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PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall

Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and pro...

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Autores principales: De, Partho Sakha, Glass, Torben, Stein, Merle, Spitzlei, Thomas, Raguin, Adélaïde
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663758/
https://www.ncbi.nlm.nih.gov/pubmed/38022701
http://dx.doi.org/10.1016/j.csbj.2023.09.026
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author De, Partho Sakha
Glass, Torben
Stein, Merle
Spitzlei, Thomas
Raguin, Adélaïde
author_facet De, Partho Sakha
Glass, Torben
Stein, Merle
Spitzlei, Thomas
Raguin, Adélaïde
author_sort De, Partho Sakha
collection PubMed
description Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material.
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spelling pubmed-106637582023-09-29 PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall De, Partho Sakha Glass, Torben Stein, Merle Spitzlei, Thomas Raguin, Adélaïde Comput Struct Biotechnol J Software/Web server Article Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material. Research Network of Computational and Structural Biotechnology 2023-09-29 /pmc/articles/PMC10663758/ /pubmed/38022701 http://dx.doi.org/10.1016/j.csbj.2023.09.026 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Software/Web server Article
De, Partho Sakha
Glass, Torben
Stein, Merle
Spitzlei, Thomas
Raguin, Adélaïde
PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title_full PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title_fullStr PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title_full_unstemmed PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title_short PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
title_sort predig: web application to model and predict the enzymatic saccharification of plant cell wall
topic Software/Web server Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663758/
https://www.ncbi.nlm.nih.gov/pubmed/38022701
http://dx.doi.org/10.1016/j.csbj.2023.09.026
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