Harnessing Bacillus inaquosorum AGSP2 for Enhancing ω-Transaminase Production Through Classical and AI-Supported Statistical Design

Harnessing Bacillus inaquosorum AGSP2 for Enhancing ω-Transaminase Production Through Classical and AI-Supported Statistical Design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Harnessing Bacillus inaquosorum AGSP2 for Enhancing ω-Transaminase Production Through Classical and AI-Supported Statistical Design Shreya Pandya, Urvish Chhaya, Akshaya Gupte This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7609083/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 19 You are reading this latest preprint version Abstract ω-transaminases are PLP-dependent enzymes able to catalyze the synthesis of various chiral amines, an important building block in the pharmaceutical industry. Here we describe the isolation and optimization of a wild-type strain of Bacillus species ( Bacillus inaquosorum AGSP2) isolated from an industrially polluted site of Amlakhadi, Gujarat, India. The isolate AGSP2 is a chiral amine producer and can synthesize a wide variety of compounds (Aldehydes, ketones, amino acids, and amines). The optimization was performed using the OFAT method (One Factor at a Time), followed by RSM-CCD (Response Surface Methodology-Central Composite Design), with further validation by an AI (Artificial Intelligence) tool, SVM (Support Vector Machine). The media optimized by statistical means were designated as Modified Luria Bertani (MLB) medium, which contains fructose, NaCl, yeast extract, and peptone supplemented with α-MBA. An overall 2.8-fold increase in transaminase production was observed with an enzyme activity of 6121.88 ± 42 U/ml. The other optimized parameters were temperature, pH, agitation speed, and inoculum size. AGSP2 is an (S) - selective ω-transaminase producer and has synthesised acetophenone using (S) -α-Methylbenzylamine with a 64.35% conversion and 51% of enantiomeric excess. Biological sciences/Biochemistry Biological sciences/Biotechnology Physical sciences/Chemistry Biological sciences/Microbiology Chiral compounds biocatalyst Bacillus inaquosorum RSM-CCD Artificial Intelligence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Chiral amines are an important building block for synthesizing several bioactive compounds and active pharmaceutical ingredients (APIs). Chiral amines are most well-recognized in pharmaceutical industries for imparting desired biological activity to various chemical entities 1 . Almost 50% of the drugs produced annually are chiral, thus creating a huge market 2 . Different strategies for producing chiral amines via biocatalytic and chemical transformations have been developed. The chemical approaches used for the enantioselective synthesis of chiral amines suffer from various drawbacks such as harsh reaction conditions, low efficiency, selectivity, and a high environmental impact 3 . In the last two decades, many investigations have been carried out for the development of alternative biocatalytic routes that are selective, efficient, and eco-friendly. With mild reaction conditions and high stereospecificity of enzymatic reactions, biocatalysis is increasingly considered an alternative route to chemical synthesis. Different enzymes, such as amine dehydrogenase, oxidoreductase, hydrolases, ammonia lyase, imine reductase, and transaminases, have been used successfully. Among these enzymes, ω-transaminase has gained attention for preparing enantiopure products capable of stereoselective amination of ketones. Transaminase or aminotransferase (E.C. no 2.6.1.X), a chiral amine producer, is emerging as a promising biocatalyst. Transaminase belongs to the family of transferase enzymes and is PLP (Pyridoxal-5-Phosphate)- dependent. It follows a ping-pong bi-bi mechanism and catalyzes the two-step reaction of deamination and amination of amino acids or amines into keto compounds and vice versa using PLP as a cofactor 4 . The complete mechanism involves the formation of intermediates such as external aldimine, quinoid, ketamine, and PMP (Pyridoxamine-5’-phosphate) and simultaneous release of the newly formed amines or amino acid and a ketone compound. Transaminase is being used to produce several Active Pharmaceutical Ingredients (APIs), which are the essential moiety of many drugs. These enzymes can be explored for the synthesis of many drugs such as ramatroban, sitagliptin, and sacubitril, which help cure various diseases 5 . Transaminases can be classified into different categories and based on different criteria. Mainly, there are two types: α-transaminase and ω-transaminase. ω-transaminase has wide applications in industries because of its wide substrate specificity. The ω-transaminase can react with amino acids, amines, ketones, and aldehydes 6 . Being an extremely enantioselective and stereoselective transaminase can synthesize optically active compounds (S/R enantiomers). Apart from having great biological activities, transaminase offers additional advantages. The enzyme is proficient in synthesizing unnatural amino acids (β-amino acids), and amino alcohols in addition to amino steroids 7 – 9 . Transaminase is the only PLP-dependent superfamily enzyme that can catalyze amine synthesis through all three routes possible, i.e., kinetic resolution, deracemization, and asymmetric synthesis. The enzyme also catalyzes cascade reactions along with different enzymes. The enzyme holds the application not just in pharmaceuticals but in the cosmetics and food industries. Till now, many transaminases have been discovered using different strategies like culture-based techniques, homologous sequence searching, and metagenomics 10 . These technique helps to explore both (S)- (R) transaminases, α and ω-transaminases, and industrially important compounds, which are mainly because of their enantioselectivity property, which bypasses the large routes, escapes from chemical waste, as well as carry out reactions with great efficiency 11 . The present study attempts to isolate the ω-transaminase producing bacteria and to optimize the media for higher transaminase production. The optimization was carried out using strategies like OFAT (One Factor at a Time) and RSM-CCD (Response surface methodology- Central composite design) analysis, followed by its validation through an AI (Artificial Intelligence) tool, i.e., SVM (Support Vector Machine). As per our knowledge and literature review, we are the first to report the optimization of media for ω-transaminase production. We have optimised all the factors and then also validated them using an AI tool. Modified Luria Broth (MLB) was considered the best medium for the ω-transaminase production. AGSP2 is also efficient in catalysing the biotransformation reactions for the synthesis of industrially important compounds. MATERIAL AND METHODS 2.1 Chemicals— All the chemicals used in the experiments were of analytical grade and were acquired from Sigma Aldrich (St. Louis, USA), SRL (Mumbai, India), and Hi-Media (Mumbai, India). The glassware used was properly washed with Milli-Q water before use. 2.2 Isolation and Screening of Transaminase-Producing Bacteria— Organisms capable of producing transaminase were isolated from soil and water samples collected from various locations. Soil samples were taken from agricultural fields in Vallabh Vidyanagar (22.567852°N, 72.915305°E), pond water from Vadtal (22.5954672 N, 72.8711112°E), and industrial wastewater from the Amblakhadi River in Ankleshwar, Gujarat (21.614407°N, 73.000525°E). These samples were transferred to sterile containers and brought to the lab. All samples were enriched with 5 mM of different amines—alpha-methylbenzylamine, benzylamine, and cyclopropylamine—as the sole nitrogen source. The enrichment medium contained salts such as 1 g/L MgSO₄·7H₂O, 0.02 mg/L H₃BO₃, 0.2 mM CaCl₂, 0.1 mg/L MnSO₄·4H₂O, 0.1 mg/L CuSO₄·5H₂O, 0.1 mg/L NiSO₄·6H₂O, 2.0 mg/L NaMoO₄, 0.05 mg/L CoCl₂, 0.1 mg/L ZnCl₂, 4 mg/L FeSO₄·7H₂O, along with 100 mM glycerol and potassium phosphate buffer (50 mM, pH 7.0). One gram of soil sample and 1-2 mL of water sample were added to the medium and incubated at 37°C with shaking at 120 rpm for three weeks. After enrichment, 100 µL of each sample was spread on minimal agar plates supplemented with different amines. Colonies were selected for further testing of transaminase production in the culture broth, where enzyme activity was measured as described by Hwang and Kim 12 . Bacterial growth was assessed via serial dilution, with plates incubated at 37°C for 24-96 hours, and colony counts were expressed as log CFU/mL. 2.3 Transaminase Assay – ω-Transaminase production was measured in the broth. The broth was centrifuged, and the supernatant was collected to estimate enzyme activity. The reaction mixture contained 200 µl alpha-methylbenzylamine (200 mM) as an amine donor, 200 µl pyruvic acid (100 mM) as an amine acceptor, 100 µl PLP (0.5 mM) as a cofactor, and 800 µl potassium phosphate buffer (pH 7). The mixture was incubated at 37°C for 30 minutes, after which alanine was detected at 595 nm 6 . All experiments were performed in triplicate. One unit of transaminase activity is defined as the amount of enzyme required to produce 1 µmole of alanine in one minute under standard assay conditions. The Lowry method was used for protein estimation 13 . 2.4Time course study of enzyme— The time course study for the production and growth of the isolate AGSP2 was performed in submerged fermentation using a Luria Bertini broth along with 5mM alpha-MBA. The pH was adjusted to 7.0. 1mL of 24-hour-old culture was inoculated into 250 mL Erlenmeyer flasks containing 100 mL(w/v) of the medium. The flasks were incubated in an orbital shaker incubator (120 rpm) at 37°C for 96 hrs. The broth was centrifuged, and the supernatant collected was analyzed every 24 h up to 4 days for transaminase activity, protein content, and biomass. All experiments were carried out in triplicate 2.5 Identification of bacterial strain- To identify and classify the bacterial strain at the genus level, several tests were conducted. Morphological characterization included examining colony morphology, motility, spore formation, and Gram staining. Various biochemical tests, such as citrate, TSI, urease, and hemolytic activity, were used for classification. Additionally, the Vitck 2 compact system was utilized for further biochemical analysis 14 . The culture was also tested against different sugars and antibiotics. The culture was further characterized through 16S rRNA sequencing analysis. Genomic DNA was extracted with a HiPurA® kit from Hi Media, Mumbai, India. The DNA's quality was verified by agarose gel electrophoresis and measuring the A260/A280 ratio. The purified DNA then underwent amplification via polymerase chain reaction (PCR). The resulting amplicons were sequenced on an ABI 3500 x L Genetic analyzer. The obtained sequences were compared against NCBI GenBank entries using BLAST. The top ten sequences were aligned with the Clustal W software, selecting those with the highest identity scores. Finally, a distance matrix and phylogenetic tree were generated using the neighbor-joining method with MEGA 7. 