An enzyme separation method based on enzyme catalytic activity
By using an enzyme-based catalytic activity method, and employing a microfluidic platform and mathematical models to simulate differences in enzyme migration rates, efficient separation of active and inactive enzymes was achieved. This solves the problem of limited enzyme separation efficiency and selectivity in traditional methods and provides a self-driven separation approach for enzyme molecules.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING UNIV OF SCI & TECH
- Filing Date
- 2024-01-17
- Publication Date
- 2026-06-12
AI Technical Summary
Existing active biomolecule separation technologies are difficult to efficiently separate the active and inactive forms of enzymes, and traditional methods may damage enzyme molecules, with limited separation efficiency and selectivity.
This method, based on enzyme catalytic activity, describes the diffusion and cross-diffusion flows of enzymes in the presence of substrates. It utilizes a microfluidic platform and mathematical models to simulate differences in enzyme mobility, achieving self-driven enzyme separation and avoiding external field-driven separation. Microchannels are used for enzyme separation.
This method achieves efficient separation of active and inactive enzymes, avoids enzyme damage, and provides a basis for further separation of weakly active enzymes, leading to a deeper understanding of the self-driven behavior of enzyme molecules.
Smart Images
Figure CN117995295B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of biochemical analysis technology, and particularly relates to an enzyme separation method based on enzyme catalytic activity. Background Technology
[0002] Active biomolecule separation technology is a widely used technique in biotechnology, pharmaceutical industry, and environmental science. It is primarily used to separate functionally active biomolecules, such as proteins, nucleic acids, and polysaccharides, from complex biological samples. These active biomolecules play important physiological roles in organisms, such as catalyzing biochemical reactions, transmitting genetic information, and maintaining cell structure. Therefore, the development of active biomolecule separation technology is of great significance for biological research, drug development, and environmental protection.
[0003] Bioactive biomolecules possess specific structures and properties; for example, proteins are composed of amino acids, nucleic acids are composed of nucleotides, and polysaccharides are composed of monosaccharides. These biomolecules exist in low concentrations within organisms and interact with other biomolecules (such as proteins and lipids). Therefore, bioactive biomolecule separation techniques must fully consider the structure and properties of these biomolecules to achieve efficient and highly selective separation. Bioactive biomolecules are typically found in complex biological samples, such as blood, urine, and tissue fluid. These samples contain not only the target bioactive biomolecule but also a large number of other biomolecules and non-biological substances (such as salts and fats). Therefore, bioactive biomolecule separation techniques need to overcome these interfering factors to achieve efficient separation of the target bioactive biomolecule. The development of bioactive biomolecule separation techniques has gone through several stages. Early separation methods mainly relied on chemical and physical methods, such as centrifugation, precipitation, and extraction. While these methods could achieve a certain degree of separation, their efficiency was low, and they were prone to damaging bioactive biomolecules. With the development of science and technology, separation methods based on biological principles have gradually been developed, such as affinity chromatography, ion exchange chromatography, and gel filtration chromatography. These methods have high separation efficiency and good selectivity, but they still have certain limitations, such as small processing capacity and complex operation.
[0004] The ultimate goal of biomolecule separation technology is to analyze and identify the separated biomolecules. Therefore, the development of analytical techniques is crucial for biomolecule separation. Currently, commonly used analytical methods include spectrometry, electrophoresis, and mass spectrometry. These methods can accurately determine the structure, purity, and concentration of biomolecules, providing important data for subsequent research and applications. Summary of the Invention
[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for separating active enzymes from inactive enzymes.
[0006] The objective of this invention is achieved through the following technical solution: an enzyme separation method based on enzyme catalytic activity, used to separate active enzymes and inactive enzymes, comprising the following steps:
[0007] Describe the diffusion flux of an active enzyme in the presence of a substrate:
[0008] (1)
[0009] Describe the diffusion flux of inactive enzymes in the presence of substrate:
[0010] (2)
[0011] Where D1 represents the Fick's law diffusion coefficient of the active enzyme, and D2 represents the Fick's law diffusion coefficient of the inactive enzyme. Indicates the cross-diffusion coefficient of the active enzyme. Indicates the cross-diffusion coefficient of inactive enzymes; , These represent the concentration gradients of active and inactive enzymes, respectively. Substrate concentration gradient;
[0012] The cross-diffusion coefficient of the active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D1 of the active enzyme.
