Study on Optimal Production Conditions of Fibrinolytic Kinase Derived from the Nereid Worm, Perinereis aibuhitensis Grub

Abstract

The fibrinolytic kinase identified in the nereid worm (Perinereis aibuhitensis Grub) displays exceptional kinase activity, stability, and specificity, suggesting its potential as a promising candidate for the advancement of new thrombolytic drugs. In this study, a process was optimized for the production of fibrinolytic kinase using Escherichia coli, and the effects of factors such as inoculum, pH, OD, temperature, inducer concentration, and time on the protein yield were investigated. The optimum points of key parameters were determined by single-factor experiments, and the initial pH, OD, and time were determined to be significant by PB (Plackett–Burman design) with six factors at two levels of experiments. The response surface experiments highlighted the key roles of initial pH and induced OD values, and the convergence of the model and experimental data confirmed the optimal conditions and reasonable fluctuation intervals, which proved the reliability of the model.

1. Introduction

The fibrinolytic kinase is a kind of thrombolytic kinase enzyme extracted from a nereid worm (Perinereis aibuhitensis Grub), which belongs to the field of biopharmaceutical technology [1]. Its molecular weight is between 25,000 Da and 55,000 Da, and its isoelectric point is between three and seven [2,3]. Currently, there is few relevant research focusing on fibrinolytic kinase in international settings, with limited publications available in China. The specific methods of separation and purification of this enzyme can be found in the papers of Ting, W [4]. Nereid fibrinolytic kinase has excellent kinase activity, not less than that of urokinase. It can activate plasminogen, which degrades fibrin by indirect means. This mechanism of action gives fibrinolytic kinase a significant advantage in thrombolytic therapy. Nereid fibrinolytic kinase has good thermal and pH stability. Nereid kinase can maintain a higher activity at 37 °C, and even after one hour of treatment at 60 °C, its activity can still maintain more than 90% [5,6]. This excellent stability makes it possible to be used as a new thrombolytic drug suitable for oral administration, instead of intravenous injection as in the case of urokinase. Nereid fibrinolytic kinase is also highly specific and can efficiently hydrolyze the α-chain of plasminogen. This property enhances its effectiveness in the treatment of thrombotic diseases [7]. From the application point of view, nereid fibrinolytic kinase can be used in the production of oral drugs for the treatment of thrombotic diseases caused by cerebral thrombosis, myocardial infarction, and other thrombotic diseases. With its abundant source of raw materials and low price, it is expected to become a regular therapeutic drug. In this experiment, recombinant E. coli with fibrinolytic kinase DNA (GenBank No.: AHZ01188.1) was used in the laboratory to study the optimal fermentation conditions [8,9] to produce fibrinolytic kinase.

Single-factor, PB (Plackett–Burman design), and response surface experiments are commonly used in process optimization. Single-factor experimentation is simple and straightforward; it focuses on the effect of a single variable on the process result and observes the result change by fixing the other factors and changing the level of only one factor [10]. This method can quickly determine the best value range of a factor, but it cannot take into account the interaction between factors [11,12]. PB experimentation is an efficient screening method, which is used to quickly identify the key influencing factors from many factors. It quickly assesses the contribution of each factor to the result by constructing a specific experimental matrix so that each factor is tested at both high and low levels [13]. PB experiments are suitable for the initial screening stage and guide subsequent in-depth studies [14,15]. Response surface design is a more systematic and comprehensive optimization method. It carries out experiments by selecting different combinations of multiple factors in the experiment space and uses experimental data to build a mathematical model to describe the relationship between factors and results [16]. By analyzing the model, the optimal process conditions can be determined and the interaction between the factors can be evaluated. In practical applications, these three methods are often combined [17,18].

In this study, by precisely controlling the change in each variable [19], the changing trend of protein yield under different conditions could be observed, and the influence degree and rule of each parameter on protein yield could be preliminarily determined [20,21,22]. The outcomes of these single-factor experiments have not only furnished us with crucial experimental data but also established a strong basis for the following PB experiments to identify the primary effector factors affecting protein quantity [23,24,25]. Therefore, this study proposed identifying the main effect factors in the response surface design.