2.6 Optimization of media and its components by the OFAT method Transaminase production by the isolate AGSP2 was optimized using the OFAT method. Different parameters were optimized and were also added in each succeeding step of the optimization process. 2.6.1 Media selection —Different growth media were examined for higher production of ω-transaminase. The media used in the present study were Minimal medium, Bushnell-Hass medium, Nutrient broth medium, and Luria Bertani medium. All the media were enriched with 5mM (Rac)-α-MBA (alpha-methylbenzylamine) as a substrate. These media were inoculated and incubated at 37°C at 120 rpm and checked for enzymatic activity for 4 days. 2.6.2 Inoculum size optimization— Inoculum size is another crucial parameter that must be optimized to achieve maximum transaminase enzyme production. The production media were inoculated with 1%, 2%, 3%, 4%, 5%, 7%, and 10% (v/v) of inoculum and incubated at 37°C in a shaker at 120 rpm. 2.6.3 Effect of carbon source— Luria Bertani broth was supplemented with 1% (w/v) of various carbon sources, including glucose, fructose, maltose, mannose, xylose, lactose, cellulose, sucrose, and starch, along with 5mM (rac)—α-MBA as a substrate. The mixture was incubated at 37°C on a shaker set to 120 rpm. Sugars were sterilized separately before being added to the media. 2.6.4 Effect of nitrogen source— Different nitrogen sources were added to Luria Bertani to determine which was the best and aided maximum enzyme production. Both organic and inorganic sources, such as urea, peptone, ammonium chloride, ammonium sulfate, and ammonium nitrate, were included at an initial concentration of 1% (w/v). 2.6.5 Effect of pH and Temperature on enzyme production: The optimum temperature and pH for transaminase production were determined between the temperature of 28-45 °C and a pH range of 4-9, respectively, by incubating an inoculated flask for 4 days at 37°C at 120 rpm. 2.6 . 6 Optimization of Agitation: The effect of agitation speed was investigated at a shake flask level by incubating the flasks at a range of 50-150rpm. For this study, all the parameters, like inoculum size, temperature, pH, carbon, and nitrogen source, were maintained at their optimum values obtained from our earlier studies . 2.7 Determination of significant medium components using the RSM-Central Composite Design (CCD) approach RSM (Response Surface Methodology) is a statistical tool generally used to analyze the responses generated through experimental work for optimization purposes. The response can concern the production, activity, degradation, or growth of enzymes. The analysis helps us to understand the interaction between independent variables and dependent variables. CCD (Central Composite Design) is an experimental design of RSM in which the complete experiment is designed, and the trial runs are generated according to factors and their concentrations. Here, we use Stat-Ease Inc. Design Expert 13 software for optimization purposes 15 . To determine the effect of four variables, i.e., yeast extract, NaCl, fructose, and peptone, on the production of the enzyme was surveyed by RSM for the Bacillus strain AGSP-2 to check their optimum concentration, which enhances the response (Transaminase activity). It is a statistical method that investigates the combined effects of independent variables and has been applied in various studies 16-19 . The CCD method generated a design of 30 trial runs, and all the experiments were performed in triplicate. This design tells us about, i.e., the effect of variables, the optimum concentration of variables, and the kind of interaction these variables have with each other, i.e., either synergistic or antagonistic. The first-order polynomial model represents the system behavior, which can be explained by the following equation- Y= b0 Σb i x i +Σb ii x i 2 + Σb ij x i x j where Y was the anticipated response, xi and xj were the coded values of independent variables that affect the response variable, β0 was the model intercept, βi, βii, and βiii represented the linear coefficient, quadratic coefficient, and the interaction coefficient, respectively. After the 30 trials were run, the experimental values were validated against the response of the CCD design (predicted values) through different tests and measurements, such as ANOVA (Analysis of Variance), coefficient determination, and significance calculated in terms of p- and f-values. RSM-CCD results were validated through an Artificial Intelligence tool, SVM (Support Vector Machine). SVM is an algorithm-based tool that helps to minimize errors and analyze the data based on regression and classification 20 . Support Vector Machine (SVM) is a classification method based on optimal margins in machine learning. It functions as a binary linear classifier but can handle non-linear data through Kernels and multi-class data using techniques such as one-versus-one, one-versus-rest, Crammer-Singer SVM, Weston-Watkins SVM, and directed acyclic graph SVM (DAGSVM). An SVM with a linear Kernel is called a linear SVM, while one with a non-linear Kernel is known as a non-linear SVM 21 . The tool also aims to find a hyperplane to differentiate the data. Kernel, linear, polynomial, and radial basis functions (RBF) are the different parameters that SVM uses. Here, we have used an R programming tool, and SVM is an expert in reading R-language in the format: X as data and Y as response. • SVM (kernel = "linear", type = NULL, scale = TRUE, x, y = NULL degree = 3, gamma = if (is. vector [x]) 1 else 1 / [num.of columns]- ncol (x), coef0 = 0, cost = 1, nu = 0.5, convergence tolerance = 0.001 • class. weights = NULL, cache size = 40, epsilon = 0.1, shrinking = TRUE, cross = 0, fitted = TRUE,...,subset probability = FALSE, na.action = na.omit) 2.7 Kinetic resolution of Primary amine- Kinetic resolution of α-Methylbenzylamine (α-MBA) was carried out by setting up a reaction which contains: (S/R) -α-MBA (200 mM, amine donor), pyruvic acid (100 mM, amine acceptor), pyridoxal-5-phosphate (0.5 mM, co-factor), and potassium phosphate buffer (50 mM, pH 7) along with transaminase enzyme. The reaction mixture was kept at 37 °C for 2 h. To stop the reaction, we further added NaOH (2 M, 100 µL), followed by extraction with ethyl acetate (300 µL). Acetophenone (product) formation was analyzed by HPLC using a C18 reversed-phase column. Acetonitrile and water (30:70) were used as a mobile phase. The sample was filtered before loading into the HPLC column. % conversion of product and enantiomeric excess was also calculated using the formula- RESULTS AND DISCUSSION 3.1 Isolation and Identification of transaminase-producing bacteria In the present study, 250 isolates were found initially from different samples, and out of those, only 50 were found promising with consistent growth and transaminase activity (Fig. S1). The flasks were initially enriched with various amines, but the maximum isolates were found at a 5 mM concentration of α-MBA from the Amlakhadi River sample and thus the 5mM concentration was finalized for further studies. Out of 50 isolates, the growth of 5 potent isolates obtained from primary screening was subjected to secondary screening. Table 1 shows the results of promising isolates that gave the maximum activity. Among these 5, two isolates, AGSP1 and AGSP2, were found to be potential. The culture AGSP2 was selected for further optimization studies as it showed the highest transaminase activity. Table 1: ω-transaminase activity of selected isolates Isolates Transaminase Activity (U/ml) Isolates-1 (Lambhvel) (AGSP-1) 1208.02± 132.45 Isolates-2 ( Amlakhadi ) (AGSP-2) 1279.22±140.78 Isolates-3 (Agricultural soil) 841.637±221.05 Isolates-4 (Sewage water) 800.006±152.46 Isolates-5 (Soil sample) 779.25±116.23 3.2 Time course study of enzyme- The time course study for the transaminase production by isolate AGSP-2 is presented in Fig. S2. It was observed that transaminase secretion commenced after 24 hours of fermentation, with a steady increase in its production and an increase in incubation time. The highest enzyme production (1280.14 U/ml) was recorded after 72 hours of incubation. However, the enzyme production declined beyond this period (96 h). Optimum transaminase production was achieved at pH 7 with a protein content of 4.7 mg/ml after 72 h of fermentation. 3.3 Morphological, Biochemical, and Molecular Identification of AGSP2 The isolated AGSP2 was identified based on morphological and biochemical analysis, as shown in Tables S1 and S2. The analysis revealed that the isolate is a Gram-positive, rod-shaped, endospore-forming bacterium. The colony appears white, opaque, and mucoid with raised elevation, rough texture, and irregular margin on a Nutrient agar plate. The morphological characteristics suggested that the isolate is rod-shaped bacilli around 3-4 µm in length, usually in chains, with the presence of an endospore. The biochemical test reveals that the bacteria belong to the Bacillus genera. The isolate was further identified using 16S rRNA sequencing analysis. The sequence of the isolate AGSP2 with a length of 1400 nucleotide base pairs was aligned, and a phylogenetic tree was constructed (Fig. S3). The comparison of results showed that the isolated strain AGSP2 shares the closest homology with Bacillus inaquasorum A 651 belongs to the genus Bacillus and was identified as Bacillus inaquasorum. The sequence was deposited in the NCBI Genebank data with the accession number JAPXF4000000000 22 . 3.4 Optimization of media and its components 34.1 Effect of different media- To select the best media for the production of transaminase, the isolate identified as Bacillus inaquasorum was grown in four different media supplemented with α-MBA at a concentration of 5mM. The media used in the present study were Minimal media, Bushnell-Haas medium, Nutrient broth medium, and Luria Bertani broth. Among the four media that were examined, Luria broth was found to be the best for the production of transaminase. The maximum production obtained was 3977.8±250 U/ml (Fig 1). The possibility of the presence of α-MBA in the medium increases the production by 1.8-fold, thus indicating its ability as an inducer too 23 . 3.4.2 Effect of inoculum size - The inoculum size of 2% (v/v) was found to be the best and was considered optimum, which resulted in the enzyme production of 4001.76 U/ml (Fig.2). However, increasing the inoculum size did not improve the production of the enzyme. At higher concentrations of the inoculum, rapid utilization and competition of the organisms between themselves resulted in a decreased production of the enzyme. Noshahri and co-workers reported using 5% inoculum in a bioreactor containing 300 mL of media 24 . 