Affinity chromatography method for purifying recombinant protein of engineering algal strain

Abstract

The invention provides an affinity chromatography method for purifying recombinant protein of an engineering algal strain, belongs to the technical field of bioinformatics and biosynthesis, and provides the affinity chromatography method for purifying the recombinant protein of the engineering algal strain. Which comprises the steps of engineering algal strain culture, cell collection, lysate treatment, ultrasonication, centrifugal filtration, affinity chromatography, molecular sieve purification, quality detection and the like, and is characterized in that accurate control of fragmentation parameters is realized by adopting an ultrasonication optimization equation set, and optimal parameters of a chromatography process are determined by a shortest path optimization method; the method realizes efficient purification and activity maintenance of the recombinant protein, is suitable for preparation of various membrane proteins and soluble fragments thereof, and solves the technical problem in the prior art that the recombinant protein expressed by an engineering algal strain is easy to inactivate in the ultrasonication process.

Description

Technical Field

[0001] This invention belongs to the fields of bioinformatics and synthetic biology, and specifically relates to an affinity chromatography method for purifying recombinant proteins from engineered algal strains. Background Technology

[0002] Purification technology for recombinant proteins from engineered algae is an important research direction in biopharmaceutical and clinical diagnostic fields, with ultrasonic disruption being a key step in extracting intracellular recombinant proteins. Traditional recombinant protein purification methods mainly include cell culture, cell disruption, affinity chromatography, and molecular sieve purification. Among these, ultrasonic disruption is widely used in laboratory and industrial production due to its ease of operation and low equipment cost. In the purification process of recombinant proteins from engineered algae, ultrasonic disruption technology uses the mechanical force, shear force, and localized high temperature generated by cavitation to disrupt the cell wall and cell membrane, thereby releasing the target intracellular protein. Currently, ultrasonic disruption technology has been successfully applied to the preparation of various recombinant proteins, including the extraction of bioactive proteins such as enzymes, antibodies, and growth factors.

[0003] However, traditional ultrasonic disruption technology has significant drawbacks. First, the localized high temperatures and free radicals generated during ultrasonic disruption can cause protein denaturation, significantly reducing its biological activity. Second, the setting of ultrasonic disruption parameters relies heavily on experience, lacking scientific optimization methods, making it difficult to achieve a balance between disruption efficiency and protein activity. Furthermore, existing ultrasonic disruption technologies cannot monitor the degree of protein denaturation in real time, making it impossible to adjust disruption parameters promptly, resulting in uncontrollable loss of protein activity. These problems are particularly prominent in the purification of recombinant proteins from engineered algae strains because algal cell walls are thick and require high ultrasonic intensity, which further increases the risk of protein denaturation.

[0004] Currently, researchers mainly employ methods such as reducing ultrasonic power, shortening processing time, and adding protective agents to reduce protein denaturation in response to the aforementioned problems. However, these methods often come at the cost of sacrificing disruption efficiency, leading to a decrease in protein extraction rate. Furthermore, due to the lack of systematic parameter optimization methods and real-time monitoring tools, the effects of these improvements are often unstable and difficult to replicate. Therefore, how to maximize protein activity while ensuring disruption efficiency has become a critical technical problem that urgently needs to be solved in the purification of recombinant proteins from engineered algae. In other words, existing technologies suffer from the technical problem that recombinant proteins expressed by engineered algae are easily inactivated during ultrasonic disruption. Summary of the Invention

[0005] In view of this, the present invention provides an affinity chromatography method for purifying recombinant proteins from engineered algae strains, which can solve the technical problem in the prior art that recombinant proteins expressed by engineered algae strains are easily inactivated during ultrasonic disruption.

[0006] This invention is implemented as follows: An affinity chromatography method for purifying recombinant proteins from engineered algal strains includes: culturing an engineered algal strain expressing the target recombinant protein; collecting and washing the algal cells; adding a lysis buffer containing detergent and protease inhibitor; performing cell disruption using an ultrasonic cell disruptor, wherein the disruption process is parameter-adjusted based on an optimized ultrasonic disruption equation set; collecting the supernatant by centrifugation and filtering; determining the imidazole concentration gradient using a shortest path optimization method; passing the supernatant through an affinity chromatography column; eluting using an imidazole concentration gradient; further purification using a molecular sieve chromatography column; collecting and concentrating the target protein; dialysis; and detecting the purity of the target protein.

[0007] Specifically, the step of culturing the engineered algal strain expressing the target recombinant protein involves inoculating the engineered algal strain expressing the target recombinant protein into a liquid culture medium containing selective antibiotics and culturing it at 25 to 30°C for 72 to 96 hours.

[0008] The step of collecting and washing algae specifically involves centrifuging the cultured algal suspension to collect the algae, washing the algae three times with phosphate buffer solution, each time at a centrifugation speed of 4000 to 6000 revolutions per minute for 10 to 15 minutes.

[0009] In the step of adding a lysis buffer containing detergent and protease inhibitor, the lysis buffer contains 0.1% to 1% detergent and 1% protease inhibitor by mass fraction and 1 to 5 mmol / L protease inhibitor.

[0010] The ultrasonic disruption optimization equation set includes optimization equations for disruption parameters, activity parameters, and energy consumption parameters, which are used to optimize ultrasonic power, processing time, protease inhibitor concentration, ionic strength, and ultrasonic power per unit volume.

[0011] The optimization equation for the disruption parameters is used to optimize the ultrasonic power and processing time. The inputs include cell density, cell suspension volume, target cell disruption rate, and number of disrupted cells. The outputs are the optimized ultrasonic power and processing time values.

[0012] The activity parameter optimization equation is used to optimize the protease inhibitor concentration and ionic strength. The inputs include solution temperature, ultrasonic time, target protein activity value, measured protein activity value, and protein denaturation rate. The outputs are the optimized protease inhibitor concentration and ionic strength values.

[0013] The energy consumption parameter optimization equation is used to optimize the ultrasonic power and processing time per unit volume. The inputs include cell suspension volume, cell density, solution temperature, target energy consumption value, and energy consumed value. The output is the optimized ultrasonic power and processing time per unit volume.

[0014] The step of centrifuging to collect the supernatant and filtering specifically involves centrifuging the broken algal suspension at 8000 to 12000 rpm for 30 to 60 minutes, collecting the supernatant, and filtering it through a 0.22-micron filter membrane.

[0015] In the step of determining the imidazole concentration gradient using the shortest path optimization method, the affinity chromatography column is regarded as a multi-node network, and the node status includes imidazole concentration, elution volume, protein concentration, and elution time.

[0016] The step of passing the supernatant through an affinity chromatography column specifically involves passing the supernatant through an affinity chromatography column at a flow rate of 0.5 to 1.0 mL per minute, the column being filled with nickel ion affinity medium; the step of eluting with an imidazole concentration gradient specifically involves eluting with an imidazole concentration gradient of 10 to 500 mmol per liter to collect the target protein fraction.

[0017] The step of further purification using a molecular sieve chromatography column specifically involves passing the target protein component through the molecular sieve chromatography column at a flow rate of 0.8 to 1.2 mL per minute; the step of collecting and concentrating the target protein specifically involves collecting the target protein peak and concentrating it using an ultrafiltration centrifuge tube at a centrifugation speed of 3000 to 4000 rpm.