2. Materials and Methods

2.1. Raw Materials and Reagents

Yeast powder (OXOID, item No. LP0021B, Basingstoke, UK), tryptone (OXOID, item No. LP0042), kanamycin sulfate (Shenggong Biotechnology, item No. A600286-0025, Shanghai, China), IPTG (Beyotime, item No. ST1416, Shanghai, China), Tris (Shenggong Biotechnology, item No. A100826-0500, Shanghai, China), Tris-HCL (Shenggong Biotechnology, item No. A610103-0250), dipotassium hydrogen phosphate (Shanghai Titan, item No. G82678E, Shanghai, China), potassium dihydrogen phosphate (Shanghai Titan, item No. G82821A), glycerin (Shanghai Titan, item No. 01271856, Shanghai, China), colored pre-stained protein marker (Tanon, item No. 180-6006), Caulmers Brilliant Blue G250 (Sangong, item No. A100615-0005, Shanghai, China), anhydrous ethanol (Nanjing Reagent, Article No. C06901555010, Nanjing, China), glacial acetic acid (Jinke Chemical, item No. 02030074, Shaoxing, China), isopropyl alcohol (AMRESCO, item No. 67-63-0, Framingham, MA, USA), plasminogen (Shanghai Kuan-Dong Biological, Shanghai, China), urokinase (Shanghai Guandong Bio, UPA005, Shanghai, China), TEV protease (Novozymes, no. JE1006-01, Hong Kong, China), and fibrin analogues (AAT Bioquest, No. 13201, Guangzhou, China) were used in this study.

2.2. Media, Growth Conditions, and Chemicals

Seed (LB) medium was prepared by using 10 g/L of tryptone, 5 g/L of yeast powder, 10 g/L of sodium chloride, 50 μg/mL of kanamycin final concentration, and 50 mL/250 mL of liquid filling. The culture conditions were as follows: the glycerin tube strains were inoculated by a constant temperature incubator (Shanghai Yiheng Scientific Research Equipment, Shanghai, China, model DHP-9272) into the seed medium at 1% inoculation rate, 37 °C, and oscillated at 250 rpm to OD600 1.5 detected by ultraviolet spectrophotometer (Shanghai Yuan analysis instrument, Shanghai, China, model V-5000) for later use.

Fermentation (TB) medium was prepared by mixing 12 g/L of peptone, 24 g/L of yeast powder, 2.31 g/L of potassium dihydrogen phosphate, 12.54 g/L of dipotassium hydrogen phosphate, 4 g/L of glycerin, 50 μg/mL of kanamycin final concentration, and 50 mL/250 mL of liquid capacity. The culture conditions were as follows: The seed solution was inoculated at a pH value of 7.0, and the inoculated amount was 5% and oscillated at a rate of 220 rpm at 37 °C to OD600 1.5. Then, on this basis, IPTG was added, the final concentration was adjusted to 0.2 mM and oscillated at a rate of 220 rpm for 16 h at 25 °C.

E. coli strain was prepared in the marine biopharmaceutical group of Shanghai Ocean University (plasmid: pET28a(+)(Takara), expression host: BL21*(DE3) (Takara)). A solution of E. coli was prepared by impaling E. coli with a toothpick, which was then submerged in LB liquid medium and left to incubate for 16 h at 37 °C. To create a Caulmers Brilliant Blue R250 dye solution, 1 g of Caulmers Brilliant Blue R250 was combined with 650 mL of water, 250 mL of isopropyl alcohol, and 100 mL of glacial acetic acid. One liter of decolorizing solution was made by mixing 850 mL of water, 50 mL of ethanol, and 100 mL of glacial acetic acid. For the urokinase standard solution, urokinase was dissolved in 1.0 mL of sterile deionized water to achieve a concentration of approximately 10,000 U/mL. The mixture was gently shaken to ensure homogenization and prevent air bubbles. After standing at room temperature (18–25 °C) for at least 15 min, the solution was well shaken and then diluted with a specific buffer to the desired working concentration.

2.3. Method

2.3.1. To Investigate the Effect of Condition on Protein Amount

Single Factor: In single-factor experimental design, a single factor is controlled to vary at different levels, and the other factors are held constant. In total, 50 mL of liquid medium and 2.5 mL E. coli solution are added to a sterile 250 mL flask on an ultra-clean workbench, inoculated with the bacterial solution. Design parameters are detailed in

Table 1. Conditions for inspection.