3.4.3 Effect of carbon source - The isolate Bacillus inaquasorum was grown in an LB medium wherein various mono, di, and polysaccharides (glucose, fructose, xylose, maltose, lactose, mannose, sucrose, cellulose, and starch) were added along with other media constituents and keeping the physiological parameters constant. Fructose as a carbon source was found to be the best, with a maximum enzyme production of 4322.11±224.45 U/ml, followed by mannose, maltose, glucose, etc. (Fig.3) 25,26 . Adding various sugars to the media enhanced the enzyme production compared to the control, suggesting the positive effect of their addition. The organism's uptake of fructose is possible because of the bacterial phosphoenolpyruvate (PEP), which is the carbohydrate phosphotransferase system (PTS). Bacillus subtilis and its subspecies. inaquosorum both are facultative anaerobes as reported by Dunlap 26 . The PTS system plays a crucial role in carbon and nitrogen metabolism. The PTS system allows the uptake of sugar in the phosphorylated form. The PTS system takes up the fructose as fructose-1-phosphate and then further transforms it into fructose-1-6-bisphosphate 28-30 . The genome sequencing (Results not shown here) also confirmed the presence of genes specific for fructose utilization required in the organism's PTS system. The fructose is transported through the PTS system in Bacillus subtilis . The transport and phosphorylation of fructose are strictly regulated and involve several proteins 31,32 . 3.4.4 Effect of nitrogen source - The addition of nitrogen source is expected to strongly affect the induction level of transaminase, as the enzyme is involved in nitrogen metabolism. Among all the nitrogen sources incorporated in the Luria broth, along with the 5mM of α-MBA and supplemented with peptone was the best source with an enzyme activity of 4136.36±278.08 (Fig. 4). The various nitrogen sources used were added by replacing tryptone in the Luria Bertani medium. Generally, the organisms' uptake of peptone occurs via a specialized transport system. Supplementation of peptone in the growth media not only increases the cell number and enzyme production but also helps maintain the medium's pH, making it favorable for bacterial growth 33 . Clay and co-workers reported the use of peptone in the medium for the production of ω-transaminase 34 . 3.4.5 Effect of Temperature and pH: Temperature is one of the most critical parameters for the growth of organisms. The influence of different temperatures on the production of transaminase by the isolate AGSP-2 was investigated. The temperature of 37°C was found to be optimum, producing the maximum enzyme with an activity of 4053.99 U/ml on the 3rd day of incubation, as depicted in Fig. 5. An increase in the temperature above 37°C was detrimental to the growth of the organisms, leading to a decrease in enzyme production 24,35,36 . pH also plays a vital role in growth and enzyme production, as both growth and enzyme secretion are sensitive and pH-dependent. The production of the transaminase was carried out over a range of pH (4.0 to 9.0). Figure 6 depicts the maximal enzyme production at pH 7.0 with an enzyme unit of 4049 U/ml. A pH above 7.0 leads to a decrease in enzyme production. Xiang and colleagues reported similar results where they checked the pH profile for three different bacteria and found that among all, two transaminases, ATA-Gze from Gibberella zeae and ATA-Ate from Aspergillus terreus showed an optimum pH of 7.5 37 . Noshahri and team also reported that the optimum pH for transaminase production was 7 24 . Production studies of transaminase have not been conducted in a wide range. Low enzyme activities were detected at acidic and high alkaline pH. At lower pH (4,5 and 6), the organisms were able to grow but failed to produce a significant amount of enzymes. The change in the pH during the growth and production was observed during production. The pH shifted to a more alkaline level due to the release of ammonia by the organisms, leading to less growth and eventually affecting enzyme production. 3.4.6 Effect of Agitation speed: The effect of agitation speed was investigated at a shake flask level by incubating the flasks at a range of 50,100,120, and 150 rpm. The impact of agitation on the production of transaminase is shown in Fig. 7. It was noticed that the optimum level of agitation needed for the maximum production of the enzyme was 120 rpm. The maximum enzyme production was found to be 4112.44 U/ml at 120 rpm. An increase in rpm above 120 leads to a decrease in transaminase activity, and also 3.5 RSM-CCD Analysis The OFAT-optimized factors were verified by RSM-CCD analysis using Design Expert 13 software 15 . These independent variables were added to the model in high and low ranges. Then, 30 runs generated by the software were performed in the laboratory, which was the experimental one. Table 2 represents the ANOVA analysis of the RSM-CCD design. The p-value is less than 0.05, and the F-value of 25.07 indicates the factors have a statistical correlation and significantly affect the response generated 38,39 . Table 3 represents the statistics of the model, where values like Predicted R² and adjusted R² are measured. The data revealed that the R² value is 0.95, the adjusted R² value is 0.92, and the predicted R² value is 0.78. The higher R² value indicates that the model is a perfect fit and that the model is capable of explaining all the variability. On the other hand, the adjusted R² value suggests that the model is accurate and that all the predictors added have improved the model and made it a good fit. The predicted R² value of the model tells us about the predictive ability of the model’s performance on the new data. The higher the predictive value, the higher the model's accuracy in predicting the latest data. Apart from this, the other two critical parameters of the statistical model are the adequate precision value and the coefficient of variation (CV%). The proper precision value is 20.0354. A value greater than 4 is desirable, representing a signal-to-noise ratio, justifying that the model is a perfect fit and that the predictions are reliable with a strong signal. The CV% obtained is 8.12 and should be less than 10%. It is a ratio of the standard deviation to the mean response. The lower the value of %CV, the more consistent the results are with less variability (Bhatt et al., 2023). Noshahari et al. (2021) reported the same observation after statistical optimization using RSM-CCD, where they confirmed that α-MBA helps in the induction of the ω-transaminase enzyme. They reported that the transaminase activity (acetophenone formation) was at its maximum after 72 hours. The optimum culture conditions were 38°C, pH 7. Table 2- ANOVA Analysis Source Sum of Squares Df Mean Square F-value p-value Model 3.722E+07 14 2.659E+06 25.07 < 0.0001* Significant A-yeast extract 1.163E+06 1 1.163E+06 10.97 0.0047 B-NaCl 27367.88 1 27367.88 0.2581 0.6188 C-fructose 35514.73 1 35514.73 0.3349 0.5714 D-peptone 1.105E+05 1 1.105E+05 1.04 0.3236 AB 3742.69 1 3742.69 0.0353 0.8535 AC 3.634E+05 1 3.634E+05 3.43 0.0839 AD 39556.24 1 39556.24 0.3730 0.5505 BC 12182.09 1 12182.09 0.1149 0.7393 BD 2.307E+05 1 2.307E+05 2.18 0.1609 CD 9.546E+05 1 9.546E+05 9.00 0.0090 A² 2.985E+07 1 2.985E+07 281.53 < 0.0001 B² 4.486E+06 1 4.486E+06 42.30 < 0.0001 C² 4.468E+06 1 4.468E+06 42.13 < 0.0001 D² 4.593E+06 1 4.593E+06 43.31 < 0.0001 Lack of Fit 1.397E+06 10 1.397E+06 3.61 0.0848 Not Significant Pure Error 1.937E+05 5 3.874E+04 Cor Total 3.881E+07 29 [df = Degree of freedom] Transaminase activity was as follow: Transaminase activity = 5,820.73 + 220.136 * A + 33.7687 * B + 38.4679 * C + -67.8404 * D + 15.2944 * AB + 150.709 * AC + -49.7219 * AD + 27.5931 * BC + -120.078 * BD + -244.258 * CD + -1,043.26 * A 2 + -404.397 * B 2 + -403.588 * C 2 + -409.2 * D 2 Table 3 statistical parameters Standard Deviation 325.63 R² 0.9590 Mean 3.87 Adjusted R² 0.9208 C.V. % 8.12 Predicted R² 0.7855 Adequate Precision 20.0354 The 3D response surface plots and Contour plots display the interaction between the two independent variables while keeping other variables constant. These 3D surface graphical presentations show how two variables affect each other, positively or negatively, and will also tell the optimum point where the maximum response is generated. A total of 6 plots were generated, which show the interactions between the different variables. Factor A is yeast extract, factor B is NaCl, factor C is fructose, and factor D is peptone. The first plot generated by the design expert shows the interaction between factors AB (yeast extract and NaCl), AC (yeast extract and fructose), AD (yeast extract and peptone), BC (NaCl and fructose), BD (NaCl and peptone) and, CD (fructose and peptone) (Fig 8a-f). All the factors positively interacted with each other, and the tool found that at a maximum concentration of yeast extract (7.5 g/L), NaCl (7.5 g/L), Fructose (12 g/L), and peptone (12 g/L), the maximum response in terms of transaminase activity was generated. After the statistical analysis, we compared the unoptimized media and optimized media for the transaminase production. There was a 2.8-fold increase in the enzyme activity (Fig. S4). We also validated this model using an AI tool, i.e., SVM. It is a supervised model with algorithms that help the user to analyze data in classification and regression analysis. The model is advanced with a Gaussian kernel function in the R programming language. Table S3- Represents the RSM-CCD design model generated with all 30 runs and the responses (Transaminase activity), i.e., the experimental, RSM-predicted, and SVM-predicted values. The SVM values are very close to the RSM-predicted values, which shows that the model is a perfect fit. AI validation was found helpful as it minimized the error in the RSM-CCD experiment. 