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Experimental and computational studies of cellulases as bioethanol enzymes
Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345620/ https://www.ncbi.nlm.nih.gov/pubmed/35730402 http://dx.doi.org/10.1080/21655979.2022.2085541 |
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author | Ranganathan, Shrivaishnavi Mahesh, Sankar Suresh, Sruthi Nagarajan, Ayshwarya Z. Sen, Taner M.Yennamalli, Ragothaman |
author_facet | Ranganathan, Shrivaishnavi Mahesh, Sankar Suresh, Sruthi Nagarajan, Ayshwarya Z. Sen, Taner M.Yennamalli, Ragothaman |
author_sort | Ranganathan, Shrivaishnavi |
collection | PubMed |
description | Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol conversion catalysts are microorganism-based cellulolytic enzymes, but lack optimization for high bioethanol conversion, due to biological and other factors. A better understanding of molecular underpinnings of cellulolytic enzyme mechanisms and significant ways to improve them can accelerate the bioethanol commercial production process. In order to do this, experimental methods are the primary choice to evaluate and characterize cellulase’s properties, but they are time-consuming and expensive. A time-saving, complementary approach involves computational methods that evaluate the same properties and improves our atomistic-level understanding of enzymatic mechanism of action. Theoretical methods in many cases have proposed research routes for subsequent experimental testing and validation, reducing the overall research cost. Having a plethora of tools to evaluate cellulases and the yield of the enzymatic process will aid in planning more optimized experimental setups. Thus, there is a need to connect the computational evaluation methods with the experimental methods to overcome the bottlenecks in the bioethanol industry. This review discusses various experimental and computational methods and their use in evaluating the multiple properties of cellulases. |
format | Online Article Text |
id | pubmed-9345620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-93456202022-08-03 Experimental and computational studies of cellulases as bioethanol enzymes Ranganathan, Shrivaishnavi Mahesh, Sankar Suresh, Sruthi Nagarajan, Ayshwarya Z. Sen, Taner M.Yennamalli, Ragothaman Bioengineered Review Bioethanol industries and bioprocesses have many challenges that constantly impede commercialization of the end product. One of the bottlenecks in the bioethanol industry is the challenge of discovering highly efficient catalysts that can improve biomass conversion. The current promising bioethanol conversion catalysts are microorganism-based cellulolytic enzymes, but lack optimization for high bioethanol conversion, due to biological and other factors. A better understanding of molecular underpinnings of cellulolytic enzyme mechanisms and significant ways to improve them can accelerate the bioethanol commercial production process. In order to do this, experimental methods are the primary choice to evaluate and characterize cellulase’s properties, but they are time-consuming and expensive. A time-saving, complementary approach involves computational methods that evaluate the same properties and improves our atomistic-level understanding of enzymatic mechanism of action. Theoretical methods in many cases have proposed research routes for subsequent experimental testing and validation, reducing the overall research cost. Having a plethora of tools to evaluate cellulases and the yield of the enzymatic process will aid in planning more optimized experimental setups. Thus, there is a need to connect the computational evaluation methods with the experimental methods to overcome the bottlenecks in the bioethanol industry. This review discusses various experimental and computational methods and their use in evaluating the multiple properties of cellulases. Taylor & Francis 2022-06-22 /pmc/articles/PMC9345620/ /pubmed/35730402 http://dx.doi.org/10.1080/21655979.2022.2085541 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Ranganathan, Shrivaishnavi Mahesh, Sankar Suresh, Sruthi Nagarajan, Ayshwarya Z. Sen, Taner M.Yennamalli, Ragothaman Experimental and computational studies of cellulases as bioethanol enzymes |
title | Experimental and computational studies of cellulases as bioethanol enzymes |
title_full | Experimental and computational studies of cellulases as bioethanol enzymes |
title_fullStr | Experimental and computational studies of cellulases as bioethanol enzymes |
title_full_unstemmed | Experimental and computational studies of cellulases as bioethanol enzymes |
title_short | Experimental and computational studies of cellulases as bioethanol enzymes |
title_sort | experimental and computational studies of cellulases as bioethanol enzymes |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345620/ https://www.ncbi.nlm.nih.gov/pubmed/35730402 http://dx.doi.org/10.1080/21655979.2022.2085541 |
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