Optimization of process parameters for improved chitinase activity from Thermomyces sp. by using artificial neural network and genetic algorithm

Engineering Bacterial Chitinases for Industrial Application: From Protein Engineering to Bacterial Strains Mutation! A Comprehensive Review of Physical, Molecular, and Computational Approaches

Bacterial chitinases are integral in breaking down chitin, the natural polymer in crustacean and insect exoskeletons. Their increasing utilization across various sectors such as agriculture, waste management, biotechnology, food processing, and pharmaceutical industries highlights their significance as biocatalysts. The current review investigates various scientific strategies to maximize the efficiency and production of bacterial chitinases for industrial use. Our goal is to optimize the heterologous production process using physical, molecular, and computational tools. Physical methods focus on isolating, purifying, and characterizing chitinases from various sources to ensure optimal conditions for maximum enzyme activity. Molecular techniques involve gene cloning, site-directed mutation, and CRISPR-Cas9 gene editing as an approach for creating chitinases with improved catalytic activity, substrate specificity, and stability. Computational approaches use molecular modeling, docking, and simulation techniques to accurately predict enzyme-substrate interactions and enhance chitinase variants' design. Integrating multidisciplinary strategies enables the development of highly efficient chitinases tailored for specific industrial applications. This review summarizes current knowledge and advances in chitinase engineering to serve as an indispensable guideline for researchers and industrialists seeking to optimize chitinase production for various uses.

Response Surface Methodology-Based Optimization of the Chitinolytic Activity of Burkholderia contaminans Strain 614 Exerting Biological Control against Phytopathogenic Fungi

As part of the development of alternative and environmentally friendly control against phytopathogenic fungi, Burkholderia cepacia could be a useful species notably via the generation of hydrolytic enzymes like chitinases, which can act as a biological control agent. Here, a Burkholderia contaminans S614 strain exhibiting chitinase activity was isolated from a soil in southern Tunisia. Then, response surface methodology (RSM) with a central composite design (CCD) was used to assess the impact of five factors (colloidal chitin, magnesium sulfate, dipotassium phosphate, yeast extract, and ammonium sulfate) on chitinase activity. B. contaminans strain 614 growing in the optimized medium showed up to a 3-fold higher chitinase activity. This enzyme was identified as beta-N-acetylhexosaminidase (90.1 kDa) based on its peptide sequences, which showed high similarity to those of Burkholderia lata strain 383. Furthermore, this chitinase significantly inhibited the growth of two phytopathogenic fungi: Botrytis cinerea M5 and Phoma medicaginis Ph8. Interestingly, a crude enzyme from strain S614 was effective in reducing P. medicaginis damage on detached leaves of Medicago truncatula. Overall, our data provide strong arguments for the agricultural and biotechnological potential of strain S614 in the context of developing biocontrol approaches.

Response surface methodology based optimization of keratinase from Bacillus velezensis strain ZBE1 and nanoparticle synthesis, biological and molecular characterization

The increasing demands of keratinases for biodegradation of recalcitrant keratinaceous waste like chicken feathers has lead to research on newer potential bacterial keratinases to produce high-value products with biological activities. The present study reports a novel keratinolytic bacterium Bacillus velezensis strain ZBE1 isolated from deep forest soil of Western Ghats of Karnataka, which possessed efficient feather keratin degradation capability and induced keratinase production. Production kinetics depicts maximum keratinase production (11.65 U/mL) on 4th day with protein concentration of 0.61 mg/mL. Effect of various physico-chemical factors such as, inoculum size, metal ions, carbon and nitrogen sources, pH and temperature influencing keratinase production were optimized and 3.74 folds enhancement was evidenced through response surface methodology. Silver (AgNP) and zinc oxide (ZnONP) nanoparticles with keratin hydrolysate produced from chicken feathers by the action of keratinase were synthesized and verified with UV-Visible spectroscopy that revealed biological activities like, antibacterial action against Bacillus cereus and Escherichia coli. AgNP and ZnONP also showed potential antioxidant activities through radical scavenging activities by ABTS and DPPH. AgNP and ZnONP revealed cytotoxic effect against MCF-7 breast cancer cell lines with IC50 of 5.47 µg/ml and 62.26 µg/ml respectively. Characterizations of nanoparticles were carried out by Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray, X-ray diffraction, thermogravimetric analysis and atomic force microscopy analysis to elucidate the thermostability, structure and surface attributes. The study suggests the prospective applications of keratinase to trigger the production of bioactive value-added products and significant application in nanotechnology in biomedicine.