3.6 HPLC detection- HPLC analysis of the product formation in the reaction where both (S) and (R) enantiomers of α-MBA were used revealed that product formation occurred with the (S) -enantiomer only. It may be because the transaminase enzyme is (S) - specific and can convert the (S) isomer more efficiently than the (R) isomer. Figure S5 represents the chromatogram of acetophenone. The retention time of acetophenone is 5.2, with a 53.32 % conversion of product, and the concentration of product formed is 106 ppm (substrate used was 200ppm), which indicates that the (S) -enantiomer is more favoured as compared to the (R) -enantiomer with a moderate excess. Acetophenone is an industrially important ketone compound which is used in fragrances and related products due to its sweet and floral aroma, as a flavouring agent in candies and chewing gum, precursor in the production of phenytoin (anticonvulsant), and used as a raw material in making resins used in eyeglass lenses, automotive parts, and electronics CONCLUSION The isolate AGSP2, i.e., Bacillus inaquosorum , isolated from the Amlakhadi River, is a transaminase-producing bacterium and can produce amines and amino acids. The bacteria show maximum transaminase activity in MLB media supplemented with alpha-methylbenzylamine as the best substrate for the bacteria. A 1.5-fold increase in transaminase activity was achieved after the RSM-CCD analysis, along with the AI validation using the SVM tool. Transaminase is a crucial enzyme for pharmaceuticals as it can produce chiral compounds. The optimization of parameters helps enhance production, which may help carry out biotransformation reactions efficiently using different substrates. The biotransformation reaction carried out led to the synthesis of acetophenone, an industrially important ketone product. However, further experiments should be performed to check the affinity of enzymes towards other kinds of substrates, and also to scale up the reactions from a smaller to a larger scale. Declarations Ethical approval: Not Applicable Funding: Not Applicable. Disclosure statement: The authors report there are no competing interests to declare. Informed consent: Not Applicable Author contribution: All authors contributed to the article's conception and design. The experimental work and data analysis were performed by Shreya Pandya. The first draft of the manuscript was written by Shreya Pandya and revised/ supervised by Urvish Chhaya and Akshaya Gupte. Conceptualization- [Shreya Pandya, Urvish Chhaya, Akshaya Gupte], Formal analysis- [Shreya Pandya], Writing- Original draft preparation- [Shreya Pandya]. Writing- Review and Editing- [Shreya Pandya, Urvish Chhaya, Akshaya Gupte], Supervision- [Urvish Chhaya, Akshaya Gupte]. All authors read and approved the final manuscript. Data Availability Statement : The datasets generated (Whole genome sequence) and analyzed during the current study are available in the NCBI repository. BioSampleID-SAMN32292702 WGS project-JAPXFU01 BioProject- PRJNA913322 Submitted GenBank assembly GCA_028664335.1 NCBI RefSeq assembly- GCF_028664335.1 Assembly name ASM2866433V1 NCBI accession no- JAPXF4000000000 Link- https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_028664335.1/ Acknowledgments I would like to thank the Knowledge Consortium of Gujarat (KCG), Govt. of Gujarat for providing the SHODH fellowship [ScHeme Of Developing High quality research]. 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13:19:55","extension":"html","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157849,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/37d790e978821b43cf2a6efd.html"},{"id":93143296,"identity":"8b967643-e355-47b9-a6a7-8eb6496e3219","added_by":"auto","created_at":"2025-10-09 13:19:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of different media on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Control- media without an amine donor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Media- media supplemented with amine donor\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/e7d3f69fa0d85d9f33e8b74f.png"},{"id":93144242,"identity":"8f203470-6a02-49f4-b188-6fca36515c7c","added_by":"auto","created_at":"2025-10-09 13:27:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimization of Inoculum Size for ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/911d81f6a33a15f9d7bf00d6.png"},{"id":93143298,"identity":"a38dc05f-cde2-45e3-8a68-c1c5a3c03ca5","added_by":"auto","created_at":"2025-10-09 13:19:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of carbon source on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/4007475f701c5e59eeba675c.png"},{"id":93144243,"identity":"945c8478-7869-4a21-bbf1-35736ff6512d","added_by":"auto","created_at":"2025-10-09 13:27:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of nitrogen source on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/98d040756b8cc0367150da31.png"},{"id":93143301,"identity":"0c1e4fb6-1958-478c-a9ba-c3d8c6c91503","added_by":"auto","created_at":"2025-10-09 13:19:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of Temperature on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/21f76ef957fb489ff26be1ef.png"},{"id":93144244,"identity":"8466fb24-ee5b-47e0-b0ea-43d001f8b067","added_by":"auto","created_at":"2025-10-09 13:27:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":19593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of pH on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/ae8ecfc16b7ab86aa61ac4ae.png"},{"id":93143299,"identity":"87b3b14a-7c7b-4aff-8816-51794b3614cc","added_by":"auto","created_at":"2025-10-09 13:19:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimization of Agitation Speed on ω-transaminase production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/c5741e38590211dfe206cb6a.png"},{"id":93143304,"identity":"43bcf77a-3be6-4962-881f-c8c2d2962fcc","added_by":"auto","created_at":"2025-10-09 13:19:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1720328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a-f) 3D Surface plots and Contour plots for the different variables in the optimized design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/c437ca2b51914ee923e53c61.png"},{"id":107350733,"identity":"8c2023eb-608f-401e-8feb-331e071e8235","added_by":"auto","created_at":"2026-04-20 16:01:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2881407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/5819ecf1-c4ca-4b0f-9e39-b66cd1c03e7c.pdf"},{"id":93144246,"identity":"e305f326-9029-44c6-8445-131745733e7f","added_by":"auto","created_at":"2025-10-09 13:27:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":650584,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialSN.docx","url":"https://assets-eu.researchsquare.com/files/rs-7609083/v1/522625784972cb1277b7b721.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harnessing Bacillus inaquosorum AGSP2 for Enhancing ω-Transaminase Production Through Classical and AI-Supported Statistical Design","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChiral amines are an important building block for synthesizing several bioactive compounds and active pharmaceutical ingredients (APIs). Chiral amines are most well-recognized in pharmaceutical industries for imparting desired biological activity to various chemical entities \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Almost 50% of the drugs produced annually are chiral, thus creating a huge market \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Different strategies for producing chiral amines via biocatalytic and chemical transformations have been developed. The chemical approaches used for the enantioselective synthesis of chiral amines suffer from various drawbacks such as harsh reaction conditions, low efficiency, selectivity, and a high environmental impact \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In the last two decades, many investigations have been carried out for the development of alternative biocatalytic routes that are selective, efficient, and eco-friendly. With mild reaction conditions and high stereospecificity of enzymatic reactions, biocatalysis is increasingly considered an alternative route to chemical synthesis. Different enzymes, such as amine dehydrogenase, oxidoreductase, hydrolases, ammonia lyase, imine reductase, and transaminases, have been used successfully. Among these enzymes, ω-transaminase has gained attention for preparing enantiopure products capable of stereoselective amination of ketones.\u003c/p\u003e\u003cp\u003eTransaminase or aminotransferase (E.C. no 2.6.1.X), a chiral amine producer, is emerging as a promising biocatalyst. Transaminase belongs to the family of transferase enzymes and is PLP (Pyridoxal-5-Phosphate)- dependent. It follows a ping-pong bi-bi mechanism and catalyzes the two-step reaction of deamination and amination of amino acids or amines into keto compounds and vice versa using PLP as a cofactor \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. The complete mechanism involves the formation of intermediates such as external aldimine, quinoid, ketamine, and PMP (Pyridoxamine-5\u0026rsquo;-phosphate) and simultaneous release of the newly formed amines or amino acid and a ketone compound. Transaminase is being used to produce several Active Pharmaceutical Ingredients (APIs), which are the essential moiety of many drugs. These enzymes can be explored for the synthesis of many drugs such as ramatroban, sitagliptin, and sacubitril, which help cure various diseases \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Transaminases can be classified into different categories and based on different criteria. Mainly, there are two types: α-transaminase and ω-transaminase. ω-transaminase has wide applications in industries because of its wide substrate specificity. The ω-transaminase can react with amino acids, amines, ketones, and aldehydes \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Being an extremely enantioselective and stereoselective transaminase can synthesize optically active compounds (S/R enantiomers). Apart from having great biological activities, transaminase offers additional advantages. The enzyme is proficient in synthesizing unnatural amino acids (β-amino acids), and amino alcohols in addition to amino steroids \u003csup\u003e\u003cb\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Transaminase is the only PLP-dependent superfamily enzyme that can catalyze amine synthesis through all three routes possible, i.e., kinetic resolution, deracemization, and asymmetric synthesis. The enzyme also catalyzes cascade reactions along with different enzymes. The enzyme holds the application not just in pharmaceuticals but in the cosmetics and food industries. Till now, many transaminases have been discovered using different strategies like culture-based techniques, homologous sequence searching, and metagenomics \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. These technique helps to explore both \u003cem\u003e(S)- (R)\u003c/em\u003e transaminases, α and ω-transaminases, and industrially important compounds, which are mainly because of their enantioselectivity property, which bypasses the large routes, escapes from chemical waste, as well as carry out reactions with great efficiency \u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe present study attempts to isolate the ω-transaminase producing bacteria and to optimize the media for higher transaminase production. The optimization was carried out using strategies like OFAT (One Factor at a Time) and RSM-CCD (Response surface methodology- Central composite design) analysis, followed by its validation through an AI (Artificial Intelligence) tool, i.e., SVM (Support Vector Machine). As per our knowledge and literature review, we are the first to report the optimization of media for ω-transaminase production. We have optimised all the factors and then also validated them using an AI tool. Modified Luria Broth (MLB) was considered the best medium for the ω-transaminase production. AGSP2 is also efficient in catalysing the biotransformation reactions for the synthesis of industrially important compounds.