[0013] (3)
[0014] The cross-diffusion coefficient of the inactive enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D2 of the inactive enzyme.
[0015] (4)
[0016] in, Indicates the concentration of active enzyme. k1 represents the binding constant of the active enzyme, k2 represents the binding constant of the inactive enzyme;
[0017] Substituting equation (3) into equation (1), we obtain the coefficient of the cross-diffusion flow driven by the active enzyme:
[0018] (5)
[0019] Substituting equation (4) into equation (2) respectively, we obtain the coefficients for the cross-diffusion flow driven by inactive enzymes:
[0020] (6)
[0021] Modeling the reaction-diffusion equation for active enzyme group Q1:
[0022] (7)
[0023] Modeling the reaction-diffusion equation for inactive enzyme group Q2:
[0024] (8)
[0025] This represents the diffusion coefficient of the active enzyme group Q1. R(Q1) represents the diffusion coefficient of the inactive enzyme group Q2, R(Q1) represents the catalytic reaction rate of the active enzyme, XD(Q1,S) is the cross-diffusion term of the active enzyme group Q1 with respect to the substrate concentration gradient, and XD(Q2,S) is the cross-diffusion term of the inactive enzyme group Q2 with respect to the substrate concentration gradient, where S represents the substrate.
[0026] The cross-diffusion term of the active enzyme group Q1 is simulated according to formula (5):
[0027] (9)
[0028] The cross-diffusion term of the inactive enzyme group Q2 is simulated according to formula (6):
[0029] (10)
[0030] in, Indicates the concentration of active enzyme. Indicates the concentration of inactive enzyme;
[0031] Each substrate concentration corresponds to a partial differential equation. After obtaining a set of partial differential equations, the rate of change of function values is calculated in the discrete space. The odeint function is used for time numerical integration to solve the discrete equation set, obtaining the simulated value of the normalized intensity. Based on the simulated value of normalized intensity The rate of change of simulated values between the active enzyme group and the blank group, and the rate of change of simulated values between the inactive enzyme group and the blank group are calculated. That is, the migration rate of active enzyme and inactive enzyme is obtained respectively. The migration rates of active enzyme and inactive enzyme are compared, and the active enzyme and inactive enzyme are separated based on the difference in migration rate.
[0032] Furthermore, the active enzyme includes urease; the inactive enzyme includes β-galactosidase; and the substrate includes urea.
[0033] Furthermore, a series of substrate solutions with varying concentration gradients were prepared and mixed with the active enzyme solution, followed by further mixing of the substrate solutions. DLS measurements were then performed to obtain the particle size distribution of the active enzyme. Finally, the diffusion coefficient D1 was calculated using the Stokes-Einstein equation. The Stokes-Einstein equation is as follows:
[0034] D=
[0035] in, k Represents Boltzmann's constant. T Represents absolute temperature. η Indicates viscosity. R h This represents the average radius of the enzyme in the solution.
[0036] Furthermore, the DLS measurement is completed within 30 seconds after mixing the substrate solution.
[0037] Furthermore, the separation of active and inactive enzymes based on their different migration rates specifically involves: using a second microchannel, which is provided with an upper inlet, a lower inlet, a reaction chamber, an upper collection port, and a lower collection port; the upper inlet, lower inlet, upper collection port, and lower collection port are all connected to the reaction chamber; a substrate solution is introduced into the upper inlet, and a mixed solution of active and inactive enzymes is introduced into the lower inlet; the active enzyme flows out of the upper collection port, and the inactive enzyme flows out of the lower collection port.