Biotransformation of grease waste into fatty acid by Penicillium chrysogenum SNP5 through media engineering and artificial neural network

Degradation of grease waste remains a challenging task. Current work deals with the biotransformation of grease waste into fatty acids under submerged fermentation using Penicillium chrysogenum SNP5 through media formulation and artificial neural network (ANN). Fermentation media was formulated to ameliorate the uptake of hydrocarbon by enhancing alkane hydroxylase (AlkB) activity, extracellular release of fatty acids and inhibiting beta-oxidation of fatty acid by regulating transketolase. Further, the process parameters of fermentation were optimized through Artificial Neural Network (ANN) using three critical variables viz; inoculum size (spores/ml), pH, and incubation time (days) while media engineering was done with the optimal supplementation of various medium components such as glucose, YPD, MnSO₄, tetrahydrobiopterin (THB) and phloretin. The maximum conversion of 66.5% of grease waste into fatty acid was achieved at optimum conditions: inoculums size 3.36 × 10⁷ spores/ml, incubation time 11.5 days, pH 7.2 along with formulated media composed of 1% grease in czapek-dox medium supplemented with 55.5 mM glucose, 0.5% YPD, 16.6 mM hexadecane, 1 mM MnSO₄, 1 mM THB, and 1 mM phloretin. The presence of long-chain fatty acids in purified extracts such as oleic acid and octadecanoic acid as end products has valued the evolved process as another source of alternative fuel.

Overproduction of a thermo-stable halo-alkaline protease on agro-waste-based optimized medium through alternate combinatorial random mutagenesis of Stenotrophomonas acidaminiphila

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Alternate combinatorial random mutagenesis selected a protease high-yielding mutant.

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Medium optimization led to 25.55-fold raise in specific protease yield in bioreactor.

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20% PEG-1500/Na+ 15% citrate recovered 74% activity yield with 52.55 purity.

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Activity was retained at elevated physicochemical levels but inhibited by PMSF.

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Keratinolytic and blood-stain removal activities confer industrial potential on protease.

A strain of Stenotrophomonas acidaminiphila, isolated from fermenting bean-processing wastewater, produced alkaline protease in pretreated cassava waste-stream, but with low yield. Strain improvement by alternate combinatorial random mutagenesis and bioprocess optimization using comparative statistical and neural network methods enhanced yield by 17.8-fold in mutant kGy-04-UV-25. Kinetics of production by selected mutant modeled by logistic and modified Gompertz functions revealed higher specific growth rate in mutant than in the parent strain, likewise volumetric and specific productivities. Purification by PEG/Na⁺ citrate aqueous two-phase system recovered 73.87% yield and 52.55-fold of protease. Its activity was stable at 5–35% NaCl, 45–75°C, and was significantly enhanced by 1–15 mM sodium dodecyl sulfate (SDS). The protease was inhibited by low concentrations of phenyl-methyl-sulfonyl fluoride but was activated by 1–5 mM Mn²⁺ suggesting a manganese-dependent serine‑protease. The 45.7 kDa thermo-halo-stable alkaline protease demonstrated keratinolytic and blood-stain removal potentials showing prospects in textile and detergent industries, respectively.

Comparative study of response surface methodology and artificial neural network for optimization of process parameters for synthesis of gold nanoparticles by Desmostachya bipinnata extract

Green synthesis of nanoparticles has gained attention due to its eco-friendly and sustainable approach to synthesize nanoparticles at a reduced cost. Artificial neural network (ANN) and response surface model (RSM) are important to reduce experimental efforts in nanoparticle synthesis. In this work, optimization of gold nanoparticle synthesis by Desmostachya bipinnata extract was performed using the volume of plant extract, concentration of auric chloride, reaction time, pH, and temperature as process parameters, and the output was absorbance. The experimental design was obtained from RSM and the model was optimized further using ANN. Thirty-two experimental runs generated by RSM were performed and the results obtained experimentally were compared with those generated by RSM and ANN. Different algorithms of ANN were tested to obtain the best one. The optimization studies resulted in a maximum response for 20^th run with 15 ml, 2.5 mM, 45 min, 7, and 40 °C as parameters. Optimized input parameters obtained by RSM were 10 ml, 2 mM, 30 min, 6, and 30 °C. The formation of gold nanoparticles was confirmed by UV spectroscopy, XRD, and SEM. Different algorithms of ANN, such as leven marquardt, scaled conjugate gradient, and bayesian network were used. Leven marquardt algorithm was found to be the most suitable algorithm for the current study.

Optimization of fermentation medium and conditions for enhancing valinomycin production by Streptomyces sp. ZJUT-IFE-354

Valinomycin is a cyclodepsipeptide antibiotic with a broad spectrum of biological activities, such as antiviral, antitumor, and antifungal activities. However, the low yield of valinomycin often limits its applications in medicine, agriculture, and industry. In our previous report, Streptomyces sp. ZJUT-IFE-354 was identified as a high-yielding strain of valinomycin. In this study, Plackett-Burman design (PBD) and response surface methodology (RSM) were used to optimize components of medium. The optimal medium contained 31 g/L glucose, 22 g/L soybean meal, and 1.6 g/L K₂HPO₄·3H₂O, which could generate 262.47 ± 4.28 mg/L of valinomycin. Then, the culture conditions were optimized by a one-factor-at-a-time (OFAT) approach. The optimal conditions for the strain included a seed age of 24 h, an inoculum size of 8% (v/v), an incubation temperature of 28 °C, an initial pH of 7.2, an elicitor of 0.1% Bacillus cereus feeding at 24 h cultivation, and the feeding of 0.6% _L-valine at 36 h cultivation. The final valinomycin production increased to 457.23 ± 9.52 mg/L, which was the highest yield ever reported. It highlights that RSM and OFAT may be efficient methods to enhance valinomycin production by Streptomyces sp. ZJUT-IFE-354.