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Chemicals\u0026mdash;\u003c/strong\u003eAll the chemicals used in the experiments were of analytical grade and were acquired from Sigma Aldrich (St. Louis, USA), SRL (Mumbai, India), and Hi-Media (Mumbai, India). The glassware used was properly washed with Milli-Q water before use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Isolation and Screening of Transaminase-Producing Bacteria\u0026mdash;\u003c/strong\u003eOrganisms capable of producing transaminase were isolated from soil and water samples collected from various locations. Soil samples were taken from agricultural fields in Vallabh Vidyanagar (22.567852\u0026deg;N, 72.915305\u0026deg;E), pond water from Vadtal (22.5954672 N, 72.8711112\u0026deg;E), and industrial wastewater from the Amblakhadi River in Ankleshwar, Gujarat (21.614407\u0026deg;N, 73.000525\u0026deg;E). These samples were transferred to sterile containers and brought to the lab. All samples were enriched with 5 mM of different amines\u0026mdash;alpha-methylbenzylamine, benzylamine, and cyclopropylamine\u0026mdash;as the sole nitrogen source. The enrichment medium contained salts such as 1 g/L MgSO₄\u0026middot;7H₂O, 0.02 mg/L H₃BO₃, 0.2 mM CaCl₂, 0.1 mg/L MnSO₄\u0026middot;4H₂O, 0.1 mg/L CuSO₄\u0026middot;5H₂O, 0.1 mg/L NiSO₄\u0026middot;6H₂O, 2.0 mg/L NaMoO₄, 0.05 mg/L CoCl₂, 0.1 mg/L ZnCl₂, 4 mg/L FeSO₄\u0026middot;7H₂O, along with 100 mM glycerol and potassium phosphate buffer (50 mM, pH 7.0). One gram of soil sample and 1-2 mL of water sample were added to the medium and incubated at 37\u0026deg;C with shaking at 120 rpm for three weeks. After enrichment, 100 \u0026micro;L of each sample was spread on minimal agar plates supplemented with different amines. Colonies were selected for further testing of transaminase production in the culture broth, where enzyme activity was measured as described by Hwang and Kim \u003cstrong\u003e\u003csup\u003e12\u003c/sup\u003e\u003c/strong\u003e. Bacterial growth was assessed via serial dilution, with plates incubated at 37\u0026deg;C for 24-96 hours, and colony counts were expressed as log CFU/mL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Transaminase Assay \u0026ndash;\u0026nbsp;\u003c/strong\u003e\u0026omega;-Transaminase production was measured in the broth. The broth was centrifuged, and the supernatant was collected to estimate enzyme activity. The reaction mixture contained 200 \u0026micro;l alpha-methylbenzylamine (200 mM) as an amine donor, 200 \u0026micro;l pyruvic acid (100 mM) as an amine acceptor, 100 \u0026micro;l PLP (0.5 mM) as a cofactor, and 800 \u0026micro;l potassium phosphate buffer (pH 7). The mixture was incubated at 37\u0026deg;C for 30 minutes, after which alanine was detected at 595 nm \u003csup\u003e6\u003c/sup\u003e. All experiments were performed in triplicate. One unit of transaminase activity is defined as the amount of enzyme required to produce 1 \u0026micro;mole of alanine in one minute under standard assay conditions. The Lowry method was used for protein estimation \u003cstrong\u003e\u003csup\u003e13\u003c/sup\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4Time course study of enzyme\u0026mdash;\u003c/strong\u003eThe time course study for the production and growth of the isolate AGSP2 was performed in submerged fermentation using a Luria Bertini broth along with 5mM alpha-MBA. The pH was adjusted to 7.0. \u0026nbsp; 1mL of 24-hour-old culture was inoculated into 250 mL Erlenmeyer flasks containing 100 mL(w/v) of the medium. The flasks were incubated in an orbital shaker incubator (120 rpm) at 37\u0026deg;C for 96 hrs. The broth was centrifuged, and the supernatant collected was analyzed every 24 h up to 4 days for transaminase activity, protein content, and biomass. All experiments were carried out in triplicate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Identification of bacterial strain-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify and classify the bacterial strain at the genus level, several tests were conducted. Morphological characterization included examining colony morphology, motility, spore formation, and Gram staining. Various biochemical tests, such as citrate, TSI, urease, and hemolytic activity, were used for classification. Additionally, the Vitck 2 compact system was utilized for further biochemical analysis \u003cstrong\u003e\u003csup\u003e14\u003c/sup\u003e\u003c/strong\u003e. The culture was also tested against different sugars and antibiotics. The culture was further characterized through 16S rRNA sequencing analysis. Genomic DNA was extracted with a HiPurA\u0026reg; kit from Hi Media, Mumbai, India. The DNA\u0026apos;s quality was verified by agarose gel electrophoresis and measuring the A260/A280 ratio. The purified DNA then underwent amplification via polymerase chain reaction (PCR). The resulting amplicons were sequenced on an ABI 3500 x L Genetic analyzer. The obtained sequences were compared against NCBI GenBank entries using BLAST. The top ten sequences were aligned with the Clustal W software, selecting those with the highest identity scores. Finally, a distance matrix and phylogenetic tree were generated using the neighbor-joining method with MEGA 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Optimization of media and its components by the OFAT method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransaminase production by the isolate AGSP2 was optimized using the OFAT method. Different parameters were optimized and were also added in each succeeding step of the optimization process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.1 Media selection\u003c/strong\u003e\u0026mdash;Different growth media were examined for higher production of \u0026omega;-transaminase. The media used in the present study were Minimal medium, Bushnell-Hass medium, Nutrient broth medium, and Luria Bertani medium. All the media were enriched with 5mM (Rac)-\u0026alpha;-MBA (alpha-methylbenzylamine) as a substrate. These media were inoculated and incubated at 37\u0026deg;C at 120 rpm and checked for enzymatic activity for 4 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.2 Inoculum size optimization\u0026mdash;\u003c/strong\u003eInoculum size is another crucial parameter that must be optimized to achieve maximum transaminase enzyme production. The production media were inoculated with 1%, 2%, 3%, 4%, 5%, 7%, and 10% (v/v) of inoculum and incubated at 37\u0026deg;C in a shaker at 120 rpm.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.3 Effect of carbon source\u0026mdash;\u003c/strong\u003eLuria Bertani broth was supplemented with 1% (w/v) of various carbon sources, including glucose, fructose, maltose, mannose, xylose, lactose, cellulose, sucrose, and starch, along with 5mM (rac)\u0026mdash;\u0026alpha;-MBA as a substrate. The mixture was incubated at 37\u0026deg;C on a shaker set to 120 rpm. Sugars were sterilized separately before being added to the media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.4 Effect of nitrogen source\u0026mdash;\u003c/strong\u003eDifferent nitrogen sources were added to Luria Bertani to determine which was the best and aided maximum enzyme production. Both organic and inorganic sources, such as urea, peptone, ammonium chloride, ammonium sulfate, and ammonium nitrate, were included at an initial concentration of 1% (w/v).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.5 Effect of pH and Temperature on enzyme production:\u0026nbsp;\u003c/strong\u003eThe optimum temperature and pH for transaminase production were determined between the temperature of 28-45 \u0026deg;C and a pH range of 4-9, respectively, by incubating an inoculated flask for 4 days at 37\u0026deg;C at 120 rpm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6\u003c/strong\u003e.\u003cstrong\u003e6\u003c/strong\u003e \u003cstrong\u003eOptimization of Agitation:\u0026nbsp;\u003c/strong\u003eThe effect of agitation speed was investigated at a shake flask level by incubating the flasks at a range of 50-150rpm. For this study, all the parameters, like inoculum size, temperature, pH, carbon, and nitrogen source, were maintained at their optimum values obtained from our earlier studies\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Determination of significant medium components using the RSM-Central Composite Design (CCD) approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRSM (Response Surface Methodology) is a statistical tool generally used to analyze the responses generated through experimental work for optimization purposes. The response can concern the production, activity, degradation, or growth of enzymes. The analysis helps us to understand the interaction between independent variables and dependent variables. \u0026nbsp;CCD (Central Composite Design) is an experimental design of RSM in which the complete experiment is designed, and the trial runs are generated according to factors and their concentrations.\u003c/p\u003e\n\u003cp\u003eHere, we use Stat-Ease Inc. Design Expert 13 software for optimization purposes \u003cstrong\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/strong\u003e. To determine the effect of four variables, i.e., yeast extract, NaCl, fructose, and peptone, on the production of the enzyme was surveyed by RSM for the \u003cem\u003eBacillus\u003c/em\u003e strain AGSP-2 to check their optimum concentration, which enhances the response (Transaminase activity). It is a statistical method that investigates the combined effects of independent variables and has been applied in various studies\u003cstrong\u003e\u003csup\u003e16-19\u003c/sup\u003e\u003c/strong\u003e. \u0026nbsp;The CCD method generated a design of 30 trial runs, and all the experiments were performed in triplicate. This design tells us about, i.e., the effect of variables, the optimum concentration of variables, and the kind of interaction these variables have with each other, i.e., either synergistic or antagonistic.\u003c/p\u003e\n\u003cp\u003eThe first-order polynomial model represents the system behavior, which can be explained by the following equation-\u003c/p\u003e\n\u003cp\u003eY=\u0026nbsp;b0 \u0026Sigma;b\u003csub\u003ei\u003c/sub\u003e x\u003csub\u003ei\u003c/sub\u003e +\u0026Sigma;b\u003csub\u003eii\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e+ \u0026Sigma;b\u003csub\u003eij\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003e x\u003csub\u003ej\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ewhere Y was the anticipated response, xi and xj were the coded values of independent variables that affect the response variable, \u0026beta;0 was the model intercept, \u0026beta;i, \u0026beta;ii, and \u0026beta;iii represented the linear coefficient, quadratic coefficient, and the interaction coefficient, respectively.