[0018] Specifically, the dialysis process involves using a dialysis bag to dialyze the concentrated target protein solution at 4°C for 12 to 24 hours, changing the dialysis fluid 2 to 3 times. The step of detecting the purity of the target protein involves using sodium dodecyl sulfate polyacrylamide gel electrophoresis and Western blotting to detect the purity of the target protein, with the purity reaching 90% or higher.

[0019] Compared with existing technologies, this invention provides an affinity chromatography method for purifying recombinant proteins from engineered algal strains. This invention proposes an ultrasonic disruption process based on a multi-parameter optimization equation set. By establishing equations for cell disruption efficiency, protein stability, and energy efficiency, real-time optimization and precise control of various parameters during ultrasonic disruption are achieved. This method employs a feedback control algorithm to dynamically adjust ultrasonic parameters based on real-time monitoring data, effectively solving the problem of difficult protein activity control in traditional techniques. Simultaneously, this invention integrates a shortest path optimization algorithm, realizing intelligent control of the affinity chromatography process and further improving protein purification efficiency.

[0020] This invention achieves precise control of the ultrasonic disruption process for the first time by establishing a complete parameter optimization system. The cell disruption efficiency equation considers multiple key factors such as ultrasonic power, processing time, and cell density to ensure optimal disruption results. The protein stability equation predicts and controls the degree of protein denaturation in real time by monitoring parameters such as solution temperature and protease inhibitor concentration. The energy efficiency equation optimizes energy input, avoiding the impact of excessive disruption on protein activity. This multi-dimensional parameter control strategy not only improves protein yield but also maximizes the preservation of protein bioactivity.

[0021] By implementing the technical solution of this invention, the problem of inactivation of recombinant proteins from engineered algae strains during ultrasonic disruption has been successfully solved. This solution not only establishes a scientific parameter optimization system but also achieves real-time monitoring and intelligent regulation of the disruption process, maintaining an optimal balance between protein extraction efficiency and activity. This multi-parameter optimization-based method provides a reliable technical guarantee for the large-scale preparation of recombinant proteins from engineered algae strains and has significant practical application value. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention.

[0023] Figure 2 This is a graph showing the changes in multiple parameters during the cultivation of the engineered algal strain in Example 2.

[0024] Figure 3 The image shows the elution curves and Gaussian fitting results during the affinity chromatography process in Example 2.

[0025] Figure 4 This is a graph showing the multi-parameter monitoring data of the protein concentration process in Example 2. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0027] like Figure 1 The diagram shows a flowchart of an affinity chromatography method for purifying recombinant proteins from engineered algal strains provided by this invention. This method includes the following steps:

[0028] S01. Inoculate the engineered algal strain expressing the target recombinant protein into a liquid culture medium containing selective antibiotics and culture it at 25 to 30°C for 72 to 96 hours.

[0029] S02. Collect the algal cells by centrifuging the cultured algal suspension. Wash the algal cells three times with phosphate buffer, each time at a speed of 4000 to 6000 rpm for 10 to 15 minutes.

[0030] S03. Add lysis buffer to the collected algae, wherein the lysis buffer contains 0.1 to 1% detergent by mass and 1 to 5 mmol / L protease inhibitor.

[0031] S04. The algal suspension is placed in an ultrasonic cell disruptor for disruption, and the disruption process is based on the ultrasonic disruption optimization equation set for parameter adjustment.

[0032] S05. Centrifuge the broken algal suspension at 8000 to 12000 rpm for 30 to 60 minutes, collect the supernatant, and filter it through a 0.22-micron filter membrane.

[0033] S06. The shortest path optimization method is used to determine the imidazole concentration gradient. In the shortest path optimization method, the affinity chromatography column is regarded as a multi-node network. The node status includes imidazole concentration, elution volume, protein concentration and elution time.

[0034] S07. Based on the results of the shortest path optimization method, the supernatant is passed through an affinity chromatography column at a flow rate of 0.5 to 1.0 mL per minute, the affinity chromatography column being filled with a nickel ion affinity medium;

[0035] S08. Elute the affinity chromatography column using an imidazole concentration gradient of 10 to 500 mmol / L and collect the target protein fraction;

[0036] S09. The target protein component is purified using a molecular sieve chromatography column, wherein the target protein component is passed through the molecular sieve chromatography column at a flow rate of 0.8 to 1.2 mL per minute;

[0037] S10. Collect the target protein peak and concentrate the target protein solution using an ultrafiltration centrifuge tube at a speed of 3000 to 4000 revolutions per minute.

[0038] S11. Dialyze the concentrated target protein solution at 4°C for 12 to 24 hours using a dialysis bag, and change the dialysis fluid 2 to 3 times.

[0039] S12. The purity of the target protein was detected by sodium dodecyl sulfate polyacrylamide gel electrophoresis and Western blotting, and the purity of the target protein reached more than 90%.

[0040] The ultrasonic fragmentation optimization equation set includes fragmentation parameter optimization equations, activity parameter optimization equations, and energy consumption parameter optimization equations.

[0041] The optimization equation for the disruption parameters is used to optimize the ultrasonic power and processing time. The inputs include cell density, cell suspension volume, target cell disruption rate, and number of disrupted cells. The outputs are the optimized ultrasonic power and processing time values.

[0042] The activity parameter optimization equation is used to optimize the protease inhibitor concentration and ionic strength. The inputs include solution temperature, ultrasonic time, target protein activity value, measured protein activity value, and protein denaturation rate. The outputs are the optimized protease inhibitor concentration and ionic strength values.

[0043] The energy consumption parameter optimization equation is used to optimize the ultrasonic power and processing time per unit volume. The inputs include cell suspension volume, cell density, solution temperature, target energy consumption value, and energy consumed value. The output is the optimized ultrasonic power and processing time per unit volume.

[0044] The target cell disruption rate is 90% to 95%.

[0045] The target value for protein activity is 85% to 90%;

[0046] The energy consumption target is 100 to 150 joules per milligram of target protein;

[0047] The number of broken cells was determined by cell counting.

[0048] The measured values ​​of the protein activity were obtained by enzyme activity assay.

[0049] The energy consumed is obtained through the energy metering module of the ultrasonic cell disruptor.

[0050] The protein denaturation rate was obtained by real-time fluorescence detection.

[0051] The specific implementation methods of the above steps are described in detail below.

[0052] The specific implementation of step S01 involves first expanding the engineered algal strain in liquid culture. The culture medium uses a modified culture medium formula specifically for engineered algal strains, in which selective antibiotics are added to maintain plasmid stability. The concentration of the selective antibiotics needs to be determined through preliminary experiments to find the optimal concentration, which is usually 50 to 100 mg / L. The culture is carried out at 27°C on a shaker at 120 rpm for 84 hours. The absorbance of the culture medium is detected using a spectrophotometer. When the absorbance value reaches 2.0 to 2.5, the algal strain is harvested. At this time, the algal strain is in the late logarithmic growth phase, and the recombinant protein expression level reaches its maximum. The relationship between culture time and protein expression level is optimized using a dynamic programming algorithm to determine the optimal harvest time.

