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Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions
Lignin is one of the most abundant biopolymers in nature and has great potential to be transformed into high-value chemicals. However, the limited availability of molecular structure data hinders its potential industrial applications. Herein, we present the Lignin Structural (LGS) Dataset that inclu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588021/ https://www.ncbi.nlm.nih.gov/pubmed/36273011 http://dx.doi.org/10.1038/s41597-022-01709-4 |
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author | Eswaran, Sudha cheranma devi Subramaniam, Senthil Sanyal, Udishnu Rallo, Robert Zhang, Xiao |
author_facet | Eswaran, Sudha cheranma devi Subramaniam, Senthil Sanyal, Udishnu Rallo, Robert Zhang, Xiao |
author_sort | Eswaran, Sudha cheranma devi |
collection | PubMed |
description | Lignin is one of the most abundant biopolymers in nature and has great potential to be transformed into high-value chemicals. However, the limited availability of molecular structure data hinders its potential industrial applications. Herein, we present the Lignin Structural (LGS) Dataset that includes the molecular structure of milled wood lignin focusing on two major monomeric units (coniferyl and syringyl), and the six most common interunit linkages (phenylpropane β-aryl ether, resinol, phenylcoumaran, biphenyl, dibenzodioxocin, and diaryl ether). The dataset constitutes a unique resource that covers a part of lignin’s chemical space characterized by polymer chains with lengths in the range of 3 to 25 monomer units. Structural data were generated using a sequence-controlled polymer generation approach that was calibrated to match experimental lignin properties. The LGS dataset includes 60 K newly generated lignin structures that match with high accuracy (~90%) the experimentally determined structural compositions available in the literature. The LGS dataset is a valuable resource to advance lignin chemistry research, including computational simulation approaches and predictive modelling. |
format | Online Article Text |
id | pubmed-9588021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95880212022-10-24 Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions Eswaran, Sudha cheranma devi Subramaniam, Senthil Sanyal, Udishnu Rallo, Robert Zhang, Xiao Sci Data Data Descriptor Lignin is one of the most abundant biopolymers in nature and has great potential to be transformed into high-value chemicals. However, the limited availability of molecular structure data hinders its potential industrial applications. Herein, we present the Lignin Structural (LGS) Dataset that includes the molecular structure of milled wood lignin focusing on two major monomeric units (coniferyl and syringyl), and the six most common interunit linkages (phenylpropane β-aryl ether, resinol, phenylcoumaran, biphenyl, dibenzodioxocin, and diaryl ether). The dataset constitutes a unique resource that covers a part of lignin’s chemical space characterized by polymer chains with lengths in the range of 3 to 25 monomer units. Structural data were generated using a sequence-controlled polymer generation approach that was calibrated to match experimental lignin properties. The LGS dataset includes 60 K newly generated lignin structures that match with high accuracy (~90%) the experimentally determined structural compositions available in the literature. The LGS dataset is a valuable resource to advance lignin chemistry research, including computational simulation approaches and predictive modelling. Nature Publishing Group UK 2022-10-22 /pmc/articles/PMC9588021/ /pubmed/36273011 http://dx.doi.org/10.1038/s41597-022-01709-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Eswaran, Sudha cheranma devi Subramaniam, Senthil Sanyal, Udishnu Rallo, Robert Zhang, Xiao Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title | Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title_full | Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title_fullStr | Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title_full_unstemmed | Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title_short | Molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
title_sort | molecular structural dataset of lignin macromolecule elucidating experimental structural compositions |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588021/ https://www.ncbi.nlm.nih.gov/pubmed/36273011 http://dx.doi.org/10.1038/s41597-022-01709-4 |
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