\u003c/p\u003e\n\u003cp\u003eAfter the 30 trials were run, the experimental values were validated against the response of the CCD design (predicted values) through different tests and measurements, such as ANOVA (Analysis of Variance), coefficient determination, and significance calculated in terms of p- and f-values. RSM-CCD results were validated through an Artificial Intelligence tool, SVM (Support Vector Machine). SVM is an algorithm-based tool that helps to minimize errors and analyze the data based on regression and classification \u003cstrong\u003e\u003csup\u003e20\u003c/sup\u003e\u003c/strong\u003e. Support Vector Machine (SVM) is a classification method based on optimal margins in machine learning. It functions as a binary linear classifier but can handle non-linear data through Kernels and multi-class data using techniques such as one-versus-one, one-versus-rest, Crammer-Singer SVM, Weston-Watkins SVM, and directed acyclic graph SVM (DAGSVM). An SVM with a linear Kernel is called a linear SVM, while one with a non-linear Kernel is known as a non-linear SVM\u003cstrong\u003e\u003csup\u003e21\u003c/sup\u003e\u003c/strong\u003e. The tool also aims to find a hyperplane to differentiate the data. Kernel, linear, polynomial, and radial basis functions (RBF) are the different parameters that SVM uses. Here, we have used an R programming tool, and SVM is an expert in reading R-language in the format: X as data and Y as response.\u003c/p\u003e\n\u003cp\u003e\u0026bull; SVM (kernel = \u0026quot;linear\u0026quot;, type = NULL, scale = TRUE, x, y = NULL\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;degree = 3, gamma = if (is. vector [x]) 1 else 1 / [num.of columns]- ncol (x),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecoef0 = 0, cost = 1, nu = 0.5, convergence tolerance = 0.001\u003c/p\u003e\n\u003cp\u003e\u0026bull; class. weights = NULL, cache size = 40, epsilon = 0.1,\u003c/p\u003e\n\u003cp\u003eshrinking = TRUE, cross = 0,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003efitted = TRUE,...,subset\u003c/p\u003e\n\u003cp\u003eprobability = FALSE, na.action = na.omit)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Kinetic resolution of Primary amine-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKinetic resolution of \u0026alpha;-Methylbenzylamine (\u0026alpha;-MBA) was carried out by setting up a reaction which contains: \u003cem\u003e(S/R)\u003c/em\u003e-\u0026alpha;-MBA (200 mM, amine donor), pyruvic acid (100 mM, amine acceptor), pyridoxal-5-phosphate (0.5 mM, co-factor), and potassium phosphate buffer (50 mM, pH 7) along with transaminase enzyme. The reaction mixture was kept at 37 \u0026deg;C for 2 h. To stop the reaction, we further added NaOH (2 M, 100 \u0026micro;L), followed by extraction with ethyl acetate (300 \u0026micro;L). Acetophenone (product) formation was analyzed by HPLC using a C18 reversed-phase column. Acetonitrile and water (30:70) were used as a mobile phase. The sample was filtered before loading into the HPLC column. % conversion of product and enantiomeric excess was also calculated using the formula-\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1760015443.png\" width=\"514\" height=\"352\"\u003e\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e3.1 Isolation and Identification of transaminase-producing bacteria\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, 250 isolates were found initially from different samples, and out of those, only 50 were found promising with consistent growth and transaminase activity (Fig. S1). The flasks were initially enriched with various amines, but the maximum isolates were found at a 5 mM concentration of \u0026alpha;-MBA from the Amlakhadi River sample and thus the 5mM concentration was finalized for further studies. Out of 50 isolates, the growth of 5 potent isolates obtained from primary screening was subjected to secondary screening. Table 1 shows the results of promising isolates that gave the maximum activity. Among these 5, two isolates, AGSP1 and AGSP2, were found to be potential. The culture AGSP2 was selected for further optimization studies as it showed the highest transaminase activity. \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: \u0026omega;-transaminase activity of selected isolates\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransaminase Activity (U/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates-1 (Lambhvel) (AGSP-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1208.02\u0026plusmn; 132.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates-2 (\u003c/strong\u003e\u003cstrong\u003eAmlakhadi\u003c/strong\u003e\u003cstrong\u003e) (AGSP-2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1279.22\u0026plusmn;140.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates-3 (Agricultural soil)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e841.637\u0026plusmn;221.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates-4 (Sewage water)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e800.006\u0026plusmn;152.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsolates-5 (Soil sample)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e779.25\u0026plusmn;116.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Time course study of enzyme-\u0026nbsp;\u003c/strong\u003eThe time course study for the transaminase production by isolate AGSP-2 is presented in Fig. S2. It was observed that transaminase secretion commenced after 24 hours of fermentation, with a steady increase in its production and an increase in incubation time. The highest enzyme production (1280.14 U/ml) was recorded after 72 hours of incubation. However, the enzyme production declined beyond this period (96 h). Optimum transaminase production was achieved at pH 7 with a protein content of 4.7 mg/ml after 72 h of fermentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Morphological, Biochemical, and Molecular Identification of AGSP2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe isolated AGSP2 was identified based on morphological and biochemical analysis, as shown in Tables S1 and S2. The analysis revealed that the isolate is a Gram-positive, rod-shaped, endospore-forming bacterium. The colony appears white, opaque, and mucoid with raised elevation, rough texture, and irregular margin on a Nutrient agar plate. The morphological characteristics suggested that the isolate is rod-shaped bacilli around 3-4 \u0026micro;m in length, usually in chains, with the presence of an endospore. The biochemical test reveals that the bacteria belong to the \u003cem\u003eBacillus\u003c/em\u003e genera. The isolate was further identified using 16S rRNA sequencing analysis. The sequence of the isolate AGSP2 with a length of 1400 nucleotide base pairs was aligned, and a phylogenetic tree was constructed (Fig. S3). The comparison of results showed that the isolated strain AGSP2 shares the closest homology with \u003cem\u003eBacillus inaquasorum\u003c/em\u003e A 651 belongs to the genus \u003cem\u003eBacillus\u0026nbsp;\u003c/em\u003eand was identified as \u003cem\u003eBacillus inaquasorum.\u0026nbsp;\u003c/em\u003eThe sequence was deposited in the NCBI Genebank data with the accession number JAPXF4000000000 \u003cstrong\u003e\u003csup\u003e22\u003c/sup\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Optimization of media and its components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e34.1 Effect of different media-\u0026nbsp;\u003c/strong\u003eTo select the best media for the production of transaminase, the isolate identified as\u003cem\u003e\u0026nbsp;Bacillus inaquasorum\u0026nbsp;\u003c/em\u003ewas grown in four different media supplemented with \u0026alpha;-MBA at a concentration of 5mM. The media used in the present study were Minimal media, Bushnell-Haas medium, Nutrient broth medium, and Luria Bertani broth. Among the four media that were examined, Luria broth was found to be the best for the production of transaminase. The maximum production obtained was 3977.8\u0026plusmn;250 U/ml (Fig 1). The possibility of the presence of \u0026alpha;-MBA in the medium increases the production by 1.8-fold, thus indicating its ability as an inducer too \u003cstrong\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/strong\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Effect of inoculum size\u003c/strong\u003e- The inoculum size of 2% (v/v) was found to be the best and was considered optimum, which resulted in the enzyme production of 4001.76 U/ml (Fig.2). However, increasing the inoculum size did not improve the production of the enzyme. At higher concentrations of the inoculum, rapid utilization and competition of the organisms between themselves resulted in a decreased production of the enzyme. Noshahri and co-workers reported using 5% inoculum in a bioreactor containing 300 mL of media \u003cstrong\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3 Effect of carbon source\u003c/strong\u003e- The isolate \u003cem\u003eBacillus inaquasorum\u003c/em\u003e was grown in an LB medium wherein various mono, di, and polysaccharides (glucose, fructose, xylose, maltose, lactose, mannose, sucrose, cellulose, and starch) were added along with other media constituents and keeping the physiological parameters constant. Fructose as a carbon source was found to be the best, with a maximum enzyme production of 4322.11\u0026plusmn;224.45 U/ml, followed by mannose, maltose, glucose, etc. \u0026nbsp;(Fig.3) \u003cstrong\u003e\u003csup\u003e25,26\u003c/sup\u003e\u003c/strong\u003e. Adding various sugars to the media enhanced the enzyme production compared to the control, suggesting the positive effect of their addition. The organism\u0026apos;s uptake of fructose is possible because of the bacterial phosphoenolpyruvate (PEP), which is the carbohydrate phosphotransferase system (PTS). \u0026nbsp;\u003cem\u003eBacillus subtilis\u003c/em\u003e and its subspecies. \u003cem\u003einaquosorum\u003c/em\u003e both are facultative anaerobes as reported by Dunlap \u003cstrong\u003e\u003csup\u003e26\u003c/sup\u003e\u003c/strong\u003e. The PTS system plays a crucial role in carbon and nitrogen metabolism. The PTS system allows the uptake of sugar in the phosphorylated form. The PTS system takes up the fructose as fructose-1-phosphate and then further transforms it into fructose-1-6-bisphosphate \u003cstrong\u003e\u003csup\u003e28-30\u003c/sup\u003e\u003c/strong\u003e. The genome sequencing (Results not shown here) also confirmed the presence of genes specific for fructose utilization required in the organism\u0026apos;s PTS system. The fructose is transported through the PTS system in\u003cem\u003e\u0026nbsp;Bacillus subtilis\u003c/em\u003e. The transport and phosphorylation of fructose are strictly regulated and involve several proteins \u003cstrong\u003e\u003csup\u003e31,32\u003c/sup\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.4 Effect of nitrogen source\u003c/strong\u003e- The addition of nitrogen source is expected to strongly affect the induction level of transaminase, as the enzyme is involved in nitrogen metabolism. Among all the nitrogen sources incorporated in the Luria broth, along with the 5mM of \u0026alpha;-MBA and