[0053] The specific implementation of step S02 is as follows: the cultured algae are collected by continuous flow centrifugation. The centrifugation temperature is controlled at 4℃, the centrifugation speed is 5000 rpm, and the centrifugation time is 12 minutes. The centrifuged algae are resuspended and washed with phosphate buffer. The pH value of the phosphate buffer is 7.4 and the ionic strength is 150 mmol / L. The washing process is repeated 3 times to remove residual impurities in the culture medium. The washing effect of the algae after the last washing is detected by a conductivity meter. When the conductivity drops to less than 5% of the conductivity of the culture medium, it indicates that the washing effect meets the standard. A greedy algorithm is used to optimize the relationship between the number of washings and the washing effect to determine the optimal number of washings.

[0054] The specific implementation of step S03 involves adding pre-cooled lysis buffer to the collected algae. The formulation of the lysis buffer was optimized through orthogonal experimental design and contains 50 mmol / L of tris(hydroxymethyl)aminomethane hydrochloride buffer, 150 mmol / L of sodium chloride, 0.5% detergent, and 2 mmol / L of protease inhibitor. The pH value was adjusted to 8.0, and the concentration of each component was optimized using a simulated annealing algorithm to obtain the best lysis effect. The ratio of lysis buffer to algae was 5:1. The mixture was stirred under ice bath conditions and allowed to stand for 10 minutes to allow the lysis buffer to fully penetrate into the algae.

[0055] The specific implementation of step S04 involves transferring the algal suspension to an ultrasonic cell disruptor equipped with a cooling system. The disruptor is then subjected to disruption based on the optimal parameters calculated using the ultrasonic disruption optimization equation set. The ultrasonic power is set to 400 watts, and an intermittent ultrasonic mode is used, with 5 seconds of ultrasonic activity followed by a 3-second interval. The total processing time is 20 minutes. During the disruption process, a real-time fluorescence detection system is used to monitor the degree of protein denaturation. When the fluorescence intensity change exceeds the threshold, the ultrasonic power is automatically adjusted. A feedback control algorithm is used to optimize the disruption parameters in real time, ensuring that the protein activity is not affected.

[0056] The specific implementation of step S05 involves transferring the broken algal suspension to a high-speed centrifuge and centrifuging it at 10,000 rpm for 45 minutes at 4°C. Insoluble impurities are separated using a layered centrifugation technique. The supernatant is then filtered through a 0.22-micron polyethersulfone membrane. The membrane undergoes a pretreatment process to reduce non-specific protein adsorption. During filtration, membrane flux changes are monitored, and the membrane flux curve is fitted using the least squares method to predict the degree of membrane fouling. The membrane is replaced promptly to ensure filtration efficiency.

[0057] The specific implementation of step S06 involves constructing a state transition network for the affinity chromatography process based on the shortest path optimization method. The network nodes include four key parameters: imidazole concentration, elution volume, protein concentration, and elution time. The Dijkstra algorithm is used to calculate the optimal state transition path and determine the strategy for changing the imidazole concentration gradient. A 6 mL nickel ion affinity packing material is selected for the chromatography column, with a column height of 5 cm. First, the chromatography column is equilibrated with 5 column volumes of binding buffer containing 20 mmol / L of tris(hydroxymethyl)aminomethane hydrochloride buffer and 500 mmol / L of sodium chloride, with a pH of 8.0. The loading amount is determined using a dynamic binding capacity assay, typically 2 to 3 mg of target protein per mL of packing material.

[0058] The specific implementation of step S07 is as follows: the optimal operating parameters calculated by the shortest path optimization method are used to pass the supernatant through the nickel ion affinity chromatography column at a flow rate of 0.8 mL per minute. The sample loading process adopts an adaptive flow rate control system, which adjusts the flow rate in real time by monitoring the pressure at the column inlet. The upper limit of the pressure is set to 0.8 MPa. When the pressure exceeds the threshold, the system automatically reduces the flow rate to protect the chromatography medium. The recurrent neural network algorithm is used to predict the pressure change trend and adjust the flow rate parameters in advance. After the sample loading is completed, the column is washed with 2 column volumes of binding buffer to remove non-specifically bound contaminating proteins.

[0059] The specific implementation of step S08 is to use a linear gradient elution method with an initial concentration of 10 mmol / L imidazole and a final concentration of 500 mmol / L imidazole. The gradient volume is 15 times the column volume. During the elution process, a real-time ultraviolet detection system is used to monitor the protein concentration in the effluent. The system inputs the detection data into the peak shape analysis algorithm, and identifies the target protein peak through Gaussian curve fitting. When the target protein is detected to be eluting, the system automatically reduces the gradient slope, prolongs the elution time of that concentration range, and improves the separation of the target protein. The target protein peak is collected in segments, with 2 ml collected in each collection tube.

[0060] The specific implementation of step S09 involves selecting a molecular sieve column with a molecular weight exclusion range of 10 to 100 kilodaltons, a column volume of 120 mL, and using a mobile phase containing 50 mmol / L phosphate buffer and 150 mmol / L sodium chloride at a pH of 7.4. The column is first equilibrated with 3 times the column volume of mobile phase. The collected target protein fraction is injected at a flow rate of 1.0 mL / min. A multi-wavelength ultraviolet detection system is used to monitor the elution process in real time, while a dynamic light scattering detector is used to monitor the aggregation state of the protein. Principal component analysis is used to process the multidimensional detection data to accurately determine the elution position of the target protein.

[0061] The specific implementation of step S10 is to concentrate the target protein solution using a polyethersulfone ultrafiltration membrane with a molecular weight cutoff of 10 kilodaltons. The concentration process is carried out at 4°C, with a centrifugation speed of 3500 rpm. An intermittent centrifugation strategy is adopted, and the protein concentration is detected every 10 minutes. The final concentrated volume is predicted using a Monte Carlo simulation algorithm. Centrifugation is stopped when the protein concentration reaches 1 mg / mL. At the same time, the turbidity of the protein solution is monitored to ensure that no protein aggregation occurs during the concentration process.

[0062] The specific implementation of step S11 is as follows: a regenerated cellulose dialysis bag with a molecular weight cutoff of 3.5 kDaltons is selected. The concentrated protein solution is transferred into the dialysis bag. The dialysate is phosphate buffer with a pH of 7.4. Dialysis is performed at 4°C for 18 hours, during which the dialysate is changed 3 times with an interval of 6 hours between each change. The dialysis effect is monitored in real time using a conductivity monitoring system. The change in ionic strength is predicted using an exponential decay model. Dialysis is completed when the conductivity drops to 150 μS / cm. After dialysis, the protein concentration is measured and the volume loss rate is recorded.

[0063] The specific implementation of step S12 is as follows: Prepare a 12% sodium dodecyl sulfate polyacrylamide gel, mix an appropriate amount of protein sample with the loading buffer, denature at 95°C for 5 minutes, perform electrophoretic separation at 120 volts, stop electrophoresis when the bromophenol blue indicator migrates to the bottom of the gel, stain with Coomassie brilliant blue, acquire images using a gel imaging system, calculate the gray value of the target band using an image analysis algorithm, convert it into protein content through a standard curve, calculate the purity, and simultaneously perform a Western blot experiment to verify the specificity of the target protein, detect the target band, and quantitatively analyze the band signal intensity using a digital image analysis system.

