Background
Lignocellulosic biomass, is a great resource for the production of bio-energy and bio-based 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-1,4-glucanase from the sheep rumen metagenome. At first, the multi-step in-silico screening approach was utilized to find primary candidate enzymes with superior properties. Among the computationally selected candidates, PersiCel4 was found and subjected to cloning, expression, and purification followed by functional and structural characterization. The enzymes’ kinetic parameters, including Vmax, 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 h of storage at 85 °C. Furthermore, this enzyme was able to maintain its catalytic activity in the presence of different concentrations of NaCl and several metal ions contains Mg2+, Mn2+, Cu2+, Fe2+ and Ca2+. Our results showed that treatment with MnCl2 could enhance the enzyme’s activity by 78%. 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 h 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.
Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time‐effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence‐based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non‐thermophilic, thermophilic, and hyper‐thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross‐validations as well as holdout tests, and it is freely accessible as a web‐service at arimees.com.
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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