[0038] This invention also provides an enzyme separation method based on enzyme catalytic activity, used to separate active enzymes and weakly active enzymes, comprising the following steps:
[0039] Describe the diffusion flux of an active enzyme in the presence of a substrate:
[0040] (1)
[0041] Describe the diffusion flux of a weakly active enzyme in the presence of a substrate:
[0042] (2)
[0043] Where D1 represents the Fick's law diffusion coefficient of the active enzyme, and D3 represents the Fick's law diffusion coefficient of the weakly active enzyme. Indicates the cross-diffusion coefficient of the active enzyme. Indicates the cross-diffusion coefficient of a weakly active enzyme; , These represent the concentration gradients of active and weakly active enzymes, respectively. Substrate concentration gradient;
[0044] The cross-diffusion coefficient of the active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D1 of the active enzyme.
[0045] (3)
[0046] The cross-diffusion coefficient of the weakly active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D3 of the weakly active enzyme.
[0047] (4)
[0048] in, Indicates the concentration of active enzyme. The concentration of the weakly active enzyme is represented by k1, the binding constant of the active enzyme is represented by k3, and the binding constant of the weakly active enzyme is represented by k3.
[0049] Substituting equation (3) into equation (1), we obtain the coefficient of the cross-diffusion flow driven by the active enzyme:
[0050] (5)
[0051] Substituting equation (4) into equation (2), we obtain the coefficients for the cross-diffusion flow driven by the weakly active enzyme:
[0052] (6)
[0053] Modeling the reaction-diffusion equation for active enzyme group Q1:
[0054] (7)
[0055] Modeling the Q3 reaction-diffusion equation for the weakly active enzyme group:
[0056] (8)
[0057] This represents the diffusion coefficient of the active enzyme group Q1. R(Q1) represents the diffusion coefficient of the weakly active enzyme group Q3, R(Q3) represents the catalytic reaction rate of the active enzyme, XD(Q1,S) is the cross-diffusion term of the active enzyme group Q1 with respect to the substrate concentration gradient, XD(Q3S) is the cross-diffusion term of the weakly active enzyme group Q3 with respect to the substrate concentration gradient, and S represents the substrate.
[0058] The cross-diffusion term of the active enzyme group Q1 is simulated according to formula (5):
[0059] (9)
[0060] The Q3 cross-diffusion term of the weakly active enzyme group is simulated according to formula (6):
[0061] (10)
[0062] in, Indicates the concentration of active enzyme. Indicates the concentration of a weakly active enzyme;
[0063] Each substrate concentration corresponds to a partial differential equation. After obtaining a set of partial differential equations, the rate of change of function values is calculated in the discrete space. The odeint function is used for time numerical integration to solve the discrete equation set, obtaining the simulated value of the normalized intensity. Based on the simulated value of normalized intensity The rate of change of simulated values between the active enzyme group and the blank group, and the rate of change of simulated values between the weak active enzyme group and the blank group are calculated. That is, the migration rate of active enzyme and weak active enzyme are obtained respectively. The migration rates of active enzyme and weak active enzyme are compared, and the active enzyme and weak active enzyme are separated based on the difference in migration rate.
[0064] Furthermore, the active enzyme includes urease; the weakly active enzyme includes β-galactosidase; and the substrate includes urea.
[0065] Furthermore, a series of substrate solutions with varying concentrations were prepared and mixed with the active enzyme solution, then the substrate solutions were mixed again, and DLS measurements were performed to obtain the particle size distribution of the active enzyme. Finally, the diffusion coefficient D1 of the active enzyme was calculated using the Stokes-Einstein equation; similarly, the diffusion coefficient D3 of the weakly active enzyme was calculated. The Stokes-Einstein equation is as follows:
[0066] D=
[0067] in, k Represents Boltzmann's constant. T Represents absolute temperature. η Indicates viscosity. R h This represents the average radius of the enzyme in the solution.
[0068] Furthermore, the DLS measurement is completed within 30 seconds after mixing the substrate solution.
[0069] Furthermore, the separation of active and weakly active enzymes based on their different migration rates specifically involves: using a second microchannel, which is provided with an upper inlet, a lower inlet, a reaction chamber, an upper collection port, and a lower collection port; the upper inlet, lower inlet, upper collection port, and lower collection port are all connected to the reaction chamber; a substrate solution is introduced into the upper inlet, and a mixed solution of active and weakly active enzymes is introduced into the lower inlet; the active enzyme flows out of the upper collection port, and the weakly active enzyme flows out of the lower collection port.