[0064] This method is particularly suitable for the purification of membrane proteins and their soluble fragments, including CD45, CD3, CD4, CD8, CD16, CD56, CD19, CD55, CD59, CD25, CD127, PD1, CD28, CD64, CD11b, CD35, Perforin, and Granzyme. Optimized parameter control strategies ensure that these proteins maintain good conformation and activity during purification. Applicable algae include one or more cell lines from *Chlamydomonas reinhardtii*, *Chlorella* sp., *Dunaliella salina*, *Prorocentrum minimum*, *Alexandrium* sp., *Platymonas* sp., *Scenedesmus* sp., *Euglenasp.*, *Porphyridium* sp., and *Nephroselmis* sp., with *Chlamydomonas reinhardtii* cell lines being preferred. Through long-term laboratory domestication and genetic modification, the above-mentioned algal cell lines have established stable ribosome entry site sequences, chloroplast targeting sequences, and endoplasmic reticulum localization sequences, enabling efficient expression of the aforementioned human cell surface marker antibodies.

[0065] The equations or calculation processes involved in this invention will be described in detail below.

[0066] 1. The specific expression of the crushing parameter optimization equation is as follows:

[0067]

[0068] In the formula, P o The optimized ultrasonic power value is expressed in watts (T). o This is the optimized processing time value, in seconds; ρ c V represents cell density, expressed in cells per milliliter. c R represents the volume of the cell suspension, in milliliters. t The target value for cell disruption rate is 90% to 95%; R max The theoretical maximum breakage rate is taken as 100%; N b The number of broken cells is expressed in cells per milliliter; N total The total number of cells is expressed as cells per milliliter; k1, k2, k3, k4, and k5 are the disruption power coefficients; α1, α2, α3, and α4 are the processing time coefficients; ε1 and ε2 are error terms, ranging from 0.01 to 0.05.

[0069] This equation introduces ln(V) cThe first term reflects the non-linear increase in power demand due to increased volume; adding... and The item describes the dynamic changes in the crushing process; using The term reflects the decrease in fragmentation efficiency as the number of fragmented cells decreases; an exponential term is used. Describe the asymptotic characteristics as the breakage rate approaches its maximum;

[0070] 2. The specific equation for optimizing the activity parameters is expressed as follows:

[0071]

[0072] In the formula, C i The optimized protease inhibitor concentration is expressed in millimoles per liter; I s The optimized ionic strength is expressed in millimoles per liter; T s t represents the solution temperature in degrees Celsius. s Ultrasound time, in seconds; A t The target value for protein activity is 85% to 90%; A m This represents the measured protein activity, expressed as a percentage; v d This refers to the protein denaturation rate, expressed as a percentage per second; v max The maximum denaturation rate is expressed as a percentage per second; β1, β2, β3, β4, and β5 are inhibitor concentration coefficients; γ1, γ2, γ3, γ4, and γ5 are ionic strength coefficients; ε3 and ε4 are error terms, ranging from 0.02 to 0.06.

[0073] The equation: Introducing T s ln(t s The first item reflects the coupling effect of temperature and time; adding... and Describe the dynamic characteristics of activity changes; use and The item reflects the exponential effect of denaturation rate on inhibitor demand; using The item reflects the effect of activity differences on parameter adjustment;

[0074] 3. The specific expression of the energy consumption parameter optimization equation is as follows:

[0075]

[0076] In the formula, P v The optimized ultrasonic power per unit volume is expressed in watts per milliliter; T v E represents the optimized processing time per unit volume, expressed in seconds per milliliter; t The target energy expenditure value ranges from 100 to 150 joules per milligram of target protein; E cT represents the amount of energy consumed, measured in joules. max The maximum permissible temperature, in degrees Celsius; T ref The reference temperature is in degrees Celsius; δ1, δ2, δ3, δ4, δ5 are unit power coefficients; λ1, λ2, λ3, λ4, λ5 are unit time coefficients; ε5, ε6 are error terms, ranging from 0.03 to 0.07.

[0077] The equation: introduces ln(ρ) c ) and ln(V c The term describes the logarithmic relationship between density and volume and energy consumption; (Adding...) and Reflecting the dynamic characteristics of energy consumption changes; using and The term reflects the exponential effect of temperature on energy consumption; an integral term is used. Describe the cumulative energy consumption effect;

[0078] 4. State transition network representation of the shortest path optimization method:

[0079] The optimization objective function of the state transition network is specifically expressed as follows:

[0080]

[0081] In the formula, d ij x is the cost of the state transition from node i to node j; ij is a 0-1 decision variable, representing whether to choose the path from node i to node j; n is the total number of state nodes;

[0082] The calculation of the state transition cost is specifically represented as follows:

[0083] d ij =w1|C i -C j |+w2|V i -V j |+w3|P i -P j |+w4|T i -T j |;

[0084] In the formula, C i C j V represents the imidazole concentration, expressed in millimoles per liter. i V j This is the elution volume, in milliliters; P i P j This is the protein concentration value, in milligrams per milliliter; T i T jThe values ​​represent elution time in minutes; w1, w2, w3, and w4 are weighting coefficients.

[0085] The dynamic programming optimization equation is specifically expressed as follows:

[0086] E(t)=max{E(t-1)+p(t),0};

[0087] p(t)=μ1C(t)+μ2O(t)-μ3D(t)+ε7;

[0088] In the formula, E(t) is the protein expression level at time t, in milligrams per liter; p(t) is the protein yield at time t; C(t) is the cell density at time t; O(t) is the dissolved oxygen level at time t; D(t) is the cell death rate at time t; μ1, μ2, and μ3 are weighting coefficients; ε7 is the error term, ranging from 0.01 to 0.03.

[0089] The optimization of the cleaning effect by the greedy algorithm is specifically represented as follows:

[0090]

[0091] In the formula, R(n) is the cleaning effect score after n cleaning cycles; η i κ is the efficiency coefficient for the i-th cleaning cycle; i C is the attenuation coefficient; r (i) represents the concentration of residue after the i-th cleaning;

[0092] The membrane flux fitting curve is shown below:

[0093]

[0094] In the formula, J(t) is the membrane flux at time t, in milliliters per square centimeter per minute; J0 is the initial membrane flux; k f ε is the membrane fouling coefficient; ε8 is the error term, ranging from 0.02 to 0.05;

[0095] The Gaussian fitting of the target protein peak is specifically represented as follows:

[0096]

[0097] In the formula, A(v) is the absorbance value at the elution volume v; A0 is the peak absorbance; v0 is the elution volume corresponding to the peak; σ is the peak width coefficient; ε9 is the error term, ranging from 0.01 to 0.04;

[0098] Methods for obtaining each parameter: C(t) was measured by spectrophotometer using OD. 600 Values ​​were obtained; O(t) was obtained through real-time monitoring using a dissolved oxygen electrode; D(t) was obtained through flow cytometry; C r(i) Obtained by conductivity meter measurement; J(t) obtained by real-time recording by flow meter; A(v) obtained by real-time monitoring by ultraviolet detector; ρ c Obtained by counting with a hemocytometer; N b Obtained by counting under a microscope; A m The enzyme activity was determined by fluorescence assay; v d E was obtained by real-time fluorescence spectroscopy. c The energy measurement module of the ultrasonic cell disruptor was used to obtain the coefficients; each coefficient was obtained by fitting the experimental data using the least squares method.

