Application of Response Surface Methodology and Plackett Burman Design assisted with Support Vector Machine for the Optimization of Nitrilase Production by Bacillus subtilis AGAB-2
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Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.
Abstract
Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.
참고문헌 (42)
- Scotti C, "Natural products containing the nitrile functional group and their biological activities", Nat. Prod. Commun (2022)[DOI]
- Dash RR, "Cyanide in industrial wastewaters and its removal: a review on biotreatment", J. Hazard. Mater, 163, pp.1~11 (2009)
- Egelkamp R, "Impact of nitriles on bacterial communities", Front. Environ. Sci, 7, pp.103~ (2019)
- Cowan D, "Biochemistry and biotechnology of mesophilic and thermophilic nitrile metabolizing enzymes", Extremophiles, 2, pp.207~216 (1998)
- Brady D, "Characterisation of nitrilase and nitrile hydratase biocatalytic systems", Appl. Microbiol. Biotechnol, 64, pp.76~85 (2004)
- Bhatt A, "Current status and future of nitrile catalysis using key nitrilases enzymes and their biotechnological impact", Open Biotechnol. J, 15, pp.71~81 (2021)
- Mason RL, "Statistical design and analysis of experiments: with applications to engineering and science" (2003), John Wiley & Sons
- Montgomery DC, "The use of statistical process control and design of experiments in product and process improvement", IIE Trans, 24, pp.4~17 (1992)
- Ghafari S, "Nitrate remediation in a novel upflow bio-electrochemical reactor (UBER) using palm shell activated carbon as cathode material", Electrochim. Acta, 54, pp.4164~4171 (2009)
- Simpson TW, "Metamodels for computer-based engineering design: survey and recommendations", Eng. Comput, 17, pp.129~150 (2001)
- Yusuf F, "Enhancing nitrilase production from Fusarium proliferatum using response surface methodology", SpringerPlus, 2, pp.290~ (2013)
- Bhilare KD, "Machine learning modelling for the ultrasonicationmediated disruption of recombinant E. coli for the efficient release of nitrilase", Ultrasonics, 98, pp.72~81 (2019)
- Hameed MM, "Prediction of high-strength concrete: high-order response surface methodology modeling approach", Eng. Comput, 38, pp.1655~1668 (2022)
- Cortes C, "Support-vector networks", Mach. Learn, 20, pp.273~297 (1995)
- Desai KM, "Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization:case study of fermentative production of scleroglucan", Biochem. Eng. J, 41, pp.266~273 (2008)
- Santoshkumar M, "A plate method for screening of bacteria capable of degrading aliphatic nitriles", J. Ind. Microbiol. Biotechnol, 37, pp.111~ (2010)
- Black GW, "A high-throughput screening method for determining the substrate scope of nitrilases", Chem. Commun, 51, pp.2660~2662 (2015)
- Sambrook J, "Molecular Cloning: A Laboratory Manual" (2001), CSH Laboratory Press
- Thompson JD, "Multiple sequence alignment using ClustalW and ClustalX", Curr. Protoc. Bioinformatics Chapter, 2 (2003)
- Xi L, "Cloning and characterization of a novel thermostable amidase, Xam, from Xinfangfangia sp. DLY26", Biotechnol. Lett, 43, pp.1395~1402 (2021)
- Singh RV, "Exploring a broad spectrum nitrilase from moderately halophilic bacterium Halomonas sp. IIIMB2797 isolated from saline lake", J. Basic Microbiol, 58, pp.867~874 (2018)
- Plackett RL, "The design of optimum multifactorial experiments", Biometrika, 33, pp.305~325 (1946)
- Shojae Chaeikar S, "PFW:polygonal fuzzy weighted - an SVM kernel for the classification of overlapping data groups", Electronics, 9, pp.615~ (2020)
- Meyer D, "The support vector machine under test", Neurocomputing, 55, pp.169~186 (2003)
- Banerjee A, "Enhancing the catalytic potential of nitrilase from Pseudomonas putida for stereoselective nitrile hydrolysis", Appl. Microbiol. Biotechnol, 72, pp.77~87 (2006)
- He YC, "Biosynthesis of benzoylformic acid from benzoyl cyanide by a newly isolated Rhodococcus sp. CCZU10-1 in toluene-water biphasic system", Bioresour. Technol, 115, pp.88~95 (2012)
- Antunes VLM, "A new analytical method to quantify ammonia in freshwater with a bulk acoustic wave sensor", Sensors, 22, pp.1528~ (2022)
- Santoshkumar M, "Utilization of aliphatic nitrile by Paracoccus sp. SKG isolated from chemical waste samples", Int. Biodeterior. Biodegradation, 65, pp.153~159 (2011)
- Ogunyemi AK, "Nitrilemetabolizing bacterial strains associated with municipal waste tips in the Lagos Metropolis, Nigeria", ~ (2019)
- Kao CM, "Enzymatic degradation of nitriles by Klebsiella oxytoca", Appl. Microbiol. Biotechnol, 71, pp.228~233 (2006)
- Montaño López J, "Physiological limitations and opportunities in microbial metabolic engineering", Nat. Rev. Microbiol, 20, pp.35~48 (2022)
- Thakur N, "An improved process for synthesis of nicotinic acid using hyper induced whole cell nitrilase of Gordonia terrae MTCC8139", 3 Biotech, 12, pp.303~ (2022)
- Duca D, "Characterization of a nitrilase and a nitrile hydratase from Pseudomonas sp. strain UW4 that converts indole-3-acetonitrile to indole-3-acetic acid", Appl. Environ. Microbiol, 80, pp.4640~4649 (2014)
- Yang C, "A new nitrilase-producing strain named Rhodobacter sphaeroides LHS-305: biocatalytic characterization and substrate specificity", Appl. Biochem. Biotechnol, 165, pp.1556~1567 (2011)
- Majou D, "Mechanisms of the bactericidal effects of nitrate and nitrite in cured meats", Meat Sci, 145, pp.273~284 (2018)
- Rajagopalan KV, "Competitive inhibition of enzyme activity by urea", J. Biol. Chem, 236, pp.1059~1065 (1961)
- Zheng YG, "Isolation, identification and characterization of Bacillus subtilis ZJB-063, a versatile nitrile-converting bacterium", Appl. Microbiol. Biotechnol, 77, pp.985~993 (2008)
- Almatawah QA, "Characterization of an inducible nitrilase from a thermophilic bacillus", Extremophiles, 3, pp.283~291 (1999)
- Chen J, "Biotransformation of p‐methoxyphenylacetonitrile into p‐methoxyphenylacetic acid by resting cells of Bacillus subtilis", Biotechnol. Appl. Biochem, 50, pp.147~153 (2008)
- Prajapati D, "Production, optimization, partial-purification and pyrolysis kinetic studies of exopolysaccharide from a native brown-rot fungi Fomitopsis meliae AGDP-2", Bioresour. Technol. Rep, 17, pp.100948~ (2022)
- Gopirajan PV, "Optimization of hydrothermal liquefaction process through machine learning approach: process conditions and oil yield", Biomass Convers. Biorefin, 13, pp.1213~1222 (2021)
- Rabiya R, "Artificial intelligence driven advanced optimization strategy vis-à-vis response surface optimization of production medium: Bacterial exopolysaccharide production as a case-study", Biochem. Eng. J, 178, pp.108271~ (2022)