Factors	Lane	Inoculum Amount	Initial pH	Induced OD	Induction Temperature	Concentration of the Inducer	Induction Time
Inoculum quantity	1	1%	7.3	1.5	25 °C	0.2 mM	16 h
	2	3%
	3	5%
	4	7%
	5	9%
Initial pH	1	5%	6.3	1.5	25 °C	0.2 mM	16 h
	2		6.8
	3		7.3
	4		7.8
	5		8.3
Induced OD value	1	5%	7.3	0.4	25 °C	0.2 mM	16 h
	2			1.5
	3			2.6
	4			3.4
	5			4.2
Induction temperature	1	5%	7.3	1.5	18 °C	0.2 mM	16 h
	2				25 °C
	3				30 °C
	4				37 °C
Concentration of inducer	1	5%	7.3	1.5	25 °C	0.1 mM	16 h
	2					0.2 mM
	3					0.4 mM
	4					0.6 mM
	5					0.8 mM
Induction time	1	5%	7.3	1.5	25 °C	0.2 mM	4 h
	2						8 h
	3						12 h
	4						16 h
	5						20 h
Medium	1	5%	7.3	1.5	25 °C	0.2 mM	16 h
	2

.

PB Experiment: Find the best level point of each factor from the single factor and use the level of the best point as 0, the level before the best point as −1, and the level after the best point as 1 to design the PB experiment. The statistical software JMP, widely recognized in the industry, was used to design the PB experiment with 6 factors at 2 levels (

Table 2. Single-factor level set and PB experimental table.

Factor						Levels
					−1	0	1
Inoculum amount					3	5	7
Initial pH					5.8	6.8	7.8
Inducing OD					1.5	2.45	3.4
Induction temperature					20	25	30
Inducer concentration					0.05	0.1	0.15
Induction time					12	16	20
Modes	Inoculum amount	Initial pH	Induced OD	Induction temperature	Concentration of the inducer	Induction time	Amount of protein (mg/mL)
- + - + - -	−1	1	−1	−1	1	−1	0.7
++++++	1	1	1	1	1	1	1.2
- + + + - -	−1	1	1	1	−1	−1	1.1
+ + + - - -	1	1	1	−1	−1	−1	1.15
+ - + + + -	1	−1	1	1	1	−1	1.37
- + - + + +	−1	1	−1	1	1	1	1.1
0000000	0	0	0	0	0	0	1.1
- - - + + +	−1	−1	1	−1	1	1	1.8
+ + - - - -	1	−1	−1	−1	1	−1	0.65
- - - + - +	−1	−1	1	−1	−1	1	1.8
+ - - - + +	1	−1	−1	1	−1	1	1
+ + + - - -	1	1	−1	−1	−1	1	1.1
+ - - - - -	−1	−1	−1	1	−1	−1	1

Note: -: Level −1; 0: Level 0; +: Level 1.

), in order to find the main effect factor.

Response Surface Experiment: The response surface experiment with 3 factors at 2 levels (

Table 3. Response surface factor level set and response surface experiment table.

Factor		Levels
		−1	0	1
Initial pH		5.8	6.8	7.8
Inducing OD		1.5	2.6	3.4
Induction time		12	16	20
Patterns	Induced OD	Initial pH	Induction time	Amount ofprotein(mg/mL)
+ 0 -	1	0	−1	1.5
+ - 0	1	−1	0	1.8
0	0	0	0	2.1
0 - -	−1	−1	0	1.8
0 + +	0	1	1	1.2
- + 0	−1	1	0	1.1
000	0	0	0	2.1
000	0	0	0	2.15
0 - -	0	−1	−1	2.25
+ + 0	1	1	0	1.1
- 0 +	−1	0	1	2
+ 0 +	1	0	1	1.8
- 0 -	−1	0	−1	2.2
0 + -	0	1	−1	1.2
0 - +	0	−1	1	2.4

Note: -: Level −1; 0: Level 0; +: Level 1.

) is designed according to the main effect factors screened by PB experiment, and the best combination points were found in each main effect to maximize the yield. JMP (Pro 16) software is also used to analyze the response surface experiment’s results.

