Response surface methodology (RSM) and artificial neural networks (ANN) are considered the most efficient way for optimization and modeling studies to design and develop various biosimilars. The primary objective of this study was to create empirical modeling and optimization of media parameters for producing B. halotolerans VSH 09 lipase using RSM and ANN. One-factor-at-a-time (OFAT) analysis revealed that triacylglycerols hydrolyzed by lipase manifest substantial activity. The subsequent screening for best carbon, nitrogen, and inducer was performed using the Placket–Burman design (PBD). The statistically significant variables were further examined for their optimum level using Box–Behnken design (BBD). The lipase production was optimized (26.04 IU/ml) under the ideal molasses (2.5%), peptone (2%), and salt (0.1% CaCO3, 0.1% (NH4)2SO4, and 0.1% MgSO4.7H2O). Both models revealed impeccable predictions; however, more interestingly, it was evaluated that ANN outperforms the RSM regarding data fitting and estimation capabilities.
Beyond its potential for biofuel production, Pseudomonas putida’s capability to metabolize lignin and other lignocellulosic materials earmarks it as a pivotal candidate for engineering to yield diverse value-added chemicals, thereby challenging traditional petrochemical approaches. Recognizing the inherent environmental, economic, and societal advantages, amplifying role of P. putida in industrial applications becomes imperative. In this context, our study focused on characterizing a comprehensive set of promoters and ribosome binding site tailored for P. putida, spanning a broad spectrum of activities. By leveraging these genetic tools, we adeptly balanced the heterologous mevalonate (MVA) pathway flux within P. putida. As a culmination of our efforts, the optimal MVA-producing strains were identified, achieving a remarkable yield of 5 g/LMVA in a 5 L fed-batch fermenter, marking the highest reported yield in Pseudomonas to date. This research not only provides valuable genetic tools for future engineering studies with P. putida, but also accentuates P. putida’s potential in synthetic biology and its promise for sustainable chemical production.
Xylanolytic enzyme can successfully and efficiently breakdown xylans to fermentable carbohydrates to create useful chemicals or fuels for use in a range of industrial sectors such as food, animal feed, biofuel, pulp, and paper. In the current investigation, molecular modeling and docking analysis were performed using xylanase enzymes from 17 different fungal species with 5 substrates such as d-xylose, xylobiose, xylotriose, xylotetrose, and xylopentose to identify the active site residues and binding affinity of those complexes. Among all, 4 fungal species such as Aspergillus niger, Orpinomyces sp., Neocallimastix patriciarum, and Botrytis fuckeliana showed the maximum molecular interaction and binding affinity toward different substrate, i.e., − 5.4 (d-xylose), − 6.7 (xylobiose), − 8.2 (xylotriose), − 8.1 (Xylotetrose), and − 5 (xylopentose) kcal/mol. These four complexes were used for the simulation studies to determine its constancy of the enzyme–substrate complexes. Thus, Botrytis fuckeliana with xylobiose ligand and N. patriciarum with xylotetrose can substantially involve in xylan degradation toward bioethanol production from lignocellulosic biomass.