Enhanced production of alkane hydroxylase from Penicillium chrysogenum SNP5 (MTCC13144) through feed-forward neural network and genetic algorithm

Alkane hydroxylase (AlkB), a membrane-bound enzyme has high industrial demand; however, its economical production remains challenging due to its intrinsic nature and co-factor dependency. In the current study, various critical process parameters for optimum production of AlkB have been optimized through feed forward neural network (FFNN) and genetic algorithm (GA) models using Penicillium chrysogenum SNP5 (MTCC13144). AlkB specific activity under preliminary un-optimized conditions i.e., 1% hexadecane, 7.4 pH, 11 days incubation time, 28 °C incubation temperature and 1 ml of inoculum size was 100 U/mg. ‘One variable at a time’ (OVAT) strategy was used to identify optimum physicochemical parameters and then its output data was fed to develop a model of FFNN with ‘6-12-1’ topology. Outputs of FFNN were further optimized through GA to minimize errors and intensify search level. This has provided superior predictive performances with 0.053 U/mg overall mean absolute percentage error (MAPE), 6.801 U/mg root mean square errors (RMSE), and 0.987 overall correlation coefficient (R). The AlkB specific activity improved by 3.5-fold, i.e., from 100 U/mg under preliminary un-optimized conditions to 351.32 U/mg under optimum physicochemical conditions obtained through FFNN-GA hybrid method, i.e., hexadecane (carbon source): 1.56% v/v, FeSO₄: 0.63 mM, incubation temperature: 27.40 °C, pH: 7.38, incubation time: 12.35 days and inoculums size: 1.33 ml. The developed process would be a stepping stone to fulfill the high industrial demands of  Alkane hydroxylase.

Risk Prediction by Using Artificial Neural Network in Global Software Development

The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today's world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

Purification and characterization of chitinase produced by thermophilic fungi Thermomyces lanuginosus

BACKGROUND

In the past few years, the production of shrimp shell waste from the seafood processing industries has confronted a significant surge. Furthermore, insignificant dumping of waste has dangerous effects on both nature and human well-being. This marine waste contains a huge quantity of chitin which has several applications in different fields. The chitinase enzyme can achieve degradation of chitin, and the chitin itself can be used as the substrate as well for production of chitinase. In the current study, the chitinase enzyme was produced by Thermomyces lanuginosus. The extracellular chitinase was purified from crude extract using ammonium sulfate precipitation followed by DEAE-cellulose ion-exchange chromatography and Sephadex G-100 gel filtration chromatography. The stability and activity of chitinase with different pH, temperature, different times for a reaction, in the presence of different metal ions, and different concentration of enzyme and substrate were analyzed.

RESULT

The chitinase activity was found to be highest at pH 6.5, 50 °C, and 60 min after the reaction began. and the chitinase showed the highest activity and stability in the presence of β-mercaptoethanol (ME). The SDS-PAGE of denatured purified chitinase showed a protein band of 18 kDa.

CONCLUSION

The characterization study concludes that Cu²⁺, Hg²⁺, and EDTA have an inhibitory effect on chitinase activity, whereas β-ME acts as an activator for chitinase activity. The utilization of chitin to produce chitinase and the degradation of chitin using that chitinase enzyme would be an opportunity for bioremediation of shrimp shell waste.

Green synthesis of bioemulsifier and exopolysaccharides by Brevibacillus borstelensis and process parameters optimization using response surface model, genetic algorithm and NSGA

Bioemulsifier and exopolysaccharides are industrially important biomolecules produced by microorganisms using green technology. They have applications in food, biomedical, pharmaceutical and cosmetic industries and hence high yield of both products becomes necessary. The current study showed that Brevibacillus borstelensis has a potential to produce bioemulsifier and exopolysaccharide simultaneously but yield of both products is limited. In this study, CCD-RSM has been used as experimental design to increase concentration of both products. Concentrations of glucose, monosodium glutamate, yeast extract and magnesium sulphate were process variables and concentrations of bioemulsifiers, exopolysaccharides and biomass were responses. 30 experimental runs were performed and the models from CCD were optimized by genetic algorithm and NSGA. The results from modelling and optimization techniques were compared along with validation of models. The predicted values from optimization techniques were better than experimental values. The study concluded that NSGA is most suitable to optimize multiple responses simultaneously when compared to RSM and genetic algorithm. The optimum conditions for production were 22 g/l glucose, 14 g/l monosodium glutamate, 6 g/l yeast extract and 0.6 g/l magnesium sulphate with maximum yield of 6.1, 17.6 and 2.8 g/l bioemulsifier, exopolysaccharide and biomass, respectively. Knowledge of optimum concentrations of carbon and nitrogen source will help to utilize industrial and agricultural wastes for production of both products. They have applications in environmental bioremediation by clearing oil spills. Bioemulsifiers also help in heavy metal removal from hazardous waste. Hence this will result in environmental bioremediation by utilization of wastes by employing products generated from wastes.