2.3.2. Determination of Protein Quantity

Following the completion of fermentation, the fermentation liquid was moved to a 50 mL centrifuge tube and then centrifuged at 12,000 rpm for 20 min. The bacteria were then weighed, with 0.5 g of bacteria subsequently added to 50 mL of 20 mM Tris-HCL pH 8.0 for suspension. The mixture was then crushed by 800–900 homogenates in a high-voltage homogenizer (Antosi Nanotech, AH-MINI, Suzhou, China) at 4 °C for 3 cycles. The supernatant was collected by centrifugation of the homogenate at 12,000 rpm for 5 min using a floor-type high-speed refrigerated centrifuge (Hunan Kecheng instrument machinery and equipment, specifications H6-10KR, Changsha, China). The protein quantity of the supernatant was determined by SDS-PAGE and grayscale scanning.

Loading and Electrophoresis: Following the addition of 10 μL of the previously obtained supernatant to the loading sample wells using a pipette (Eppendorf, 2~20 µL, Shanghai, China) in precast discontinuous gradient polyacrylamide gels (GenScript SurePAGE, Cat# M00655, Nanjing, China), electrophoresis was carried out at 160 V for 50 min in an electrophoresis apparatus (Tanon, Cat#EPS-300, Zhangjiakou, China).

Dyeing and Decolorization: After the electrophoresis process, a staining box was filled with tap water, and the gel was gently transferred into the water using a rubber block. Once the water was poured out, the staining solution was added, and the gel block was submerged. The block was then oscillated at 60 rpm on a flat plate oscillator for 60 min. Subsequently, the staining solution was removed, and the destaining solution was added. The gel block was oscillated at 60 rpm on a flat plate oscillator, with the destaining solution being changed every hour until the destaining process was complete. Following destaining, the gel images were analyzed using a gel imager.

Imaging and Analysis: The gel was positioned in the designated location of the imager (Tanon, Cat#1600, Zhangjiakou, China), and the imaging software AllDoc_x 6 was utilized for analysis. The essential parameters for image analysis are saved and detailed in

Table 4. Key parameters of SDS-PAGE image analysis.

Parameter Name	Set Value
Strip sensitivity	0.50–5.0 (can be adjusted according to the actual situation; ensure that all visible strips are identified and only a small number of strip identifiers in the blank lane ≤5)
Select background mode	Peak–valley connection
Baseline connections	1% (can be adjusted according to the actual situation so that the baseline is level, and the background is completely deducted)
Molecular weight regression model	Exponential regression
Quantitative approach	Lane total/relative amount (tick)
Results report form	The “relative quantity” and “IOD” forms are saved separately.
Protein amount results	The IOD value is the gray value

.

2.3.3. Fibrinolytic Kinase Enzyme Activity Test

Fibrinolytic kinase enzyme activity formulations are utilized to assess the activation of plasminogen by testing the enzyme activity of the kinase as outlined in

Table 5. Standard curve and fibrinolytic kinase enzyme activity test formulation.

	Reagent	Volume
Standard curve formulation	Assay Buffer	48 μL
	Substrate	2 μL
	Plasminogen	2 μL
	Urokinase	0.0625 U/μL	0.125 U/μL	0.25 U/μL		0.5 U/μL	1 U/μL
	Assay Buffer	to 100 μL
Fibrinolytic kinase enzyme activity test formulation		Samples			Positive reference material
	Assay Buffer	48 μL
	Substrate	2 μL
	Plasminogen	2 μL			2 μL
	Urokinase (enzyme)	N/A			1 μL
	Enzyme	Sample (100 ng/μL) 25 μL			N/A
	Assay Buffer	to 100 μL

. To conduct the test, a 96-well enzyme labeling plate is taken, and 2 μL of plasminogen, 2 μL of fibrin analogues, and 96 μL of assay buffer are added to each well. Subsequently, 1 μL of the sample to be tested is added to the system, which is then placed into the Varioskan LUX Functional Labeling Instrument (thermos, Cat# 3020, Shanghai, China) immediately. The fluorescence is measured at 37 °C at 1 min intervals for 30 min (Ex/Em = 360/450 nm). The fluorescence value at 30 min is recorded, and the fibrinolytic kinase activity (U/μL) is determined using the standard curve (Figure 1) and fluorescence value. If the concentration of the sample protein is known, the specific activity of fibrinolytic kinase (U/mg) can also be calculated.

