Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis

Kotera, Masaaki; Goto, Susumu

doi:10.2142/biophysico.13.0_195

Cited by 17 publications

(9 citation statements)

References 83 publications

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“…Much work has been pursued to integrate mining processes, reaction rule generation, rule ranking, and final pathway proposal into cohesive, user‐friendly pipelines . For more in‐depth coverage on de novo pathway design, several other review areas are available on the subject …”

Section: Review Of Instances Of Data‐driven Me Effortsmentioning

confidence: 99%

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Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Presnell

Alper

2019

Biotechnology Journal

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The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

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Section: Review Of Instances Of Data‐driven Me Effortsmentioning

confidence: 99%

“…[173,176,185] For more in-depth coverage on de novo pathway design, several other review areas are available on the subject. [172,186,187]…”

Section: Selection Of Enzymes For De Novo Pathwaysmentioning

confidence: 99%

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Presnell

Alper

2019

Biotechnology Journal

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“…In higher plants, triacylglycerols (TAGs) may be in few percentages of total lipids in the leaf tissue but can make up to 60% of the dry weight of oil seeds. Fatty acids can be cleaved off by a lipase and further metabolized in peroxisomes through b-oxidation to yield acetyl-coA [ 20 , 31 ]. A minor application of the lipase enzyme is that it is used as a diagnostic tool in medicine.…”

Section: Introductionmentioning

confidence: 99%

Study of lipase producing gene in wheat – an in silico approach

Rani

Kumari

Poddar

et al. 2021

Journal of Genetic Engineering and Biotechnology

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Background Lipases (EC 3.1.1.3) catalyze the hydrolysis of oil into free fatty acids and glycerol forming the 3rd largest group of commercialized enzymes. Plant lipases grab attention recently because of their specificity, less production and purified cost, and easy availability. In silico approach is the first step to identify different genes coding for lipase in a most common indigenous plant, wheat, to explore the possibility of this plant as an alternative source for commercial lipase production. As the hierarchy organization of genes reflects an ancient process of gene duplication and divergence, many of the theoretical and analytical tools of the phylogenetic systematics can be utilized for comparative genomic studies. Also, in addition to experimental identification and characterization of genes, for computational genomic analysis, Arabidopsis has become a popular strategy to identify crop genes which are economically important, as Arabidopsis genes had been well identified and characterized for lipase. A number of articles had been reported in which genes of wheat have shown strong homology with Arabidopsis. The complete genome sequences of rice and Arabidopsis constitute a valuable resource for comparative genome analysis as they are representatives of the two major evolutionary lineages within the angiosperms. Here, in this in silico approach, Arabidopsis and Oryza sativa serve as models for dicotyledonous and monocotyledonous species, respectively, and the genomic sequence data available was used to identify the lipase genes in wheat. Results In this present study, Ensembl Plants database was explored for lipase producing gene present in wheat genome and 21 genes were screened down as they contain specific domain and motif for lipase (GXSXG). According to the evolutionary analysis, it was found that the gene TraesCS5B02G157100, located in 5B chromosome, has 58.35% sequence similarity with the reported lipase gene of Arabidopsis thaliana and gene TraesCS3A02G463500 located in the 3A chromosome has 51.74% sequence similarity with the reported lipase gene of Oryza sativa. Homology modeling was performed using protein sequences coded by aforementioned genes and optimized by molecular dynamic simulations. Further with the help of molecular docking of modeled structures with tributyrin, binding efficiency was checked, and the difference in energies (DE) was −9.83 kcal/mol and −6.67 kcal/mol, respectively. Conclusions The present work provides a basic understanding of the gene-encoding lipase in wheat, which could be easily accessible and used as a potent industrial enzyme. The study enlightens another direction which can be used further to explore plant lipases.

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“…As a large number of pathway design tools have been published, identifying the best method depending on the overarching project goal and available computational tools is a non-trivial task. Although a number of review articles have been published to complete the task, they generally focus on specific aspects such as existing de novo pathway design tools [8] , [9] , [18] , reconstructing metabolic pathways in organisms of interest [19] , or identifying/refactoring parts and circuit designs beyond pathway prediction [20] . In this review, we discuss in detail all the steps involved in implementing pathway design algorithms (e.g., database construction, pathway ranking, enzyme selection, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Koreta et al. classified these tools into reference-based, reaction-filling, and compound-filling frameworks [19] ; Nakamura et al. classified them as fingerprint-based, maximum common substructure-based, and rule-based method [21] ; Cho et al.…”

Section: Introductionmentioning

confidence: 99%

A review of computational tools for design and reconstruction of metabolic pathways

Wang

Dash

et al. 2017

Synthetic and Systems Biotechnology

114

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Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

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Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis

Cited by 17 publications

References 83 publications

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Study of lipase producing gene in wheat – an in silico approach

A review of computational tools for design and reconstruction of metabolic pathways

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