[0070] The beneficial effects of this invention are as follows:
[0071] (1) Separation is achieved by utilizing the catalytic activity of enzymes. This method does not require external field driving, avoids damage to biomolecules, and can achieve efficient separation.
[0072] (2) Traditional enzyme separation methods are limited to mixed enzymes with similar physical properties such as size and charge. This invention realizes the separation of active and inactive enzymes, which provides ideas for further development of methods to separate active and weakly active enzymes.
[0073] (3) The principle of this method helps to understand the self-driving behavior of enzyme molecules in depth, and further provides ideas for applications in the fields of self-driving motors, enzyme molecular nanomaterials, and enzyme molecular smart materials. Attached Figure Description
[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0075] Figure 1 This refers to the urease molecule's catalytic hydrolysis of urea and the binding site between urease and urea.
[0076] Figure 2 The Michaelis-Menten equation fitting curve for urease;
[0077] Figure 3 This is a schematic diagram of the microfluidic chip structure and the migration of urease into the substrate channel;
[0078] Figure 4 This is a graph showing the trend of diffusion coefficient of urease molecules in solutions with different substrate urea concentration gradients.
[0079] Figure 5 shows the normalized fluorescence intensity distribution of urease molecules migrating to their substrate urea. The urea concentration was kept constant. The results of the two different channel configurations both showed that urease has a tendency to migrate to the substrate urea.
[0080] Figure 6 The normalized fluorescence intensity distribution of urease molecules migrating at different substrate concentrations shows that as the substrate concentration gradually increases, the migration behavior of urease towards the channel where the substrate is located increases.
[0081] Figure 7 The migration of urease molecules at different substrate concentrations is calculated using a mathematical model based on cross-diffusion and catalysis.
[0082] Figure 8 The normalized intensity distribution of β-galactosidase molecules in the presence of urea and the corresponding migration calculated by a mathematical model based on cross-diffusion and catalysis.
[0083] Figure 9 A schematic diagram illustrating the separation of active and inactive enzyme molecules in an improved channel;
[0084] Figure 10 To improve the fluorescence distribution at the channel collection port;
[0085] Figure 11 To improve another fluorescence distribution at the channel collection port. Detailed Implementation
[0086] The present invention will now be described in detail with reference to the accompanying drawings. Unless otherwise specified, the features of the following embodiments and implementations can be combined with each other.
[0087] This invention uses urease (an active enzyme), β-galactosidase (an inactive enzyme), and urea as a substrate as an example. This invention uses a microfluidic observation platform to observe the chemotaxis of urease and its spontaneous separation from the inactive urease mimic (β-galactosidase). Figure 1 As shown, urease is a nickel-dependent metalloenzyme that catalyzes the decomposition of urea into ammonia and carbonic acid. Urease activation requires the combined action of structural proteins, accessory proteins, GTP (guanosine triphosphate), and CO2. Urea, the substrate of urease, is a waste product generated during the metabolic process of organisms. Urease plays an important role in organisms, helping them decompose urea to maintain homeostasis. The Michaelis constant of urease is typically between 1 and 10 mM. The Michaelis constant curve of urease used in this invention is shown in the figure. Figure 2 As shown, the Km value is 7 mM and the Kcat value is 3813 / s. Urease can spontaneously migrate in the surrounding fluid by catalyzing the conversion of its substrate urea. The chemotaxis of urease can be explained and quantified by two main effects. First, the study found that the enhanced diffusion of urease is substrate-dependent (…). Figure 2 The diffusion coefficient of urease changes with increasing substrate concentration and increases with increasing urea concentration, indicating that the amount of enzyme migration caused by chemotaxis is directly proportional to the substrate concentration. Secondly, cross-diffusion exists in the two-component system where urease and urea coexist; that is, the migration of urease is simultaneously affected by its own Fick diffusion and the substrate concentration gradient. Figure 