Comparative study on modeling by neural networks and response surface methodology for better prediction and optimization of fermentation parameters: Application on thermo-alkaline lipase production by Nocardiopsis sp. strain NRC/WN5

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

A thermostable organic solvent-tolerant lipase from Brevibacillus sp.: production and integrated downstream processing using an alcohol-salt-based aqueous two-phase system

Lipases are used for the synthesis of different compounds in the chemical, pharmaceutical, and food industries. Most of the reactions are carried out in non-aqueous media and often at elevated temperature, requiring the use of organic solvent-tolerant thermostable lipases. However, most known lipases are not stable in the presence of organic solvents and at elevated temperature. In this study, an organic solvent-tolerant thermostable lipase was obtained from Brevibacillus sp. SHI-160, a moderate thermophile isolated from a hot spring in the East African Rift Valley. The enzyme was optimally active at 65°C and retained over 90% of its activity after 1 h of incubation at 70°C. High lipase activity was measured in the pH range of 6.5 to 9.0 with an optimum pH of 8.5. The enzyme was stable in the presence of both polar and non-polar organic solvents. The stability of the enzyme in the presence of polar organic solvents allowed the development of an efficient downstream processing using an alcohol-salt-based aqueous two-phase system (ATPS). Thus, in the presence of 2% salt, over 98% of the enzyme partitioned to the alcohol phase. The ATPS-recovered enzyme was directly immobilized on a solid support through adsorption and successfully used to catalyze a transesterification reaction between paranitrophenyl palmitate and short-chain alcohols in non-aqueous media. This shows the potential of lipase SHI-160 to catalyze reactions in non-aqueous media for the synthesis of valuable compounds. The integrated approach developed for enzyme production and cheap and efficient downstream processing using ATPS could allow a significant reduction in enzyme production costs. The results also show the potential of extreme environments in the East African Rift Valley as sources of valuable microbial genetic resources for the isolation of novel lipases and other industrially important enzymes.

Sequential modelling for carbohydrate and bioethanol production from Chlorella saccharophila CCALA 258: a complementary experimental and theoretical approach for microalgal bioethanol production

Bioethanol production from microalgal biomass is an attractive concept, and theoretical methods by which bioenergy can be produced indicate saving in both time and efficiency. The aim of the present study was to investigate the efficiencies of carbohydrate and bioethanol production by Chlorella saccharophila CCALA 258 using experimental, semiempirical, and theoretical methods, such as response surface methods (RSMs) and an artificial neural network (ANN) through sequential modeling. In addition, the interactive response surface modeling for determining the optimum conditions for the variables was assessed. The results indicated that the maximum bioethanol concentration was 11.20 g/L using the RSM model and 11.17 g/L using the ANN model under optimum conditions of 6% (v/v %) substrate and 4% (v/v %) inoculum at 96-h fermentation, pH 6, and 40 °C. In addition, the value of the experimental data for carbohydrate concentration was 0.2510 g/g biomass at ANN with the maximums of 50% (v/v) wastewater concentration, 4% (m/m) hydrogen peroxide concentration, and 6000 U/mL enzyme activity. Finally, although the RSM model was more effective than the ANN model for predicting bioethanol concentration, the ANN model yielded more precise values than the RSM model for carbohydrate concentration.