[0099] Specifically, the principle of this invention is based on the interaction mechanism between cavitation and protein stability in an ultrasonic disruption system. Precise control of the disruption process is achieved by establishing a set of parameter optimization equations. When ultrasound propagates in a liquid, it generates periodic compression and expansion. When the negative pressure is sufficiently high, tiny bubbles form in the liquid. These bubbles continuously expand and contract in the sound field, eventually collapsing and generating localized high temperatures, high pressures, and strong shear forces. This cavitation effect is the main mechanism of cell disruption.

[0100] The core principle of the ultrasonic disruption optimization equations is to establish a mathematical model relating cell disruption efficiency, protein stability, and energy efficiency through a mapping relationship in a multidimensional parameter space. The disruption parameter optimization equations are based on the relationship between the intensity of the cavitation effect and the cell disruption rate. By monitoring the number of disrupted cells and combining parameters such as cell density and suspension volume, the optimal ultrasonic power and processing time are calculated. Excessive ultrasonic power leads to excessively high local temperatures generated when cavitation bubbles collapse, causing protein denaturation; while insufficient power fails to effectively disrupt cells. Through the optimized calculations of these equations, disruption efficiency can be ensured while minimizing protein damage.

[0101] The activity parameter optimization equation is based on the thermodynamic principles of protein conformational stability, taking into account key factors affecting protein stability such as temperature and ionic strength. Protein stability in solution is mainly influenced by various intermolecular forces, including electrostatic interactions, hydrogen bonds, and hydrophobic interactions, all of which are closely related to the solution environment. By real-time monitoring of solution temperature and protein activity, combined with kinetic data on protein denaturation rates, this equation can predict and control the degree of protein denaturation. Furthermore, by optimizing the concentration of protease inhibitors and ionic strength, a protective solution environment is established, further improving protein stability.

[0102] The energy consumption parameter optimization equation is based on the principles of heat transfer and energy conversion efficiency. By monitoring the relationship between energy input and protein yield, it optimizes the distribution of ultrasonic power per unit volume. This equation considers the heat capacity and thermal conductivity of the cell suspension, and by controlling the local temperature gradient, it avoids protein denaturation caused by heat accumulation. By establishing a quantitative relationship between energy input and disruption effect, energy utilization efficiency is maximized.

[0103] These three optimization equations interact through a feedback control system, forming a closed-loop parameter regulation network. When the system detects a deviation in a parameter, it immediately triggers a corresponding compensation mechanism to ensure the entire fragmentation process remains in an optimal state. For example, when an increase in temperature is detected, the system automatically reduces the ultrasonic power or increases the interval time, while simultaneously increasing the concentration of protease inhibitors to enhance protein protection. This multi-level regulation mechanism ensures the stability and controllability of the fragmentation process.

[0104] In affinity chromatography, a shortest path optimization method was employed to further improve the efficiency of separation and purification. This method treats the chromatography process as a multi-node network, where each node represents a different operational state. By calculating the optimal state transition path, precise control of elution conditions was achieved. This optimization strategy not only improved the purity of the target protein but also reduced non-specific binding, thereby enhancing the quality of the final product.

[0105] In summary, this invention achieves precise control of the ultrasonic disruption process by establishing a multi-parameter optimization equation set, while simultaneously improving purification efficiency through shortest path optimization. This method, based on mathematical models and control theory, provides reliable technical support for the preparation of recombinant proteins from engineered algae strains, demonstrating the organic combination of theoretical foundation and practical application in this invention.

[0106] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0107] The specific implementation of step S01 is to maximize protein expression through an engineered algal strain culture optimization system. First, the engineered algal strain is inoculated into a liquid culture medium containing selective antibiotics. The culture medium uses a modified formula specifically for the engineered algal strain, where the concentration of the selective antibiotics, determined through preliminary experiments, is 50 to 100 mg / L. This concentration range effectively maintains plasmid stability and prevents contamination by other microorganisms. Culture conditions are controlled at 27°C using a shaker at 120 rpm, and the absorbance of the culture medium is monitored in real time using a spectrophotometer. When the absorbance value reaches 2.0 to 2.5, it indicates that the algal strain is in the late logarithmic growth phase, at which point the recombinant protein expression level reaches its maximum. To accurately determine the optimal harvest time, a dynamic programming algorithm is used to optimize the relationship between culture time and protein expression level. The optimization equation is:

[0108] E(t)=max{E(t-1)+p(t),0};

[0109] p(t)=μ1C(t)+μ2O(t)-μ3D(t)+ε7.

[0110] Where E(t) represents the protein expression level at time t, in milligrams per liter; p(t) is the protein yield at time t; C(t) is the cell density at time t; O(t) is the dissolved oxygen level at time t; D(t) is the cell death rate at time t; μ1, μ2, and μ3 are weighting coefficients, reflecting the effects of cell density, dissolved oxygen level, and cell death rate on yield, respectively; ε7 is the error term, ranging from 0.01 to 0.03. This optimization equation determines the optimal harvest time by recursively calculating the protein expression level at different time points and comprehensively considering cell growth status and environmental factors.

[0111] The specific implementation of step S02 involves collecting and optimizing the washed algae using continuous flow centrifugation. The centrifugation process is conducted at a low temperature of 4°C, with a centrifugation speed controlled at 5000 rpm for 12 minutes. The collected algae are resuspended and washed using phosphate buffer, with the pH adjusted to 7.4 and an ionic strength of 150 mmol / L. A greedy algorithm is used to optimize the relationship between the number of washes and the washing effect during the washing process; the optimization equation is as follows:

[0112]

[0113] Where R(n) represents the cleaning effect score after n cleaning cycles; η i κ is the efficiency coefficient for the i-th cleaning cycle; i C is the attenuation coefficient; r(i) represents the residue concentration after the i-th wash. The washing effect is monitored in real time using a conductivity meter. When the conductivity drops below 5% of the culture medium conductivity, the washing effect is considered satisfactory. This algorithm determines the optimal number of washes by calculating the marginal benefit of each wash, thus avoiding protein loss due to over-washing.

[0114] The specific implementation of step S03 involves optimizing the lysis buffer composition and controlling cell lysis. The lysis buffer formulation was optimized using orthogonal experimental design, comprising 50 mmol / L tris(hydroxymethyl)aminomethane hydrochloride buffer, 150 mmol / L sodium chloride, 0.5% detergent, and 2 mmol / L protease inhibitor, with the pH adjusted to 8.0. Simulated annealing was used to optimize the concentration of each component to achieve the best lysis effect. The ratio of lysis buffer to algae was 5:1. After mixing under ice bath conditions, the mixture was allowed to stand for 10 minutes to allow the lysis buffer to fully penetrate the algae. This step, through precise control of lysis conditions, ensures the integrity and activity of the target protein.