2.4. Statistical Analysis

All the experiments were conducted with triplicate samples, and a concordant value was obtained from these experiments. The values were expressed as means ± SD unless otherwise indicated. The statistical difference and comparisons were made using one-way ANOVA analysis, and a p-value less than 0.05 was noted as statistically significant after treating the data in GraphPad Prism 9.4.0 and JMP 16 software.

3. Results

3.1. Influence of Inoculation Amount on Protein Yield

Based on the findings of the Figure 2a SDS-PAGE, it is evident that the highest protein yield is achieved when the inoculated amount is either 3% or 5%. This conclusion is further supported by the bacterial wet weight data presented in

Table 6. Grey scale results for different levels.

Lane	1	2	3	4	5
Inoculum levels	940,060	1,545,229	1,546,275	1,245,352	1,528,400
Initial pH	1,234,161	1,241,108	1,726,000	1,140,751	900,727
Induced OD	691,923	750,498	770,571	789,153	830,191
Induced temperature	817,865	1,011,067	842,907	855,989
Inducer concentrations	837,613	791,344	778,578	750,677	751,467
Induced time	966,524	856,401	656,099	792,244	521,263
Medium	1,011,067	693,268

and Figure 2b, which clearly show that the bacterial wet weight is maximized at a 5% inoculated amount. As a result, 5% has been identified as the ideal inoculation amount.

3.2. Influence of Initial pH on Protein Yield

Based on the findings from the SDS-PAGE analysis depicted in Figure 3a,b and Table 6, it is evident that the maximum protein production occurs at a pH of 6.8. These data determined that the maximum wet weight of bacteria is achieved at a pH of 6.8. Consequently, it is advisable to establish the ideal starting pH at 6.8 in most cases.

3.3. Influence of OD Induction on Protein Yield

The SDS-PAGE outcomes did not show clear differences, indicating that there was no statistically significant variance in protein production in this case. As per the data in Table 6, it is evident that the highest induced OD is 4.2, followed by 3.4 and 2.6. When considering the bacterial wet weight graph in Figure 4b, it is apparent that the wet weight of the bacteria was highest under OD 2.6 induction. Consequently, overall, the most suitable induced OD appears to be 2.6.

3.4. Influence of Induction Temperature on Protein Yield

Based on the SDS-PAGE comparison results in Figure 5a, it is evident that the protein yield was highest when the induction temperature was set at 25 °C. This finding is supported by the data in Table 6, which show that the wet weight of bacteria is also greatest at 25 °C, providing further evidence that this temperature is optimal for induction.

3.5. Influence of Inducer Concentration on Protein Yield

Based on the findings from Figure 6a,b and Table 6, it is evident that the highest protein yield occurs at an inducer concentration of 0.1 mM. Additionally, there is no notable variance in the wet weight of bacteria across the various inducer concentrations tested. Consequently, the ideal inducer concentration is determined to be 0.1 mM.

3.6. Influence of Inducer Time on Protein Yield

Based on the findings from the SDS-PAGE in Figure 7a, it is evident that the protein yield peaked at 4 h after induction, with slightly lower yields at 8 h and 16 h after induction. However, when considering the bacterial wet weight data in Table 6 and Figure 7b, the wet weight of the bacteria is highest at 16 h after induction. Therefore, the optimal induction time is determined to be 16 h.

3.7. Influence of Medium on Protein Yield

It was observed that TB and LB media showed minimal variance in protein yield (Figure 8a,b and Table 6). However, LB medium exhibited a lower bacterial wet weight compared to TB medium. Therefore, TB medium is a more appropriate choice for induction.

3.8. PB Experimental Results

Based on the single-factor experiment screening results, the −1 and 1 level points are presented in Table 2. The PB experiments were created, and the protein amount was input. The statistical findings in

Table 7. Fitting and effects summary.

R Square	0.910735
Adjust R square	0.82147
Root-mean-square error	0.144439
Response mean	1.159231
Number of observations (or weight sum)	13
The source	LogWorth	p value
Induced OD	2.914	0.00122
Induction time	2.176	0.00667
Initial pH	1.355	0.04419
Inoculum amount	1.069	0.08523
Induction temperature	0.374	0.42312
Concentration of inducer	0.272	0.53403

reveal that the R-square value of the simulated equation exceeds 0.9, confirming the accuracy of the PB experiment simulation results and the feasibility of implementing the screened outcomes in subsequent experiments. As noted in Table 7, the p-values of induction OD, induction time, and initial pH were all below 0.05, indicating their significant impact on the final experimental outcomes as primary factors.

