Mining of camel rumen metagenome to identify novel alkali-thermostable xylanase capable of enhancing the recalcitrant lignocellulosic biomass conversion

Ariaeenejad, Shohreh; Maleki, Mohsen; Hosseini, Elaheh; Kavousi, Kaveh; Moosavi-Movahedi, Ali Akbar

doi:10.1016/j.biortech.2019.02.059

Cited by 43 publications

(18 citation statements)

References 47 publications

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“…Advancements in sequencing technologies and metagenomics have revolutionized our access to a wealth of sequence data from enzymes with possible industrial applications. In comparison with our previous two studies, in which xylanase enzymes from the metagenomic source were identified and characterized by experimental techniques (10,11) , TAXyl enabled the identification of more putative thermoactive xylanases and enhanced the extended exploration of the metagenomic data as well as validating our two previously characterized enzymes. This tool can be implemented to significantly reduce the number of potential candidates of xylanases with a specific thermal activity profile before engaging the wet-lab experiments.…”

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confidence: 58%

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A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

Shahraki

Farhadyar

Kavousi

et al. 2019

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Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose polysaccharide, which is present in lignocellulosic biomass. The optimum temperature of enzymes is the indicator of their thermal activity and is an essential factor to be considered when choosing an appropriate biocatalyst for a particular purpose. Therefore, insilico prediction of this enzymatic attribute is a significant cost and time-effective step in the effort to identify and characterize novel enzymes. The objective of this study was to develop an accurate computational method to predict the thermal activity status of xylanases from glycoside hydrolases families 10 and 11, the most prevalent known xylanase families. Here we present TAXyl (Thermal Activity Prediction for Xylanase), a new sequence-based machine learning method that has been trained using a selected combination of various physicochemical protein features. This ensemble of four supervised learning algorithms discriminates mesophilic, thermophilic, and hyper-thermophilic xylanases based on their optimum temperature with the process of soft-voting. TAXyl's performance was ultimately evaluated through multiple iterations of six-fold cross-validations, and it exhibited a mean accuracy of ~0.94, F1-score of ~0.91, and MCC of ~0.9. Additionally, the model was tested on previously unseen data and depicted relatively similar performance. To the best of our knowledge, this tool is the most accurate and practical prediction tool currently available and operating on this class of enzymes.TAXyl is freely accessible as a web-service at http://arimees.com/ and provides users with several features to facilitate the characterization of GH10 and GH11 xylanases.

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confidence: 58%

“…Therefore, metagenomics provides access to almost all the genetic material inside an 4 environmental sample. However, experimental functional annotation of newly identified genes is becoming a significant challenge (10,11). Utilizing an in-silico approach to address this problem can make a notable contribution.…”

Section: Introductionmentioning

confidence: 99%

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

Shahraki

Farhadyar

Kavousi

et al. 2019

Preprint

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“…PersiCel4 F(5'-TAATAGGCTAGCATGAAAAGAATTTTGATTTTGGC − 3') and PersiCel4 R(5'-TGATAG GTCGAC TCACTCTATGTCTCCGCGA − 3') primers which included NdeI and NotI restriction sites, respectively. PCR product was purified, digested with NheI and SalI and ligated into pET-28a, resulting recombinant plasmid pet28 [51,52].…”

Section: Methodsmentioning

confidence: 99%

“…Among the three types of cellulose-degrading enzymes, endo-β-1,4-glucanase is the crucial enzyme that first acts on the cellulose polymers' amorphous sites and randomly cleaves the internal β-1,4-bonds and releases smaller fragments of varying random lengths [6]. Due to the sequence similarities of the catalytic domains of endo-β-1,4-glucanase enzymes were classified into glycoside hydrolase (GH) families of 5,6,7,8,9,12,44,45,48,51 and 74 [7,8].…”

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A novel high performance metagenome-derived alkali-thermostable endo-β-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions

Ariaeenejad¹,

Sheykhabdolahzadeh²,

Maleki³

et al. 2020

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Background: Lignocellulosic biomass, is a great resource for the production of bio-energy and biobased material since it is largely abundant, inexpensive and renewable. The requirement of new energy sources has led to a wide search for novel effective enzymes to improve the exploitation of lignocellulose, among which the importance of thermostable and halotolerant cellulase enzymes with high pH performance is significant.Results: The primary aim of this study was to discover a novel alkali-thermostable endo-β-1,4glucanase from the sheep rumen metagenome. Using a multi-step in-silico analysis, primary candidates with desired properties were found and subjected to cloning, expression, and purification followed by functional and structural characterization. The enzymes' kinetic parameters, including V max , Km, and specific activity, were calculated. The PersiCel4 demonstrated its optimum activity at pH 8.5 and a temperature of 85°C and was able to retain more than 70% of its activity after 150 hours of storage at 85°C. Furthermore, this enzyme was able to maintain its catalytic activity in the presence of different concentrations of NaCl, MgCl 2 , CaCl 2 , and MnCl 2 . Our results showed that treatment with MnCl 2 could enhance the enzyme's activity by 89%. PersiCel4 was ultimately used for enzymatic hydrolysis of autoclave pretreated rice straw, the most abundant agricultural waste with rich cellulose content. In autoclave treated rice straw, enzymatic hydrolysis with the PersiCel4 increased the release of reducing sugar up to 260% after 72 hours in the harsh condition ( T= 85°C, pH = 8.5).Conclusion: Considering the urgent demand for stable cellulases that are operational on extreme temperature and pH conditions and due to several proposed distinctive characteristics of PersiCel4, it can be used in the harsh condition for bioconversion of lignocellulosic biomass. BackgroundLignocellulose is the principal constituent of the biomass and is the most abundant bio-renewable organic resource on earth and therefore is of significant interest to governments, researchers, and industries. Lignocellulosic biomass is composed of three biopolymers, hemicellulose, cellulose and lignin which, depending on the type of the biomass, are intermeshed and organized into complex

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“…The DNS was used to measure activity. In order to determine the optimum temperature of enzyme activity, enzyme solution in 10 mM phosphate buffer (pH 8) with substrate was incubated in the different temperature (30-90 • C) for 20 min and DNS was used to measure activity (Ariaeenejad et al, 2019(Ariaeenejad et al, , 2020b. For reporting, relative activity was considered as percentage of the highest activity.…”

Section: Identification Cloning Expression Purification and Charamentioning

confidence: 99%

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

et al. 2020

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Mining of camel rumen metagenome to identify novel alkali-thermostable xylanase capable of enhancing the recalcitrant lignocellulosic biomass conversion

Cited by 43 publications

References 47 publications

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

A novel high performance metagenome-derived alkali-thermostable endo-β-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

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