[0115] The specific implementation of step S04 involves using an ultrasonic disruption system to achieve precise cell disruption. The algal suspension is transferred to an ultrasonic cell disruptor equipped with a cooling system, and the optimal parameters are calculated based on the ultrasonic disruption optimization equations for disruption processing. The disruption parameter optimization equations are as follows:

[0116]

[0117] Among them, P o The optimized ultrasonic power value is expressed in watts (T). o This is the optimized processing time value, in seconds; ρ c V represents cell density, expressed in cells per milliliter. c R represents the volume of the cell suspension, in milliliters. t The target value for cell disruption rate is 90% to 95%; R max The theoretical maximum breakage rate is taken as 100%; N b The number of broken cells is expressed in cells per milliliter; N total The total number of cells is expressed as cells per milliliter; k1, k2, k3, k4, and k5 are the disruption power coefficients; α1, α2, α3, and α4 are the processing time coefficients; and ε1 and ε2 are error terms, ranging from 0.01 to 0.05.

[0118] Meanwhile, the optimization equation for the activity parameter is:

[0119]

[0120] Among them, C i The optimized protease inhibitor concentration is expressed in millimoles per liter; I sThe optimized ionic strength is expressed in millimoles per liter; T s t represents the solution temperature in degrees Celsius. s Ultrasound time, in seconds; A t The target value for protein activity is 85% to 90%; A m This represents the measured protein activity, expressed as a percentage; v d This refers to the protein denaturation rate, expressed as a percentage per second; v max The maximum denaturation rate is expressed as a percentage per second; β1, β2, β3, β4, and β5 are inhibitor concentration coefficients; γ1, γ2, γ3, γ4, and γ5 are ionic strength coefficients; ε3 and ε4 are error terms, ranging from 0.02 to 0.06.

[0121] Finally, the energy consumption parameter optimization equation is:

[0122]

[0123] Among them, P v The optimized ultrasonic power per unit volume is expressed in watts per milliliter; T v E represents the optimized processing time per unit volume, expressed in seconds per milliliter; t The target energy expenditure value ranges from 100 to 150 joules per milligram of target protein; E c T represents the amount of energy consumed, measured in joules. max The maximum permissible temperature, in degrees Celsius; T ref The reference temperature is in degrees Celsius; δ1, δ2, δ3, δ4, and δ5 are unit power coefficients; λ1, λ2, λ3, λ4, and λ5 are unit time coefficients; ε5 and ε6 are error terms, ranging from 0.03 to 0.07. Based on the parameters calculated from the optimization equation, the ultrasonic power was set to 400 watts, using an intermittent ultrasonic mode with 5 seconds of ultrasonication followed by a 3-second interval, for a total processing time of 20 minutes. Throughout the disruption process, a real-time fluorescence detection system was used to monitor the degree of protein denaturation. When the detected fluorescence intensity change exceeded a preset threshold, the system automatically adjusted the ultrasonic power. A feedback control algorithm was used to optimize the disruption parameters in real time, ensuring that the protein activity remained unaffected.

[0124] The specific implementation of step S05 involves using layered centrifugation to separate insoluble impurities and optimizing the filtration process. The broken algal suspension is transferred to a high-speed centrifuge and centrifuged at 10,000 rpm for 45 minutes at 4°C. The supernatant is filtered through a 0.22-micron polyethersulfone membrane, which undergoes a pretreatment process to reduce non-specific protein adsorption. During filtration, a real-time monitoring system records membrane flux changes, and the least squares method is used to fit the membrane flux curve; the fitting equation is as follows:

[0125]

[0126] Where J(t) is the membrane flux at time t, in milliliters per square centimeter per minute; J0 is the initial membrane flux; k f ε is the membrane fouling coefficient; ε8 is the error term, ranging from 0.02 to 0.05. This is achieved by monitoring k... f The value changes trend, and when it exceeds a preset threshold of 0.1, it indicates a high degree of membrane fouling, requiring membrane replacement. This equation can effectively predict the degree of membrane fouling and allow for timely membrane replacement to ensure filtration efficiency.

[0127] The specific implementation of step S06 involves constructing a state transition network for the affinity chromatography process using a shortest path optimization method. The network nodes include four key parameters: imidazole concentration, elution volume, protein concentration, and elution time. The optimization objective function is:

[0128]

[0129] x ij ∈{0,1},i,j=1,2,…,n.

[0130] Where, d ij x is the cost of the state transition from node i to node j; ij Let be a 0-1 decision variable, representing whether to choose the path from node i to node j; n is the total number of state nodes. The equation for calculating the state transition cost is:

[0131] d ij =w1|C i -C j |+w2|V i -V j |+w3|P i -P j |+w4|T i -T j |

[0132] Among them, C i C j V represents the imidazole concentration, expressed in millimoles per liter. i V j This is the elution volume, in milliliters; P i P j This is the protein concentration value, in milligrams per milliliter; T i T jThe values ​​represent elution time in minutes; w1, w2, w3, and w4 are weighting coefficients. The optimal state transition path was calculated using the Dijkstra algorithm to determine the strategy for varying the imidazole concentration gradient. A 6 mL nickel-affinity packing column with a bed height of 5 cm was used. The column was first equilibrated with 5 column volumes of binding buffer containing 20 mmol / L tris(hydroxymethyl)aminomethane hydrochloride buffer and 500 mmol / L sodium chloride, at pH 8.0. The loading amount was determined using a dynamic binding capacity assay, typically 2 to 3 mg of target protein per mL of packing material.

[0133] The specific implementation of step S07 is to achieve precise control of affinity chromatography. Based on the optimal operating parameters calculated using the shortest path optimization method, the supernatant is passed through a nickel ion affinity chromatography column at a flow rate of 0.8 mL / min. An adaptive flow rate control system is used during sample loading, adjusting the flow rate in real time by monitoring the pre-column pressure, with the upper pressure limit set at 0.8 MPa. When the pressure exceeds the threshold, the system automatically reduces the flow rate to protect the chromatography medium. A recurrent neural network algorithm is used to predict pressure change trends and adjust the flow rate parameters in advance. The input layer of the recurrent neural network includes the current pressure value, flow rate value, and time series data; the hidden layer uses long short-term memory units; and the output layer predicts the pressure value at the next moment. After sample loading, the column is washed with 2 column volumes of binding buffer to remove non-specifically bound proteins.

[0134] The specific implementation of step S08 involves optimizing the protein elution process. A linear gradient elution method is used, with an initial concentration of 10 mmol / L imidazole and a final concentration of 500 mmol / L imidazole, and a gradient volume of 15 column volumes. During elution, a real-time UV detection system monitors the protein concentration in the effluent. The system inputs the detection data into a peak shape analysis algorithm, which identifies the target protein peak through Gaussian curve fitting. The fitting equation is as follows:

[0135]

[0136] Where A(v) is the absorbance value at elution volume v; A0 is the peak absorbance; v0 is the elution volume corresponding to the peak; σ is the peak width coefficient; and ε9 is the error term, ranging from 0.01 to 0.04. When the target protein is detected to begin elution, the system automatically reduces the gradient slope to 50%, extending the elution time in that concentration range and improving the separation of the target protein. The target protein peak is collected in segments, with 2 mL collected in each collection tube. Peak shape parameters are calculated in real time; a peak symmetry coefficient greater than 0.9 and a theoretical plate number exceeding 1000 indicate good separation performance.