supplemented with peptone was the best source with an enzyme activity of 4136.36\u0026plusmn;278.08 (Fig. 4). \u0026nbsp;The various nitrogen sources used were added by replacing tryptone in the Luria Bertani medium. \u0026nbsp;Generally, the organisms\u0026apos; uptake of peptone occurs via a specialized transport system. Supplementation of peptone in the growth media not only increases the cell number and enzyme production but also helps maintain the medium\u0026apos;s pH, making it favorable for bacterial growth \u003cstrong\u003e\u003csup\u003e33\u003c/sup\u003e\u003c/strong\u003e. Clay and co-workers reported the use of peptone in the medium for the production of \u0026omega;-transaminase \u003cstrong\u003e\u003csup\u003e34\u003c/sup\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.5 Effect of Temperature and pH:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemperature is one of the most critical parameters for the growth of organisms. The influence of different temperatures on the production of transaminase by the isolate AGSP-2 was investigated. The temperature of 37\u0026deg;C was found to be optimum, producing the maximum enzyme with an activity of 4053.99 U/ml on the 3rd day of incubation, as depicted in Fig. 5. An increase in the temperature above 37\u0026deg;C was detrimental to the growth of the organisms, leading to a decrease in enzyme production \u003cstrong\u003e\u003csup\u003e24,35,36\u003c/sup\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;pH also plays a vital role in growth and enzyme production, as both growth and enzyme secretion are sensitive and pH-dependent. \u0026nbsp;The production of the transaminase was carried out over a range of pH (4.0 to 9.0). Figure 6 depicts the maximal enzyme production at pH 7.0 with an enzyme unit of 4049 U/ml. A pH above 7.0 leads to a decrease in enzyme production. Xiang and colleagues reported similar results where they checked the pH profile for three different bacteria and found that among all, two transaminases, ATA-Gze from \u003cem\u003eGibberella zeae\u003c/em\u003e and ATA-Ate from \u003cem\u003eAspergillus terreus\u0026nbsp;\u003c/em\u003eshowed an optimum pH of 7.5 \u003cstrong\u003e\u003csup\u003e37\u003c/sup\u003e\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eNoshahri and team also reported that the optimum pH for transaminase production was 7 \u003cstrong\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/strong\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003eProduction studies of transaminase have not been conducted in a wide range. Low enzyme activities were detected at acidic and high alkaline pH. At lower pH (4,5 and 6), the organisms were able to grow but failed to produce a significant amount of enzymes. The change in the pH during the growth and production was observed during production. The pH shifted to a more alkaline level due to the release of ammonia by the organisms, leading to less growth and eventually affecting enzyme production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.6 Effect of Agitation speed:\u0026nbsp;\u003c/strong\u003eThe effect of agitation speed was investigated at a shake flask level by incubating the flasks at a range of 50,100,120, and 150 rpm. The impact of agitation on the production of transaminase is shown in Fig. 7. It was noticed that the optimum level of agitation needed for the maximum production of the enzyme was 120 rpm. The maximum enzyme production was found to be 4112.44 U/ml at 120 rpm. An increase in rpm above 120 leads to a decrease in transaminase activity, and also\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;RSM-CCD Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OFAT-optimized factors were verified by RSM-CCD analysis using Design Expert 13 software \u003cstrong\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/strong\u003e. These independent variables were added to the model in high and low ranges. Then, 30 runs generated by the software were performed in the laboratory, which was the experimental one. Table 2 represents the ANOVA analysis of the RSM-CCD design. The p-value is less than 0.05, and the F-value of 25.07 indicates the factors have a statistical correlation and significantly affect the response generated \u003cstrong\u003e\u003csup\u003e38,39\u003c/sup\u003e\u003c/strong\u003e. Table 3 represents the statistics of the model, where values like Predicted R\u0026sup2; and adjusted R\u0026sup2; are measured. The data revealed that the R\u0026sup2; value is 0.95, the adjusted R\u0026sup2; value is 0.92, and the predicted R\u0026sup2; value is 0.78. The higher R\u0026sup2; value indicates that the model is a perfect fit and that the model is capable of explaining all the variability. On the other hand, the adjusted R\u0026sup2; value suggests that the model is accurate and that all the predictors added have improved the model and made it a good fit. The predicted R\u0026sup2; value of the model tells us about the predictive ability of the model\u0026rsquo;s performance on the new data. The higher the predictive value, the higher the model\u0026apos;s accuracy in predicting the latest data. Apart from this, the other two critical parameters of the statistical model are the adequate precision value and the coefficient of variation (CV%). The proper precision value is 20.0354. A value greater than 4 is desirable, representing a signal-to-noise ratio, justifying that the model is a perfect fit and that the predictions are reliable with a strong signal. The CV% obtained is 8.12 and should be less than 10%. It is a ratio of the standard deviation to the mean response. The lower the value of %CV, the more consistent the results are with less variability (Bhatt et al., 2023). Noshahari et al. (2021) reported the same observation after statistical optimization using RSM-CCD, where they confirmed that \u0026alpha;-MBA helps in the induction of the \u0026omega;-transaminase enzyme. They reported that the transaminase activity (acetophenone formation) was at its maximum after 72 hours. The optimum culture conditions were 38\u0026deg;C, pH 7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2- ANOVA Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.722E+07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.659E+06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.0001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA-yeast extract\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.163E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.163E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-NaCl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e27367.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e27367.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.2581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.6188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-fructose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e35514.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e35514.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.5714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-peptone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.105E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.105E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.3236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3742.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3742.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.0353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.8535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.634E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.634E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e39556.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e39556.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.3730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.5505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e12182.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e12182.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.1149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.7393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.307E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.307E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.1609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.546E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.546E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.985E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.985E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e281.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.486E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.486E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.468E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.468E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.593E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.593E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e43.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.397E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.397E+06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePure Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.937E+05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.874E+04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.881E+07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e[df = Degree of freedom]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransaminase activity was as follow:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransaminase activity = 5,820.73 + 220.136 * A + 33.7687 * B + 38.4679 * C + -67.8404 * D + 15.2944 * AB + 150.709 * AC + -49.7219 * AD + 27.5931 * BC + -120.078 * BD + -244.258 * CD + -1,043.26 * A\u003csup\u003e2\u003c/sup\u003e + -404.397 * B\u003csup\u003e2\u003c/sup\u003e+ -403.588 * C\u003csup\u003e2\u003c/sup\u003e+ -409.2 * D\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 statistical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"348\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e325.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.9590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.9208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e20.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe 3D response surface plots and Contour plots display the interaction between the two independent variables while keeping other variables constant. These 3D surface graphical presentations show how two variables affect each other, positively or negatively, and will also tell the optimum point where the maximum response is generated. A total of 6 plots were generated, which show the interactions between the different variables. Factor A is yeast extract, factor B is NaCl, factor C is fructose, and factor D is peptone. The first plot generated by the design expert shows the interaction between factors AB (yeast extract and NaCl), AC (yeast extract and fructose), AD (yeast extract and peptone), BC (NaCl and fructose), BD (NaCl and peptone) and, CD (fructose and peptone) (Fig 8a-f). All the factors positively interacted with each other, and the tool found that at a maximum concentration of yeast extract (7.5 g/L), NaCl (7.5 g/L), Fructose (12 g/L), and peptone (12 g/L), the maximum response in terms of transaminase activity was generated.