3.9. Response Surface Experiment Results

According to the PB experimental results, response surface experiments with three factors and two levels were designed. The information of factors and levels is shown in Table 3. After defining the corresponding −1 and 1, the response surface experiment table as shown in Table 3 was designed by JMP (Pro 16), and the final protein quantity value was filled in. According to the calculation by statistical software, the mathematical model simulates well and can be used to predict the optimal conditions, because the R square is greater than 0.9 (

Table 8. Fitting and effects summary.

R Square	0.932067
Adjusted R square	0.809787
RMS error	0.196744
Response mean	1.78
Number of observations (or weight sum)	15
The source	LogWorth	p value
Initial pH	2.908	0.00123
Initial pH* Initial pH	1.901	0.01256
Induce OD* Induce OD	1.372	0.04247
Inducing OD	0.778	0.16673
Induced OD* Induced time	0.585	0.25975
Induction time	0.173	0.67205
Initial pH* Induction time	0.143	0.71871
Induction time * Induction time	0.129	0.74349
Induce OD* Initial pH	0.000	1.00000

Note: *: Interaction between two factors.

), and the predicted value–actual value graph (Figure 9) shows that the actual value is basically within the predicted range.

Table 8 presents the impact of different factors on the final outcome. The p-value column indicates that the p-values for initial pH, initial pH*initial pH, and induced OD*induced OD are all less than 0.05, signifying a significant influence on the final result. The 3D response surface diagram generated by the software (Figure 10a) clearly illustrates an optimal correlation between initial pH and induced OD in terms of interaction. Subsequently, the profiler function in JMP software was utilized to forecast the maximization goal, aiming for the highest possible protein quantity at a specific ratio. Once the maximization objective is established, the software identifies the levels at which each factor can maximize protein production. As depicted in Figure 10b, the optimal level for initial pH is −0.6337 (6.17), the ideal induction time level is 1 (20 h), and the optimal induction OD level is 0.02256 (2.62). Additionally, the software accounts for practical constraints by providing a standard deviation (SD) value of 0.4, indicating a range within which the optimal levels of different factors can be adjusted during actual operations without compromising the final yield. In conclusion, the initial pH is targeted at 6.17 ± 0.4, the induction time at 20 h ± 0.4, and the induction OD at 2.62 ± 0.4.

3.10. Fibrinolytic Kinase Activity Results

Figure 1 illustrates the successful establishment of the relationship between urokinase concentration and its activation activity. The linear relationship between urokinase concentration and activation activity is accurately represented by the equation y = 72.07x + 347.8. Notably, the high R-square value of 0.9182 indicates a strong linear fit for the equation, with data points closely clustered around the regression line and minimal deviation. Based on the linear equation, the activity of recombinant fibrinolytic kinase was approximately 15 U/uL, surpassing that of urokinase at 10 U/uL.

4. Discussion

The present study precisely measured fibrinolytic kinase activity based on the activation of fibrinogen through fibrinolytic kinase and urokinase, which in turn produces fibrinolytic enzymes. Fibrinolytic kinase serves as a crucial biological enzyme capable of hydrolyzing and synthesizing fibrinolytic substrates. During this process, it generates the fluorophore AMC (7-amino-4-methylcoumarin), a fluorescent compound that enables quantitative detection through a specific fluorescent enzyme marker. To enhance the precision and dependability of our experimental findings, a standard curve representing urokinase enzyme activity was constructed. This curve is derived from a range of urokinase samples with established enzyme activity levels. By measuring the AMC released from these samples under consistent conditions, the relationship between enzyme activity and AMC release was illustrated. Utilizing this standard curve allows one to indirectly assess the enzyme activity level of fibrinolytic kinase by evaluating the amount of AMC released from an unknown sample.