4 As shown. The constructed microfluidic observation platform can effectively observe and verify the chemotactic behavior of urease in microchannels, such as... Figure 5 and Figure 6As shown, urease solution and urea solution were introduced into the microchannel using different formulations, and the flow rates were controlled to create a certain urea concentration gradient. Fluorescence distribution was collected at the outlet of the first microchannel to observe the aggregation of enzyme molecules. Next, a cross-diffusion mathematical model based on catalysis was used to describe the chemotaxis of urease in the microchannel. This model considers factors such as substrate concentration gradient, enzyme diffusion coefficient, substrate-enzyme binding constant, and enzyme catalysis, and predicts the enzyme distribution under the substrate concentration gradient by solving the diffusion equation. Figure 7 As shown, the enzyme molecule migration rates of the simulated group and the experimental group were fitted and compared. Then, another β-galactosidase (R), whose Stokes radius is close to that of urease, was used. 脲酶 ≈8nm;R GAL As an inactive enzyme mimic (≈7nm), this enzyme exhibits almost no specific interaction with urea, such as... Figure 8 As shown, the fluorescence distribution changes of β-galactosidase were almost identical to those of the blank group. Mathematical model calculations showed that the migration of the β-galactosidase group was consistent with that of the blank group, with a difference of approximately 2.5% from the normalized intensity of the experimental group. This demonstrates the feasibility of separating mixed enzyme molecules in a microchannel based solely on catalytic activity. To further implement separation, the microfluidic device was modified. The original microchannel with three inlets and one outlet was changed to two inlets (upper and lower inlets) and two outlets (upper and lower collection ports), forming a second microchannel, as shown below. Figure 9 As shown, two dyes with different wavelengths are used to distinguish between active and inactive enzymes. Based on the fluorescence distribution of the two dyes at the channel outlet, the outlet location where the target enzyme can be accurately recovered is determined (i.e., Figure 9 (Two outlets on the right side of the middle channel). Prepare a mixture of two enzymes stained with dyes of different wavelengths, maintaining the concentrations of both active and inactive enzymes at 200 nM. Pass the substrate solution through the upper inlet and the enzyme mixture through the lower inlet. After the fluid stabilizes, select the appropriate fluorescence excitation block based on the dye wavelength, such as... Figure 10 and Figure 11 As shown, by observing the direction of fluorescence aggregation, it was found that the fluorescence aggregation of active enzymes was concentrated in the substrate channel, while the fluorescence aggregation of inactive enzymes was concentrated in the lower channel. There was a significant difference in the direction of fluorescence aggregation between active and inactive enzymes. By controlling the range of the collection port, active and inactive enzymes can be separated.
[0088] Similarly, replacing inactive enzymes with weakly active enzymes can also achieve the separation of active and inactive enzymes, which will not be elaborated here.
[0089] By utilizing a microfluidic observation platform, the migration of active and inactive enzymes toward the same substrate is observed solely based on the catalytic activity of enzyme molecules. This enables the separation of active and inactive enzymes with similar physical properties, providing a novel method for the field of protein separation.
[0090] This invention provides a method for separating active enzymes from inactive enzymes. The microfluidic observation platform includes an observation platform, flow field configuration, measurement method, and analysis process.
[0091] In this invention, the observation platform includes an optical imaging system and a microfluidic chip mounted on it;
[0092] Preferably, the optical imaging system includes an inverted fluorescence microscope, an sCMOS camera, and an LED light source; the fluorescence excitation block of the inverted microscope focuses the incident light emitted by the LED light source onto the bottom of the microfluidic chip through the objective lens, and the sCMOS camera takes target images at regular intervals and in quantitative quantities. The microfluidic chip includes a Ψ-shaped PDMS microchannel with 3 inlets, 1 confluence point, 1 reaction chamber, and 1 outlet, namely the first microchannel ( Figure 3 ).