\u003c/p\u003e\n\u003cp\u003eAfter the statistical analysis, we compared the unoptimized media and optimized media for the transaminase production. There was a 2.8-fold increase in the enzyme activity (Fig. S4). We also validated this model using an AI tool, i.e., SVM. It is a supervised model with algorithms that help the user to analyze data in classification and regression analysis. The model is advanced with a Gaussian kernel function in the R programming language. Table S3- Represents the RSM-CCD design model generated with all 30 runs and the responses (Transaminase activity), i.e., the experimental, RSM-predicted, and SVM-predicted values. The SVM values are very close to the RSM-predicted values, which shows that the model is a perfect fit. AI validation was found helpful as it minimized the error in the RSM-CCD experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 HPLC detection-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHPLC analysis of the product formation in the reaction where both (S) and (R) enantiomers of \u0026alpha;-MBA were used revealed that product formation occurred with the \u003cem\u003e(S)\u003c/em\u003e-enantiomer only. It may be because the transaminase enzyme is \u003cem\u003e(S)\u003c/em\u003e- specific and can convert the \u003cem\u003e(S)\u003c/em\u003e isomer more efficiently than the \u003cem\u003e(R)\u003c/em\u003e isomer. Figure S5 represents the chromatogram of acetophenone. The retention time of acetophenone is 5.2, with a 53.32 % conversion of product, and the concentration of product formed is 106 ppm (substrate used was 200ppm), which indicates that the \u003cem\u003e(S)\u003c/em\u003e-enantiomer is more favoured as compared to the \u003cem\u003e(R)\u003c/em\u003e-enantiomer with a moderate excess.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acetophenone is an industrially important ketone compound which is used in fragrances and related products due to its sweet and floral aroma, as a flavouring agent in candies and chewing gum, precursor in the production of phenytoin (anticonvulsant), and used as a raw material in making resins used in eyeglass lenses, automotive parts, and electronics\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe isolate AGSP2, i.e., \u003cem\u003eBacillus inaquosorum\u003c/em\u003e, isolated from the Amlakhadi River, is a transaminase-producing bacterium and can produce amines and amino acids. The bacteria show maximum transaminase activity in MLB media supplemented with alpha-methylbenzylamine as the best substrate for the bacteria. A 1.5-fold increase in transaminase activity was achieved after the RSM-CCD analysis, along with the AI validation using the SVM tool. Transaminase is a crucial enzyme for pharmaceuticals as it can produce chiral compounds. The optimization of parameters helps enhance production, which may help carry out biotransformation reactions efficiently using different substrates. The biotransformation reaction carried out led to the synthesis of acetophenone, an industrially important ketone product. However, further experiments should be performed to check the affinity of enzymes towards other kinds of substrates, and also to scale up the reactions from a smaller to a larger scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval: \u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement:\u003c/strong\u003e The authors report there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution: \u003c/strong\u003eAll authors contributed to the article\u0026apos;s conception and design. The experimental work and data analysis were performed by Shreya Pandya. The first draft of the manuscript was written by Shreya Pandya and revised/ supervised by Urvish Chhaya and Akshaya Gupte. Conceptualization- [Shreya Pandya, Urvish Chhaya, Akshaya Gupte], Formal analysis- [Shreya Pandya], Writing- Original draft preparation- [Shreya Pandya]. Writing- Review and Editing- [Shreya Pandya, Urvish Chhaya, Akshaya Gupte], Supervision- [Urvish Chhaya, Akshaya Gupte]. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: \u0026nbsp;The datasets generated (Whole genome sequence) and analyzed during the current study are available in the NCBI repository.\u003c/p\u003e\n\u003cp\u003eBioSampleID-SAMN32292702\u003c/p\u003e\n\u003cp\u003eWGS project-JAPXFU01\u003c/p\u003e\n\u003cp\u003eBioProject- PRJNA913322\u003c/p\u003e\n\u003cp\u003eSubmitted GenBank assembly\u0026nbsp;GCA_028664335.1\u003c/p\u003e\n\u003cp\u003eNCBI RefSeq assembly- GCF_028664335.1\u003c/p\u003e\n\u003cp\u003eAssembly name ASM2866433V1\u003c/p\u003e\n\u003cp\u003eNCBI accession no- JAPXF4000000000\u003c/p\u003e\n\u003cp\u003eLink- https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_028664335.1/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank the Knowledge Consortium of Gujarat (KCG), Govt. of Gujarat for providing the SHODH fellowship [ScHeme Of Developing High quality research].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference no-\u003c/strong\u003e 202110822\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalik, M.S.; Park, E.S. and Shin, J.S. Features and technical applications of \u0026omega;-transaminases. \u003cem\u003eAppl. Microbiol. Biotechnol\u003c/em\u003e, \u003cstrong\u003e94\u003c/strong\u003e, 1163-1171. https://doi.org/10.1007/s00253-012-4103-3 (2012).\u003c/li\u003e\n\u003cli\u003eRossino, G.; Robescu, M.S.; Licastro, E.; Tedesco, C.; Martello, I.; Maffei, L.; Vincenti, G.; Bavaro, T. and Collina, S. Biocatalysis: A smart and green tool for the preparation of chiral drugs. \u003cem\u003eChirality\u003c/em\u003e, \u003cstrong\u003e34\u003c/strong\u003e(11), 1403-1418. https://doi.org/10.1002/chir.23498 (2022).\u003c/li\u003e\n\u003cli\u003eFerrandi, E.E. and Monti, D. Amine transaminases in chiral amines synthesis: recent advances and challenges. \u003cem\u003eWorld J. Microbiol. 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Basic Microbiol\u003c/em\u003e, \u003cstrong\u003e50\u003c/strong\u003e(4), 325-335.\u003cstrong\u003ehttps://doi.org/10.1002/jobm.200900333\u003c/strong\u003e (2010).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chiral compounds, biocatalyst, Bacillus inaquosorum, RSM-CCD, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-7609083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7609083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eω-transaminases are PLP-dependent enzymes able to catalyze the synthesis of various chiral amines, an important building block in the pharmaceutical industry. Here we describe the isolation and optimization of a wild-type strain of \u003cem\u003eBacillus\u003c/em\u003e species (\u003cem\u003eBacillus inaquosorum\u003c/em\u003e AGSP2) isolated from an industrially polluted site of Amlakhadi, Gujarat, India. The isolate AGSP2 is a chiral amine producer and can synthesize a wide variety of compounds (Aldehydes, ketones, amino acids, and amines). The optimization was performed using the OFAT method (One Factor at a Time), followed by RSM-CCD (Response Surface Methodology-Central Composite Design), with further validation by an AI (Artificial Intelligence) tool, SVM (Support Vector Machine). The media optimized by statistical means were designated as Modified Luria Bertani (MLB) medium, which contains fructose, NaCl, yeast extract, and peptone supplemented with α-MBA. An overall 2.8-fold increase in transaminase production was observed with an enzyme activity of 6121.88\u0026thinsp;\u0026plusmn;\u0026thinsp;42 U/ml. The other optimized parameters were temperature, pH, agitation speed, and inoculum size. AGSP2 is an \u003cem\u003e(S)\u003c/em\u003e- selective ω-transaminase producer and has synthesised acetophenone using \u003cem\u003e(S)\u003c/em\u003e-α-Methylbenzylamine with a 64.35% conversion and 51% of enantiomeric excess.\u003c/p\u003e","manuscriptTitle":"Harnessing Bacillus inaquosorum AGSP2 for Enhancing ω-Transaminase Production Through Classical and AI-Supported Statistical Design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 13:19:49","doi":"10.21203/rs.3.rs-7609083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T13:24:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T04:03:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T12:48:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T01:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187484821721175317170633383931760280993","date":"2025-10-18T07:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68167179054887778565402713375084911655","date":"2025-10-15T07:25:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278074207517238542456364245468164380334","date":"2025-10-15T07:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119764840942855824223588404905049401146","date":"2025-10-15T07:17:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170428148871767818434706521699421166839","date":"2025-10-14T05:25:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T14:14:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336355048579189438967144331899170911096","date":"2025-10-13T14:10:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263710331607502170821900973453185486938","date":"2025-10-13T07:53:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147838731345459542555868077620202197033","date":"2025-10-13T07:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82095245406868931700732832252614900148","date":"2025-10-13T07:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-27T08:32:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T08:19:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T10:15:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T08:10:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-16T08:07:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16b73b64-5249-41ce-a358-784ad3370f4b","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55881495,"name":"Biological sciences/Biochemistry"},{"id":55881496,"name":"Biological sciences/Biotechnology"},{"id":55881497,"name":"Physical sciences/Chemistry"},{"id":55881498,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-04-20T16:00:51+00:00","versionOfRecord":{"articleIdentity":"rs-7609083","link":"https://doi.org/10.1038/s41598-026-46062-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-18 15:57:40","publishedOnDateReadable":"April 18th, 2026"},"versionCreatedAt":"2025-10-09 13:19:49","video":"","vorDoi":"10.1038/s41598-026-46062-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-46062-3","workflowStages":[]},"version":"v1","identity":"rs-7609083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7609083","identity":"rs-7609083","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}