watanabe Kohei Shimamura Shigenori Tanaka
author_sort	Makoto Ikejo
title	Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
title_short	Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
title_full	Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
title_fullStr	Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
title_full_unstemmed	Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning
title_sort	improvement of the force field for <i>β</i>-<span style="font-variant: small-caps">d</span>-glucose with machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c40d61b4b8744672a6bd528b30abbf99
work_keys_str_mv	AT makotoikejo improvementoftheforcefieldforibispanstylefontvariantsmallcapsdspanglucosewithmachinelearning AT hirofumiwatanabe improvementoftheforcefieldforibispanstylefontvariantsmallcapsdspanglucosewithmachinelearning AT koheishimamura improvementoftheforcefieldforibispanstylefontvariantsmallcapsdspanglucosewithmachinelearning AT shigenoritanaka improvementoftheforcefieldforibispanstylefontvariantsmallcapsdspanglucosewithmachinelearning
_version_	1718431786550165504

Improvement of the Force Field for <i>β</i>-<span style="font-variant: small-caps">d</span>-Glucose with Machine Learning

Ejemplares similares