After conducting numerous multi-dimensional experiments, the crucial factors influencing E. coli fermentation of fibrinolytic kinase were extensively investigated [26,27,28]. These factors primarily include inoculum amount, pH, OD, temperature, and others [29,30,31]. The objective of these experiments was to uncover the specific impacts of each parameter on the protein yield of the kinase protein of nereid. Key parameters such as optimal inoculation amount, initial pH, induction OD, induction temperature, concentration of inducer, and induction time were determined through single-factor analysis in this study to establish the most suitable induction medium: TB [32,33,34]. The selected values were 5% for inoculation amount, 6.8 for initial pH, 2.6 for induction OD, 25 °C for induction temperature, 0.1 mM for the concentration of the inducer, and 16 h for induction time. Subsequently, a PB experiment was designed based on the single-factor results. The statistical analysis of the experimental data revealed that the PB experiment simulation results were highly accurate, with an R-square value exceeding 0.9, providing a robust theoretical foundation for our future experiments [35,36]. Notably, induction OD, induction time, and initial pH were identified as having the most significant impact on the experimental outcomes, with p-values below 0.05 for all three factors. Consequently, a response surface experiment was devised to further investigate the interaction among these factors [37]. By fixing the other parameters at the optimal levels determined by the single-factor experiment, a two-level design was implemented for initial pH, induced OD, and induced time in the response surface experiment [38,39]. Utilizing JMP software for simulation and calculation, this study developed a relatively precise mathematical model capable of effectively predicting the optimal conditions. Based on the comparison chart between the predicted and actual values, it is evident that the actual value falls within the predicted range, confirming the model’s reliability.

Compared with other similar experimental cases, PB and response surface experiments were introduced in this experiment [40,41]. Systematic statistical software was used for simulation and prediction, and the optimal combination of various factors under the interaction was studied in a more detailed manner, instead of just using a simple single factor to summarize the optimal combination. The results of response surface experiments indicate that the initial pH, initial pH squared term, and induced OD squared term significantly impact the experimental outcomes. The 3D response surface diagram visually displays the optimal combination of initial pH and induced OD interaction [42]. By utilizing the profiler function of JMP software, the optimal level of protein amount can be accurately predicted, taking all factors into consideration for optimal results. To accommodate operational convenience and real-world scenarios, a fluctuation range for the optimal level value of each factor is provided, with an SD value of 0.4. Previously, Vijayaraghavan and Prakash utilized an economical fermentation medium to produce a potential fibrinolytic enzyme from a newly isolated marine bacterium, Shewanella sp. IND20. They determined that the enzyme had a molecular weight of 55.5 kDa, with optimal pH and temperature conditions of 8.0 and 50 °C, respectively [43]. Similarly, Hu et al. developed a protocol for generating a novel and highly effective fibrinolytic enzyme from Bacillus subtilis DC27, which was isolated from Douchi, a traditional Chinese fermented soybean product. This was achieved using Luria–Bertani medium at 37 °C for 72 h [44]. Additionally, Simkhada et al. reported the production of a novel fibrinolytic protease from Streptomyces sp. CS684, identifying maximum activity at 45 °C and a pH range of 7 to 8 [45]. Consistent with the current study, various researchers have documented the optimal experimental conditions and fermentation processes for producing novel fibrinolytic proteases from different sources such as Bacillus sp. strain CK 11-4 [46], hepatic caeca of Asterina pectinifera [47], Bacillus subtilis QK02 [48], Bacillus subtilis ZA400 [49], Bacillus pumilus BS15 [50], Serratia sp. KG-2-1 [51], Komagataella phaffii [52], and Bacillus subtilis Egy [53,54].

In the present study, the enzyme activity of fibrolytic kinase was systematically optimized to 15 U/uL, which is a notable improvement over the previously reported 8 U/uL [55], indicating that process optimization significantly enhances enzyme activity.

5. Conclusions

This study concluded that the highest protein yield is obtained with a bacterial inoculated amount of 3 to 5%, induction temperature of 25 °C, 4 h induction time, pH of 6.8, and 0.1 mM inducer concentration. An induced OD of 2.6 is considered the most suitable. Among the different media tested, LB medium showed lower bacterial wet weight, while TB medium emerged as the best choice for induction. Results from response surface experiments indicated that the optimal initial pH level is −0.6337 (6.17), optimal induction time level is 1 (20 h), and optimal induction OD level is 0.02256 (2.62), with a standard deviation of 0.4 to address operational variability. Accordingly, these findings will offer valuable support and guidance for future experiments, enhancing the stability and reliability of the results.