Optimization of nutrient medium composition for the production of lipase from waste cooking oil using response surface methodology and artificial neural networks

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.

Application of the novel manta-ray foraging algorithm to optimize acidic peptidase production in solid-state fermentation using binary agro-industrial waste

Peptidases, which constitute about 20% of the global enzyme market, have found applications in detergent, food and pharmaceutical industries, and could be produced on a large scale using low-cost agro-industrial waste. An acidophilic Bacillus cereus strain produced acidic peptidase on binary-agro-industrial waste comprising yam peels and fish processing waste at pH 4.5 with high catalytic activity. A five-variable central composite rotatable design of a response surface methodology was used to model bioprocess conditions for improved peptidase production in solid-state fermentation. Data generated was leveraged as the basis for applying the novel Manta-ray foraging optimization-linked feed-forward artificial neural network to predict bioprocess conditions optimally. Results obtained from the optimization experiments revealed a significant coefficient of determination of 0.9885 with low-performance error. The bioprocess predicted a peptidase activity of 1035.32 U/mL under optimized conditions set as 54.8 g/100 g yam peels, 23.85 g/100 g fish waste, 0.31 g/100 g CaCl2, 47.54% (v/w) moisture content, and pH 2. Peptidase activity was improved 5-fold, and was stable for 240 min between pH 2.5 and 3.5. Michaelis-Menten kinetics revealed a Km of 0.119 mM and a catalytic efficiency of 45462.19 mM-1 min-1. The bioprocess holds promise for sustainable enzyme-driven applications.

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.