[0093] In this invention, the flow field configuration includes the concentrations of enzymes and urea in different solutions, the viscosities of different solutions, the order in which different solutions flow into the first microchannel inlet of the microfluidic chip, and the cross-laminar flow concentration gradient of different solutions; the measurement method includes the definition of physical parameters, image statistical algorithms, and numerical fitting principles. In this embodiment, the active enzyme molecule is a natural urease molecule extracted from the plant Jack's Bean; the inactive enzyme molecule is a natural β-galactosidase extracted from Escherichia coli.
[0094] Further, the substrate is urea molecules, a specific substrate of urease. The two enzyme solutions, urea solution, urease and urea mixed solution, and inactive enzyme and urea mixed solution are obtained by dissolving urease, inactive enzyme, and urea in a buffer solution; the buffer solution is PBS buffer. The substrate solution concentration ranges from 0 to 1 M; the enzyme concentration in each solution is maintained between the maximum limit of fluorescence detector saturation and the minimum limit for molecular chemotaxis sensitivity; the relative difference between the kinetic viscosity of the urease solution, inactive enzyme solution, and substrate solution at 25°C and the blank buffer solution does not exceed 10%; the urease solution, substrate solution, and mixed solution all meet the Reynolds number requirements for laminar flow. The substrate concentration in the mixed solution ranges from 0 to 1 M, and the concentrations of the active enzyme solution and inactive enzyme solution are maintained between the maximum limit of fluorescence detector saturation and the minimum limit for molecular chemotaxis sensitivity. The different solutions flow into the microchannel inlet of the microfluidic chip in a left-to-right order, including two configuration schemes: "urease and urea mixed solution / urease solution / urease and urea mixed solution" and "buffer solution / urease solution / urea solution". The cross-laminar concentration gradient is determined by the geometry of the reaction chamber, the volumetric flow rate, and the solute diffusion coefficient. The parameters are: width of the reaction chamber is 230~360μm, height is 50~150μm, length is 0.5~4cm, volumetric flow rate is 10~100μL / h, and solute diffusion coefficient is 1.64x10-9cm2·s−1. The physical parameters include the lateral chemotactic shift rate of urease, the concentration of the substrate, and the substrate catalytic conversion rate. The image statistical algorithm is used to statistically determine the chemotactic shift of urease to a specific concentration of substrate, including light intensity acquisition, background subtraction, inter-group averaging, normalization, and mathematical modeling calculation based on cross-diffusion and catalytic interaction.
[0095] In a specific embodiment of the present invention, the microfluidic chip includes a Ψ-shaped PDMS microchannel with three inlets, one confluence point, one reaction chamber, and one outlet. It also includes a dual-inlet, dual-outlet PDMS microchannel, i.e., a second microchannel. Its analysis process includes the following steps:
[0096] Step 1: First, prepare the active enzyme and inactive enzyme solutions separately. Then, prepare the substrate solution, the active enzyme and substrate mixture, the inactive enzyme and substrate mixture, the active enzyme and inactive enzyme mixture, and the buffer solution.
[0097] Step 2: First, seal the microchannel with BSA solution, rinse the channel, and then, according to the preset sequence of solution flow into the microchannel inlet of the microfluidic chip, accurately introduce each solution into the designated inlet of the microfluidic chip in sequence.
[0098] Step 3: Activate the LED light source to excite the acceptor to produce photoluminescence. Next, transmit the light through the objective lens to an sCMOS camera to periodically capture fluorescence images of the acceptor near the exit point, indicating that it has reached a diffusion steady state. Subsequently, use an image acquisition card to transfer these images to a computer, and employ the aforementioned image statistical algorithm to process and analyze the lateral light intensity distribution.
[0099] In the following embodiments, the fabrication of the microfluidic chip strictly followed conventional methods in the art. Specifically, for the fabrication of the PDMS chip, we used the Sylgard 184 Silicone elastomer kit, with Dow Corning as the monomer, and mixed it with a high-elasticity crosslinking agent at a mass ratio of 10:1. After a 2-hour degassing and defoaming treatment, the solution was poured onto a microchannel template and aged at 70°C for 12 hours. Afterwards, perforations were made at the channel inlet and outlet using a stainless steel needle, and the PDMS and a clean coverslip were sterilized in a plasma cleaner. The PDMS was gently pressed onto the pre-treated coverslip, and after seamless adhesion, it was annealed at 100°C for 5 minutes.