[0137] The specific implementation of step S09 involves further purification of the target protein using molecular sieve chromatography. A molecular sieve column with a molecular weight exclusion range of 10 to 100 kilodaltons and a column volume of 120 mL is selected. A mobile phase containing 50 mmol / L phosphate buffer and 150 mmol / L sodium chloride is used, and the pH is adjusted to 7.4. The column is first equilibrated with three column volumes of mobile phase, and the collected target protein fraction is injected at a flow rate of 1.0 mL / min. A multi-wavelength ultraviolet detection system is used to monitor the elution process in real time. The system simultaneously acquires data at two wavelengths, 280 nm and 254 nm, and the protein purity is monitored by calculating the ratio of these two wavelengths. A dynamic light scattering detector is used to monitor the protein aggregation state. When the detected particle size distribution width exceeds a preset threshold of 10 nm, it indicates the presence of protein aggregation. Principal component analysis (PCA) is used to process the multidimensional detection data, perform dimensionality reduction on the detection signal, extract the main feature vectors, and establish a mathematical model.

[0138] X = TP T +E.

[0139] Where X is the original data matrix, containing UV absorption and light scattering data; T is the score matrix; P is the loading matrix; and E is the residual matrix. By analyzing the distribution characteristics of the score matrix T, the elution position of the target protein is determined. When the score of the first principal component is greater than 0.8, it is identified as the target protein peak.

[0140] The specific implementation of step S10 involves optimizing the protein concentration process. A polyethersulfone ultrafiltration membrane with a molecular weight cutoff of 10 kilodaltons is used to concentrate the target protein solution. The concentration process is carried out at 4°C and a centrifugation speed of 3500 rpm. An intermittent centrifugation strategy is employed, with protein concentration measured every 10 minutes. The final concentrated volume is predicted using a Monte Carlo simulation algorithm; the prediction model is as follows:

[0141]

[0142] Among them, W f V represents the predicted final volume. i ξ is the initial volume; k is the concentration rate constant; t is the concentration time; i ΔV is a random disturbance factor; i This represents the volume change increment. Centrifugation was stopped when the protein concentration reached 1 mg / mL. Simultaneously, the turbidity of the protein solution was monitored using the 90° scattering method. When the turbidity value exceeded 0.1 scattering units, it indicated the beginning of protein aggregation, requiring a reduction in centrifugation speed.

[0143] The specific implementation of step S11 is to achieve precise control of protein dialysis. A regenerated cellulose dialysis bag with a molecular weight cutoff of 3.5 kilodaltons is selected, and the concentrated protein solution is transferred into the dialysis bag. The dialysate is phosphate buffer with a pH of 7.4. Dialysis is performed at 4°C for 18 hours, with the dialysate changed three times at 6-hour intervals. A conductivity monitoring system is used to monitor the dialysis effect in real time, and an exponential decay model is used to predict changes in ionic strength.

[0144] C(t) = C0·e -λt +C ∞ .

[0145] Where C(t) is the ion intensity at time t; C0 is the initial ion intensity; λ is the decay constant; C ∞ The equilibrium ionic strength was used. Dialysis was completed when the conductivity dropped to 150 μS / cm. After dialysis, the protein concentration was measured and the volume loss rate was recorded, which was controlled to be within 10%. The monodispersity of the protein was detected using dynamic light scattering; a polydispersity index of less than 0.1 indicated good homogeneity of the protein solution.

[0146] Step S12 involves protein purity and specificity analysis. A 12% sodium dodecyl sulfate polyacrylamide gel is prepared. An appropriate amount of protein sample is mixed with loading buffer and denatured at 95°C for 5 minutes. Electrophoresis is performed at 120 volts, stopping when the bromophenol blue indicator migrates to the gel bottom. Coomassie brilliant blue staining is used, and images are acquired using a gel imaging system. The grayscale value of the target band is calculated using an image analysis algorithm, and its quantitative model is as follows:

[0147]

[0148] Where P is the purity of the target protein; I(x) is the gray value distribution function; x1 and x2 are the integral ranges of the target bands; x0 and x... n The integral range of all bands is used. Protein content is converted using a standard curve to calculate purity. A purity of 90% or higher indicates successful purification. Simultaneously, Western blotting experiments are performed to verify the specificity of the target protein. Chemiluminescence is used to detect the target bands, and the relationship between chemiluminescence signal intensity and protein amount conforms to the following equation:

[0149] S = a·ln(Q) + b.

[0150] Where S represents the chemiluminescence signal intensity; Q represents the target protein amount; and a and b are the standard curve coefficients. The band signal intensity was quantitatively analyzed using a digital image analysis system. A coefficient of variation of less than 5% indicates reliable detection results. Standard proteins were used as controls throughout the analysis process to ensure the accuracy and reproducibility of the results.

[0151] By implementing steps S09 to S12 above, high-purity, highly active target proteins can be obtained. Each step employs precise mathematical models and optimized algorithms to ensure the controllability of the purification process and the reliability of the results. This method is particularly suitable for purifying membrane proteins and their soluble fragments, such as CD45, CD3, CD4, CD8, CD16, CD56, CD19, CD55, CD59, CD25, CD127, PD1, CD28, CD64, CD11b, CD35, Perforin, and Granzyme, effectively maintaining the conformation and activity of these proteins during purification.

[0152] To better understand and implement this invention, Example 2, a specific application scenario, is provided below: Researchers, in the process of developing recombinant CD3, need to obtain high-purity recombinant CD3 protein. Using the method of this invention, the researchers successfully achieved efficient purification of CD3 protein from engineered algal strains. The specific implementation process is as follows.

[0153] First, the engineered algal strain was cultured. At 27℃, the engineered algal strain expressing CD3 protein was inoculated into a liquid culture medium containing kanamycin at a concentration of 75 mg / L. The culture was carried out using a shaker at 120 rpm, and the absorbance of the culture medium at a wavelength of 600 nm was measured every 12 hours using a spectrophotometer. The absorbance changes during the culture process are shown in Table 1.

[0154] Table 1. Changes in absorbance during the culture process.

[0155] Incubation time (hours) absorbance value <![CDATA[Cell density (×10 6 cells per milliliter)]]> Protein expression level (mg / L) 0 0.2 1.2 0 12 0.5 3.5 12 24 0.8 5.8 25 36 1.2 8.9 45 48 1.6 12.4 78 60 1.9 15.2 125 72 2.2 18.6 168 84 2.3 19.2 172

[0156] Figure 2 This figure displays multi-parameter changes during the culture of the engineered algal strain. The graph includes three parameters: OD600 value (blue curve), cell density (green curve), and protein expression level (red curve). The x-axis represents culture time (h), the left y-axis represents OD600 value, and the first right y-axis represents cell density (×10⁻¹⁰). 6 The first axis represents protein expression (mg / L), and the second axis represents protein expression (mg / L). The curves clearly show the trends of the three parameters with culture time, reaching their optimal values ​​at 84 hours. Based on the dynamic programming optimization equation:

[0157] E(t) = max{E(t-1) + p(t), 0};

[0158] p(t)=μ1C(t)+μ2O(t)-μ3D(t)+ε7.

[0159] The optimal harvest time was calculated to be 84 hours, at which point the protein expression level reached its maximum of 172 mg / L.

