Expression and characterization of alkaline protease from the metagenomic library of tannery activated sludge

Selvaraju, Gayathri Devi; Fathima, Anwar Aliya; Sanitha, Mary; Sellamuthu, Iyappan; Curtis, Wayne R.; Ramya, Mohandass

doi:10.1016/j.jbiosc.2016.05.012

Cited by 31 publications

(19 citation statements)

References 28 publications

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“…The obtained k m values are quite low compared to most of microbial proteases (0.3-19.2 g/l) [1,3,18,19].…”

Section: Determination Of the Molecular Mass Of Peptidase From B Tmentioning

confidence: 69%

“…Various results were obtained regarding the best linearization method in kinetics studies of protease-catalyzed casein hydrolysis. Devi S.G. et al in their research of alkaline protease from activated sludge of a tannery, considered the Eadie-Hofstee method to be optimal, while the Lineweaver-Burk equation resulted in sig-nificant standard errors [3]. Yoshii et al, studying the kinetics of two proteases, obtained similar values for the kinetic constants, determined by the Lineweaver-Burk and Hanes methods.…”

mentioning

confidence: 99%

“…The steady-state enzyme kinetics has been studied with the use of the Michaelis-Menten equation and various forms of its linearization, such as Lineweaver-Burk, Hanes, Eadie-Hofstee, Cornish-Bowden-Eisenthal methods [1][2][3][4][5]. Various results were obtained regarding the best linearization method in kinetics studies of protease-catalyzed casein hydrolysis.…”

mentioning

confidence: 99%

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Kinetics of casein hydrolysis by peptidase from Bacillus thuringiensis var. israelensis

Sevastyanov¹,

Shesterenko²,

Ryzhak³

et al. 2019

Ukr.Biochem.J

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“…The obtained k m values are quite low compared to most of microbial proteases (0.3-19.2 g/l) [1,3,18,19].…”

Section: Determination Of the Molecular Mass Of Peptidase From B Tmentioning

confidence: 69%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Kinetics of casein hydrolysis by peptidase from Bacillus thuringiensis var. israelensis

Sevastyanov¹,

Shesterenko²,

Ryzhak³

et al. 2019

Ukr.Biochem.J

View full text Add to dashboard Cite

“…On the other hand, LipM protein sequence exhibited 48% identity with the Alpha/beta hydrolase AS‐Trib12 belonging to an uncultured bacterium. Although these homology analyses were carried out with proteins that were identified from already reported domains, it is surprising to observe identity values even much lower than those observed for novel enzymes recovered in functional assays (67–92% identity with >90% query cover) (Biver, Portetelle, & Vandenbol, ; Devi et al., ). This result highlights even more the impact of the current approach to identify hidden novel enzymes from metagenomic samples.…”

Section: Resultsmentioning

confidence: 99%

“…Although these homology analyses were carried out with proteins that were identified from already reported domains, it is surprising to observe identity values even much lower than those observed for novel enzymes recovered in functional assays (67-92% identity with >90% query cover) (Biver, Portetelle, & Vandenbol, 2013;Devi et al, 2016).…”

Section: Recovery Of Enzymatic Activities and Characterizationmentioning

confidence: 96%

Recovery and functional validation of hidden soil enzymes in metagenomic libraries

et al. 2019

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The vast microbial diversity on the planet represents an invaluable source for identifying novel activities with potential industrial and therapeutic application. In this regard, metagenomics has emerged as a group of strategies that have significantly facilitated the analysis of DNA from multiple environments and has expanded the limits of known microbial diversity. However, the functional characterization of enzymes, metabolites, and products encoded by diverse microbial genomes is limited by the inefficient heterologous expression of foreign genes. We have implemented a pipeline that combines NGS and Sanger sequencing as a way to identify fosmids within metagenomic libraries. This strategy facilitated the identification of putative proteins, subcloning of targeted genes and preliminary characterization of selected proteins. Overall, the in silico approach followed by the experimental validation allowed us to efficiently recover the activity of previously hidden enzymes derived from agricultural soil samples. Therefore, the methodology workflow described herein can be applied to recover activities encoded by environmental DNA from multiple sources.

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A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification

Shahraki

Atanaki

Ariaeenejad

et al. 2022

Biotech & Bioengineering

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The growing adoption of enzymes as biocatalysts in various industries has accentuated the demand for acquiring access to the great natural diversity and, in the meantime, the advent and advancements of metagenomics and high‐throughput sequencing technologies have offered an unprecedented opportunity to explore this extensive resource. Lipases, enzymes responsible for the biological turnover of lipids, are among the most commercialized biocatalysts with numerous applications in different domains and therefore are of high industrial value. The relatively costly and time‐consuming wet‐lab experimental pipelines commonly used for novel enzyme discovery, highlight the necessity of agile in silico approaches to keep pace with the exponential growth of available sequencing data. In the present study, an in‐depth analysis of a tannery wastewater metagenome, including taxonomic and enzymatic profiling, was performed. Using sequence homology‐based screening methods and supervised machine learning‐based regression models aimed at prediction of lipases' pH and temperature optima, the metagenomic data set was screened for lipolytic enzymes, which led to the isolation of alkaline and highly thermophilic novel lipase. Moreover, MeTarEnz (metagenomic targeted enzyme miner) software was developed and made freely accessible (at https://cbb.ut.ac.ir/MeTarEnz) as a part of this study. MeTarEnz offers several functions to automate the process of targeted enzyme mining from high‐throughput sequencing data. This study highlights the competence of computational approaches in exploring vast biodiversity within environmental niches, while providing a set of practical in silico tools as well as a generalized methodology to facilitate the sequence‐based mining of biocatalysts.

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Expression and characterization of alkaline protease from the metagenomic library of tannery activated sludge

Cited by 31 publications

References 28 publications

Kinetics of casein hydrolysis by peptidase from Bacillus thuringiensis var. israelensis

Kinetics of casein hydrolysis by peptidase from Bacillus thuringiensis var. israelensis

Recovery and functional validation of hidden soil enzymes in metagenomic libraries

A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification

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