[0100] The coverslips used for microscopic imaging underwent rigorous pretreatment: first, they were soaked and cleaned for 2 hours in a near-boiling mixture of 7X cleaner and ultrapure water on a ceramic support, then rinsed with ultrapure water and dried, and finally annealed in a muffle furnace at 530°C for 6 hours.
[0101] The optical imaging section primarily utilized an inverted microscope (Nikon Eclipse Ti2−U). Light from the LED light source was focused onto the bottom of the PDMS chip via a green fluorescence excitation block. A Nikon sCMOS camera was used to capture fluorescence images of the channels, and real-time imaging was performed using NIS Elements software. Specific parameters were: 30-second exposure interval, 20-minute period, and 250-millisecond exposure time. The recorded images were transferred to a computer and processed and analyzed using an image acquisition card. The fluorescence intensity of the region of interest was selected, background subtracted, and averaged; then, normalization was performed using OriginPro 8.5 software with the channel width as the independent variable.
[0102] This invention provides a method for separating active and inactive enzymes. First, the migration and aggregation of enzyme molecules are observed in a Ψ-patterned PDMS microchannel to obtain data support. The active and inactive enzymes show a significant difference in their reactions to the substrate, and this difference is used to separate the two enzymes within the channel. Therefore, by improving existing microchannel devices, spontaneous separation of enzyme molecules within the channel is achieved, and they are recovered separately at the outlet. In this process, β-galactosidase is used as a mimic of the inactive enzyme, and urease is used as a mimic of the active enzyme. The specific steps are as follows:
[0103] Step 1: Prepare 200 nM urease solution and 200 nM β-galactosidase solution separately. Prepare two dyes (sulfonated Cy3 labeled urease twice; sulfonated Cy3 labeled β-galactosidase once; AF405 labeled β-galactosidase once). Each time, the molar ratio of the enzyme (including active and inactive enzymes; if separating active and weakly active enzymes, then the enzymes include both active and weakly active enzymes) to the dye is 1:10. After mixing, react for 5 hours. Specifically, for sulfonated Cy3 labeled urease (Ex / Em: 550 / 564), observe the fluorescence distribution using a green excitation block under a microscope; for AF405 labeled urease (Ex / Em: 400 / 424), observe the fluorescence distribution using a purple excitation block under a microscope. During the experiment, the dyes taken from the low-temperature freezer were allowed to reach room temperature before opening the bottle cap to prepare the stock solution. The stock solution concentration was 0.1 mg / 1 ml. Take 0.2 ml of the stock solution and dilute it to 5 ml with homemade PBS. After mixing the enzyme and dye solutions, gently stir with a 5ml pipette to ensure thorough mixing. After 5 hours of reaction, dialyze for 16 hours, changing the dialysate (homemade PBS) three times during this period. Protect from light and maintain a low temperature with an ice pack. After dialysis, measure the fluorescence intensity ratio inside / outside the dialysis bag using a microplate reader (Ex540, Em566). If the intensity inside the bag is more than 100 times higher than outside, the dialysis is effective. After dialysis, measure the labeling efficiency, calculate the molar concentration of the dye molecules and the molar concentration of the protein, and compare them.
[0104] Step 2: Prepare a series of substrate solutions with varying concentrations and mix them separately with the urease solution prepared in step (1). Then mix the substrate solutions separately. After mixing the urease solution and urea solution for 30 seconds, the DLS measurement is completed to reduce the time for protein aggregation. A monochromatic light beam (e.g., a laser) is irradiated into a test solution containing spherical particles moving in Brownian motion. When the light hits the moving particles, it causes a Doppler frequency shift, thereby changing the wavelength of the original light. The time fluctuation is analyzed by measuring the photon autocorrelation function (ACF), and the urease particle size distribution is then calculated. Finally, the diffusion coefficient D1 is calculated using the Stokes-Einstein equation.
