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Feasible-metabolic-pathway-exploration technique using chemical latent space

MOTIVATION: Exploring metabolic pathways is one of the key techniques for developing highly productive microbes for the bioproduction of chemical compounds. To explore feasible pathways, not only examining a combination of well-known enzymatic reactions but also finding potential enzymatic reactions...

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Autores principales: Fuji, Taiki, Nakazawa, Shiori, Ito, Kiyoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454040/
https://www.ncbi.nlm.nih.gov/pubmed/33381845
http://dx.doi.org/10.1093/bioinformatics/btaa809
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author Fuji, Taiki
Nakazawa, Shiori
Ito, Kiyoto
author_facet Fuji, Taiki
Nakazawa, Shiori
Ito, Kiyoto
author_sort Fuji, Taiki
collection PubMed
description MOTIVATION: Exploring metabolic pathways is one of the key techniques for developing highly productive microbes for the bioproduction of chemical compounds. To explore feasible pathways, not only examining a combination of well-known enzymatic reactions but also finding potential enzymatic reactions that can catalyze the desired structural changes are necessary. To achieve this, most conventional techniques use manually predefined-reaction rules, however, they cannot sufficiently find potential reactions because the conventional rules cannot comprehensively express structural changes before and after enzymatic reactions. Evaluating the feasibility of the explored pathways is another challenge because there is no way to validate the reaction possibility of unknown enzymatic reactions by these rules. Therefore, a technique for comprehensively capturing the structural changes in enzymatic reactions and a technique for evaluating the pathway feasibility are still necessary to explore feasible metabolic pathways. RESULTS: We developed a feasible-pathway-exploration technique using chemical latent space obtained from a deep generative model for compound structures. With this technique, an enzymatic reaction is regarded as a difference vector between the main substrate and the main product in chemical latent space acquired from the generative model. Features of the enzymatic reaction are embedded into the fixed-dimensional vector, and it is possible to express structural changes of enzymatic reactions comprehensively. The technique also involves differential-evolution-based reaction selection to design feasible candidate pathways and pathway scoring using neural-network-based reaction-possibility prediction. The proposed technique was applied to the non-registered pathways relevant to the production of 2-butanone, and successfully explored feasible pathways that include such reactions.
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spelling pubmed-84540402021-09-22 Feasible-metabolic-pathway-exploration technique using chemical latent space Fuji, Taiki Nakazawa, Shiori Ito, Kiyoto Bioinformatics Systems MOTIVATION: Exploring metabolic pathways is one of the key techniques for developing highly productive microbes for the bioproduction of chemical compounds. To explore feasible pathways, not only examining a combination of well-known enzymatic reactions but also finding potential enzymatic reactions that can catalyze the desired structural changes are necessary. To achieve this, most conventional techniques use manually predefined-reaction rules, however, they cannot sufficiently find potential reactions because the conventional rules cannot comprehensively express structural changes before and after enzymatic reactions. Evaluating the feasibility of the explored pathways is another challenge because there is no way to validate the reaction possibility of unknown enzymatic reactions by these rules. Therefore, a technique for comprehensively capturing the structural changes in enzymatic reactions and a technique for evaluating the pathway feasibility are still necessary to explore feasible metabolic pathways. RESULTS: We developed a feasible-pathway-exploration technique using chemical latent space obtained from a deep generative model for compound structures. With this technique, an enzymatic reaction is regarded as a difference vector between the main substrate and the main product in chemical latent space acquired from the generative model. Features of the enzymatic reaction are embedded into the fixed-dimensional vector, and it is possible to express structural changes of enzymatic reactions comprehensively. The technique also involves differential-evolution-based reaction selection to design feasible candidate pathways and pathway scoring using neural-network-based reaction-possibility prediction. The proposed technique was applied to the non-registered pathways relevant to the production of 2-butanone, and successfully explored feasible pathways that include such reactions. Oxford University Press 2020-12-29 /pmc/articles/PMC8454040/ /pubmed/33381845 http://dx.doi.org/10.1093/bioinformatics/btaa809 Text en © The Author(s) 2020. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Systems
Fuji, Taiki
Nakazawa, Shiori
Ito, Kiyoto
Feasible-metabolic-pathway-exploration technique using chemical latent space
title Feasible-metabolic-pathway-exploration technique using chemical latent space
title_full Feasible-metabolic-pathway-exploration technique using chemical latent space
title_fullStr Feasible-metabolic-pathway-exploration technique using chemical latent space
title_full_unstemmed Feasible-metabolic-pathway-exploration technique using chemical latent space
title_short Feasible-metabolic-pathway-exploration technique using chemical latent space
title_sort feasible-metabolic-pathway-exploration technique using chemical latent space
topic Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454040/
https://www.ncbi.nlm.nih.gov/pubmed/33381845
http://dx.doi.org/10.1093/bioinformatics/btaa809
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