Biochemical characterization and molecular modeling of a unique lipase fromStaphylococcus arlettaeJPBW-1

Biodiesel Production from Brassica juncea Using Oleaginous Yeast

The present study explores the potential of Brassica juncea as a low-cost substrate for biodiesel production through the growth of oleaginous yeast. Firstly, the selected lignocellulosic biomass, i.e., Brassica juncea, was thermochemically pretreated using dilute sodium hydroxide. Optimization of thermochemical pretreatment resulted in significant removal of lignin and hemicellulose with 8.4% increase in cellulose content. Further, the sugar hydrolysate of pretreated biomass was used as a substrate for the growth of selected oleaginous yeast (Cryptococcus sp. MTCC 5455). Lipid and biomass production was optimized using central composite design (CCD) based on response surface methodology (RSM). Maximum biomass and lipid content of 32.50 g/L and 11.05 g/L, respectively, was obtained at 30 °C temperature, pH 6.0, and after 5 days of incubation. The oleaginous yeast lipid was further transesterified using immobilized lipase. The highest fatty acid methyl ester 15% FAME yield was obtained after 10 h of enzymatic reaction. Next, the results of specific gravity, viscosity, flash points, and cloud point of obtained biodiesels were conformed to the ASTM D975 standard. Overall, the present study put forth the cost-effective approach for lignocellulosic biomass-based oleaginous lipid production toward the green synthesis of biodiesel.

Invasive weed optimization coupled biomass and product dynamics of tuning soybean husk towards lipolytic enzyme

Waste to the product approach was proposed for tuning environ-threat soybean husk towards lipolytic enzyme by integrating the invasive weed optimization with biomass and product dynamics study. The invasive weed optimization constitutes based on the non-linear regression model results in a 47 % enhancement in lipolytic enzyme using the optimization parameters of 7% Sigma Final, 9% exponent; Smax of 5 with a population size of 35 and Max. generations of 99. The biomass dynamic study showcases the dynamic parameters of 0.0239 µmax, 8.17 XLimst and 0.852 RFin values. The product dynamic studies reveal the kinetic parameters of kst, kdiv, PFin, which seem to be equal to -0.0338, 0.0896 and 68.1, respectively. Overall, the present study put forth the zero-waste (soybean husk) to the product (lipolytic enzyme) approach by introducing the novel "Invasive Weed Optimization" coupled with "Biomass and product dynamics" to the bioprocessing field.

Realm of Thermoalkaline Lipases in Bioprocess Commodities

For decades, microbial lipases are notably used as biocatalysts and efficiently catalyze various processes in many important industries. Biocatalysts are less corrosive to industrial equipment and due to their substrate specificity and regioselectivity they produced less harmful waste which promotes environmental sustainability. At present, thermostable and alkaline tolerant lipases have gained enormous interest as biocatalyst due to their stability and robustness under high temperature and alkaline environment operation. Several characteristics of the thermostable and alkaline tolerant lipases are discussed. Their molecular weight and resistance towards a range of temperature, pH, metal, and surfactants are compared. Their industrial applications in biodiesel, biodetergents, biodegreasing, and other types of bioconversions are also described. This review also discusses the advance of fermentation process for thermostable and alkaline tolerant lipases production focusing on the process development in microorganism selection and strain improvement, culture medium optimization via several optimization techniques (i.e., one-factor-at-a-time, surface response methodology, and artificial neural network), and other fermentation parameters (i.e., inoculums size, temperature, pH, agitation rate, dissolved oxygen tension (DOT), and aeration rate). Two common fermentation techniques for thermostable and alkaline tolerant lipases production which are solid-state and submerged fermentation methods are compared and discussed. Recent optimization approaches using evolutionary algorithms (i.e., Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization) are also highlighted in this article.