[0160] After collecting the algae, washing and optimization were performed. The algae were collected by centrifugation at 5000 rpm for 12 minutes at 4°C and washed with phosphate buffer solution at pH 7.4. The changes in conductivity during the washing process are shown in Table 2.

[0161] Table 2. Changes in conductivity during the cleaning process.

[0162] Number of cleaning times Electrical conductivity (millisieverts per centimeter) Cleaning efficiency Protein loss rate (%) 0 15.6 0 0 1 8.2 0.47 2.1 2 3.5 0.78 3.8 3 0.8 0.95 5.2 4 0.6 0.96 7.5

[0163] Based on the greedy algorithm to optimize the equation:

[0164]

[0165] The optimal number of washes was determined to be 3, at which point the washing efficiency reached 0.95 and the protein loss rate could be controlled within 5.2%.

[0166] The cell disruption process uses an ultrasonic disruption system. The disruption parameters calculated based on the ultrasonic disruption optimization equations are shown in Table 3.

[0167] Table 3 Optimization parameters for ultrasonic fragmentation

[0168] Parameter type initial value Optimization value Range of variation Ultrasonic power (watts) 500 400 350-450 Processing time (seconds) 1500 1200 1000-1400 Inhibitor concentration (millimoles per liter) 1.0 2.0 1.5-2.5 Ionic strength (millimoles per liter) 100 150 130-170

[0169] A membrane flux monitoring system was used to record flux changes in real time during the filtration process, and the membrane flux curve equation was obtained by fitting using the least squares method.

[0170] J(t) = 2.5·e -0.08·t +0.03.

[0171] When the membrane fouling coefficient k f Replace the filter membrane when the concentration exceeds 0.1, and replace the filter membrane a total of 3 times to finally obtain a clear protein solution.

[0172] The state transition network for affinity chromatography was constructed using the shortest path optimization method, and the optimized elution conditions are shown in Table 4.

[0173] Table 4 Optimization parameters for affinity chromatography

[0174]

[0175] Figure 3The elution curves and Gaussian fitting results during affinity chromatography are shown. The solid blue line represents the protein elution curve (A280 value), and the dashed red line represents the imidazole concentration gradient. The x-axis represents the elution volume (CV), the left y-axis represents the protein absorbance (A280), and the right y-axis represents the imidazole concentration (mmol·L⁻¹). The curves show that the target protein mainly elutes at CVs 8-9, corresponding to an imidazole concentration range of 200-300 mmol·L⁻¹. The target protein mainly elutes in elution stages 3 and 4. Peak shape analysis was performed using a Gaussian fitting equation.

[0176]

[0177] After further purification by molecular sieve chromatography, the protein was concentrated using an ultrafiltration concentration system. The monitoring data of the concentration process are shown in Table 5.

[0178] Table 5 Monitoring data of protein concentration process

[0179] Time (minutes) Volume (ml) Protein concentration (mg / mL) Turbidity value 0 30 0.2 0.02 10 25 0.24 0.02 20 20 0.3 0.03 30 15 0.4 0.03 40 10 0.6 0.04 50 6 1.0 0.05

[0180] Figure 4 The graph displays multi-parameter monitoring data of the protein concentration process. It includes three parameters: solution volume (blue curve), protein concentration (red curve), and turbidity (green curve). The x-axis represents concentration time (min), and the three y-axes represent solution volume (mL), protein concentration (mg·mL⁻¹), and turbidity, respectively. The curves show that as the concentration time increases, the solution volume gradually decreases, the protein concentration gradually increases, while the turbidity only slightly increases. The purification effect was finally verified by SDS-PAGE and Western blot analysis, and the quantitative analysis results are shown in Table 6.

[0181] Table 6. Results of protein purity analysis

[0182] Analytical methods Main grayscale value Total grayscale value Calculate purity (%) Coomassie brilliant blue staining 15682 16876 92.8 Protein blot 12458 13124 94.6

[0183] It should be noted that the variables involved in this invention are explained in detail in Table 7 below.

[0184]

[0185]

[0186] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An affinity chromatography method for purifying recombinant proteins from engineered algal strains, characterized in that, include: The engineered algal strain expressing the target recombinant protein was cultured; The algae were collected and washed; a lysis buffer containing detergent and protease inhibitor was added; the cells were disrupted using an ultrasonic cell disruptor, with parameters adjusted based on an optimized ultrasonic disruption equation set; the supernatant was collected by centrifugation and filtered; an imidazole concentration gradient was determined using a shortest path optimization method; the supernatant was passed through an affinity chromatography column; elution was performed using an imidazole concentration gradient; and further purification was carried out using a molecular sieve chromatography column. Collect and concentrate the target protein; perform dialysis; and determine the purity of the target protein.

2. The method according to claim 1, characterized in that, The step of culturing the engineered algal strain expressing the target recombinant protein specifically involves inoculating the engineered algal strain expressing the target recombinant protein into a liquid culture medium containing selective antibiotics and culturing it at 25 to 30°C for 72 to 96 hours.

3. The method according to claim 2, characterized in that, The steps of collecting and washing algae specifically involve centrifuging the cultured algal suspension to collect the algae, washing the algae three times with phosphate buffer solution, each time at a centrifugation speed of 4000 to 6000 revolutions per minute for 10 to 15 minutes.

4. The method according to claim 3, characterized in that, In the step of adding a lysis buffer containing detergent and protease inhibitor, the lysis buffer contains 0.1% to 1% detergent and 1% protease inhibitor at a concentration of 1 to 5 mmol / L.

5. The method according to claim 4, characterized in that, The ultrasonic disruption optimization equation set includes optimization equations for disruption parameters, activity parameters, and energy consumption parameters, which are used to optimize ultrasonic power, processing time, protease inhibitor concentration, ionic strength, and ultrasonic power per unit volume.

6. The method according to claim 5, characterized in that, The optimization equation for the disruption parameters is used to optimize the ultrasonic power and processing time. The inputs include cell density, cell suspension volume, target cell disruption rate, and number of disrupted cells. The outputs are the optimized ultrasonic power and processing time values.

7. The method according to claim 6, characterized in that, The activity parameter optimization equation is used to optimize the protease inhibitor concentration and ionic strength. The inputs include solution temperature, ultrasonic time, target protein activity value, measured protein activity value, and protein denaturation rate. The outputs are the optimized protease inhibitor concentration and ionic strength values.

8. The method according to claim 7, characterized in that, The energy consumption parameter optimization equation is used to optimize the ultrasonic power and processing time per unit volume. The inputs include cell suspension volume, cell density, solution temperature, target energy consumption value, and energy consumed. The output is the optimized ultrasonic power and processing time per unit volume.

9. The method according to claim 8, characterized in that, The step of centrifuging to collect the supernatant and filtering specifically involves centrifuging the broken algal suspension at 8000 to 12000 rpm for 30 to 60 minutes, collecting the supernatant, and filtering it through a 0.22-micron filter membrane.

10. The method according to claim 9, characterized in that, In the step of determining the imidazole concentration gradient using the shortest path optimization method, the affinity chromatography column is regarded as a multi-node network, and the node status includes imidazole concentration, elution volume, protein concentration, and elution time.

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