Synergistic effects of ternary mixture formulation and process parameters optimization in a sequential approach for enhanced L-asparaginase production using agro-industrial wastes

Sharma, Deepankar; Mishra, Abha

doi:10.1007/s11356-023-26977-4

Cited by 4 publications

(1 citation statement)

References 59 publications

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“…Currently, three different approaches are used for enzyme bioengineering: semi-rational, directed evolution, and rational design, depending on the level of information available about the sequence, structure and function of the target enzyme [23]. To improve the various properties of L-ASNases, researchers have used rational design and other molecular approaches [16,[24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Rational design methods have been useful in predicting and modifying enzyme structures.…”

Section: Of 60mentioning

confidence: 99%

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Shishparenok,

Gladilina,

Zhdanov

2023

IJMS

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Genetic engineering for heterologous expression has advanced in recent years. Model systems such as Escherichia coli, Bacillus subtilis and Pichia pastoris are often used as host microorganisms for the enzymatic production of L-asparaginase, an enzyme widely used in the clinic for the treatment of leukemia and in bakeries for the reduction of acrylamide. Newly developed recombinant L-asparaginase (L-ASNase) may have a low affinity for asparagine, reduced catalytic activity, low stability, and increased glutaminase activity or immunogenicity. Some successful commercial preparations of L-ASNase are now available. Therefore, obtaining novel L-ASNases with improved properties suitable for food or clinical applications remains a challenge. The combination of rational design and/or directed evolution and heterologous expression has been used to create enzymes with desired characteristics. Computer design, combined with other methods, could make it possible to generate mutant libraries of novel L-ASNases without costly and time-consuming efforts. In this review, we summarize the strategies and approaches for obtaining and developing L-ASNase with improved properties.

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Section: Of 60mentioning

confidence: 99%

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Shishparenok,

Gladilina,

Zhdanov

2023

IJMS

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Agro-industrial Waste Utilization, Medium Optimization, and Immobilization of Economically Feasible Halo-Alkaline Protease Produced by Nocardiopsis dassonvillei Strain VCS-4

Majithiya,

Gohel

2024

Appl Biochem Biotechnol

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Artificial intelligence-based optimization for extracellular L-glutaminase free L-asparaginase production by Streptomyces violaceoruber under solid state fermentation conditions

El-Naggar,

Hamouda,

Elshafey

2024

Sci Rep

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The bacterial L-asparaginase is a highly effective chemotherapeutic drug and a cornerstone of treatment protocols used for treatment the acute lymphoblastic leukemia in pediatric oncology. A potential actinomycete isolate, Streptomyces sp. strain NEAE-99, produces glutaminase-free L-asparaginase was isolated from a soil sample. This potential strain was identified as S. violaceoruber strain NEAE-99. The central composite design (CCD) approach was utilized for finding the optimal values for four variables including the mixture of soybean and wheat bran in a 1:1 ratio (w/w), the concentrations of dextrose, L-asparagine, and potassium nitrate under solid state fermentation conditions. Through the use of an artificial neural network (ANN), the production of L-asparaginase by S. violaceoruber has been investigated, validated, and predicted in comparison to CCD. It was found that the optimal predicted conditions for maximum L-asparaginase production (216.19 U/gds) were 8.46 g/250 mL Erlenmeyer flask of soybean and wheat bran mixture in a 1:1 ratio (w/w), 2.2 g/L of dextrose, 18.97 g/L of L-asparagine, and 1.34 g/L of KNO3. The experimental results (207.55 U/gds) closely approximated the theoretical values (216.19 U/gds), as evidenced by the validation. This suggests that the ANN exhibited a high degree of precision and predictive capability.

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Synergistic effects of ternary mixture formulation and process parameters optimization in a sequential approach for enhanced L-asparaginase production using agro-industrial wastes

Cited by 4 publications

References 59 publications

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Agro-industrial Waste Utilization, Medium Optimization, and Immobilization of Economically Feasible Halo-Alkaline Protease Produced by Nocardiopsis dassonvillei Strain VCS-4

Artificial intelligence-based optimization for extracellular L-glutaminase free L-asparaginase production by Streptomyces violaceoruber under solid state fermentation conditions

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