A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

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.

Rhodococcus strains as a good biotool for neutralizing pharmaceutical pollutants and obtaining therapeutically valuable products: Through the past into the future

Active pharmaceutical ingredients present a substantial risk when they reach the environment and drinking water sources. As a new type of dangerous pollutants with high chemical resistance and pronounced biological effects, they accumulate everywhere, often in significant concentrations (μg/L) in ecological environments, food chains, organs of farm animals and humans, and cause an intense response from the aquatic and soil microbiota. Rhodococcus spp. (Actinomycetia class), which occupy a dominant position in polluted ecosystems, stand out among other microorganisms with the greatest variety of degradable pollutants and participate in natural attenuation, are considered as active agents with high transforming and degrading impacts on pharmaceutical compounds. Many representatives of rhodococci are promising as unique sources of specific transforming enzymes, quorum quenching tools, natural products and novel antimicrobials, biosurfactants and nanostructures. The review presents the latest knowledge and current trends regarding the use of Rhodococcus spp. in the processes of pharmaceutical pollutants’ biodegradation, as well as in the fields of biocatalysis and biotechnology for the production of targeted pharmaceutical products. The current literature sources presented in the review can be helpful in future research programs aimed at promoting Rhodococcus spp. as potential biodegraders and biotransformers to control pharmaceutical pollution in the environment.

Getting the Most Out of Enzyme Cascades: Strategies to Optimize In Vitro Multi-Enzymatic Reactions

In vitro enzyme cascades possess great benefits, such as their synthetic capabilities for complex molecules, no need for intermediate isolation, and the shift of unfavorable equilibria towards the products. Their performance, however, can be impaired by, for example, destabilizing or inhibitory interactions between the cascade components or incongruous reaction conditions. The optimization of such systems is therefore often inevitable but not an easy task. Many parameters such as the design of the synthesis route, the choice of enzymes, reaction conditions, or process design can alter the performance of an in vitro enzymatic cascade. Many strategies to tackle this complex task exist, ranging from experimental to in silico approaches and combinations of both. This review collates examples of various optimization strategies and their success. The feasibility of optimization goals, the influence of certain parameters and the usage of algorithm-based optimizations are discussed.

Development of aldolase-based catalysts for the synthesis of organic chemicals

Aldol chemicals are synthesized by condensation reactions between the carbon units of ketones and aldehydes using aldolases. The efficient synthesis of diverse organic chemicals requires intrinsic modification of aldolases via engineering and design, as well as extrinsic modification through immobilization or combination with other catalysts. This review describes the development of aldolases, including their engineering and design, and the selection of desired aldolases using high-throughput screening, to enhance their catalytic properties and perform novel reactions. Aldolase-containing catalysts, which catalyze the aldol reaction combined with other enzymatic and/or chemical reactions, can efficiently synthesize diverse complex organic chemicals using inexpensive and simple materials as substrates. We also discuss the current challenges and emerging solutions for aldolase-based catalysts.

New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review

Today's environmental balance has been compromised by the unreasonable and sometimes dangerous actions committed by humans to maintain their dominance over the Earth's natural resources. As a result, oceans are contaminated by the different types of plastic trash, crude oil coming from mismanagement of transporting ships spilling it in the water, and air pollution due to increasing production of greenhouse gases, such as CO₂ and CH₄ etc., into the atmosphere. The lands, agricultural fields, and groundwater are also contaminated by the infamous chemicals viz., polycyclic aromatic hydrocarbons, pyrethroids pesticides, bisphenol-A, and dioxanes. Therefore, bioremediation might function as a convenient alternative to restore a clean environment. However, at present, the majority of bioremediation reports are limited to the natural capabilities of microbial enzymes. Synthetic biology with uncompromised supervision of ethical standards could help to outsmart nature's engineering, such as the CETCH cycle for improved CO₂ fixation. Additionally, a blend of synthetic biology with machine learning algorithms could expand the possibilities of bioengineering. This review summarized current state-of-the-art knowledge of the data-assisted enzyme redesigning to actively promote new research on important enzymes to ameliorate the environment.

Editorial Catalysts: Special Issue on Novel Enzyme and Whole-Cell Biocatalysts

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