Terkelsen Carstensen (eranation2)

bers were chosen as the support material for enzyme immobilization. By using this approach, the carbonic anhydrase enzyme could easily be used in the industrial area by cost-effective advantageous aspects. After applying the framework, we get a new biocatalysis application platform for carbonic anhydrase enzyme. Electrospun nanofibers were chosen as the support material for enzyme immobilization. By using this approach, the carbonic anhydrase enzyme could easily be used in the industrial area by cost-effective advantageous aspects. The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment information. Since homologous details not available for all proteins. Therefore, it is necessary to predict the protein secondary structure from single sequences. Protein secondary structure predicted from their primary sequences using n-gram word embedding and deep recurrent neural network. Protein secondary structure depends on local and long-range neighbor residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures variable-length character n-gram words. An embedding vector represents these variable-length character n-gram words. Further, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences. The proposed model evaluates on three public datasets ss.txt, RS126, and CASP9. The model shows the Q3 accuracy of 92.57%, 86.48%, and 89.66% for ss.txt, RS126, and CASP9. The proposed model performance compares with state-of-the-art methods available in the literature. After a comparative analysis, it observed that the proposed model performs better than state-of-the-art methods. The proposed model performance compares with state-of-the-art methods available in the literature. After a comparative analysis, it observed that the proposed model performs better than state-of-the-art methods. Physical parameters like pH and temperature play a major role in the design of an industrial enzymatic process. Enzyme stability and activity are greatly influenced by these parameters; hence optimization and control of these parameters becomes a key point in determining the economic feasibility of the process. This study was taken up with the objective to optimize physical parameters for maximum stability and activity of xylose reductase from D. nepalensis NCYC 3413 through separate and simultaneous optimization studies and comparison thereof. Effects of pH and temperature on the activity and stability of xylose reductase from Debaryomyces nepalensis NCYC 3413 were investigated by enzyme assays and independent variables were optimised using surface response methodology. Enzyme activity and stability were optimised separately and concurrently to decipher the appropriate conditions. Optimized conditions of pH and temperature for xylose reductase activity were determined to be 7.1 and 27 ℃ respectively, with predicted responses of specific activity (72.3 U/mg) and half-life time (566 min). The experimental values (specific activity 50.2 U/mg, half-life time 818 min) were on par with predicted values indicating the significance of the model. Simultaneous optimization of xylose reductase activity and stability using statistical methods is effective as compared to optimisation of the parameters separately. Simultaneous optimization of xylose reductase activity and stability using statistical methods is effective as compared to optimisation of the parameters separately. Genus Berberis (family Berberidaceae), which contains about 650 species and 17 genera worldwide, has been used in folklore and various traditio