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LigninGraphs: lignin structure determination with multiscale graph modeling
Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261032/ https://www.ncbi.nlm.nih.gov/pubmed/35794646 http://dx.doi.org/10.1186/s13321-022-00627-2 |
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author | Wang, Yifan Kalscheur, Jake Ebikade, Elvis Li, Qiang Vlachos, Dionisios G. |
author_facet | Wang, Yifan Kalscheur, Jake Ebikade, Elvis Li, Qiang Vlachos, Dionisios G. |
author_sort | Wang, Yifan |
collection | PubMed |
description | Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00627-2. |
format | Online Article Text |
id | pubmed-9261032 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92610322022-07-08 LigninGraphs: lignin structure determination with multiscale graph modeling Wang, Yifan Kalscheur, Jake Ebikade, Elvis Li, Qiang Vlachos, Dionisios G. J Cheminform Software Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00627-2. Springer International Publishing 2022-07-06 /pmc/articles/PMC9261032/ /pubmed/35794646 http://dx.doi.org/10.1186/s13321-022-00627-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Wang, Yifan Kalscheur, Jake Ebikade, Elvis Li, Qiang Vlachos, Dionisios G. LigninGraphs: lignin structure determination with multiscale graph modeling |
title | LigninGraphs: lignin structure determination with multiscale graph modeling |
title_full | LigninGraphs: lignin structure determination with multiscale graph modeling |
title_fullStr | LigninGraphs: lignin structure determination with multiscale graph modeling |
title_full_unstemmed | LigninGraphs: lignin structure determination with multiscale graph modeling |
title_short | LigninGraphs: lignin structure determination with multiscale graph modeling |
title_sort | ligningraphs: lignin structure determination with multiscale graph modeling |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261032/ https://www.ncbi.nlm.nih.gov/pubmed/35794646 http://dx.doi.org/10.1186/s13321-022-00627-2 |
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