[0105] Step 4: Following the derivation below, establish an index for measuring changes in urease migration and calculate it. Figure 6 , Figure 8 ):
[0106] In multi-component systems, diffusion is influenced not only by its own concentration gradient but also by the concentration gradients of other components. Therefore, for a two-component system of urease and urea in a microchannel, a complete theoretical description of its diffusion phenomenon requires combining Fick's law of urease diffusion with the effect of the urea concentration gradient on urease diffusion.
[0107] First, the diffusion flux of free urease in the presence of substrate is described:
[0108] (1)
[0109] Describe the diffusion flux of β-galactosidase in the presence of substrate:
[0110] (2)
[0111] D represents the Fick's law diffusion coefficient. Represents the cross-diffusion coefficient. , These represent the enzyme concentration gradient and the substrate concentration gradient, respectively. The cross-diffusion coefficient D is calculated using the local substrate concentration Cs and the diffusion coefficient D (obtained from the Einstein equation). XD :
[0112] Where D1 represents the Fick's law diffusion coefficient of urease, and D2 represents the Fick's law diffusion coefficient of β-galactosidase. The cross-diffusion coefficient of urease is represented. This represents the cross-diffusion coefficient of β-galactosidase; , These represent the concentration gradients of urease and β-galactosidase, respectively. Substrate concentration gradient;
[0113] The cross-diffusion coefficient of urease was calculated by comparing each substrate concentration Cs with the diffusion coefficient D1 of urease.
[0114] (3)
[0115] The cross-diffusion coefficient of β-galactosidase was calculated by comparing each substrate concentration Cs with the diffusion coefficient D2 of β-galactosidase.
[0116] (4)
[0117] in, Indicates urease concentration. The concentration of β-galactosidase is represented by k1, the binding constant of the active enzyme is represented by k2, and the binding constant of the inactive enzyme is represented by k2.
[0118] Substituting equation (3) into equation (1), we obtain the coefficient of the urease-driven cross-diffusion flow:
[0119] (5)
[0120] Substituting equation (4) into equation (2), we obtain the coefficients for the cross-diffusion flow driven by β-galactosidase:
[0121] (6)
[0122] The first term in Equations (5) and (6) represents conventional diffusion to regions of low enzyme concentration, while the second term, with the opposite sign, indicates flow to higher substrate concentrations. Besides the substrate concentration gradient, the magnitude of this coefficient is determined by three factors: the diffusion coefficient D, the enzyme concentration Ce, and a factor proportional to the number of enzyme binding sites occupied by the substrate in a given time. Similar to Fick diffusion, cross-diffusion drift is driven by thermodynamic forces that reduce the chemical potential of the system resulting from enzyme-substrate binding.
[0123] Next, the reaction-diffusion equation of urease group Q1 was modeled:
[0124] (7)
[0125] Modeling the reaction-diffusion equation for β-galactosidase group Q2:
[0126] (8)
[0127] This represents the diffusion coefficient of urease group Q1. R(Q1) represents the diffusion coefficient of β-galactosidase group Q2, R(Q1) represents the catalytic reaction rate of the active enzyme, XD(Q1,S) is the cross-diffusion term of urease group Q1 with respect to the substrate concentration gradient, XD(Q2,S) is the cross-diffusion term of β-galactosidase group Q2 with respect to the substrate concentration gradient, and S represents the substrate.
[0128] The cross-diffusion term of urease group Q1 is simulated according to formula (5):
[0129] (9)
[0130] The cross-diffusion term of β-galactosidase group Q2 is simulated according to formula (6):
[0131] (10)
[0132] in, Indicates the concentration of active enzyme. Indicates the concentration of inactive enzymes
[0133] Each substrate concentration corresponds to a partial differential equation. After obtaining a set of partial differential equations, the rate of change of function values is calculated in the discrete space. The odeint function is used for time numerical integration to solve the discrete equation set, obtaining the simulated value of the normalized intensity. Based on the simulated value of normalized intensity The rate of change of simulated values between the active enzyme group and the blank group, and the rate of change of simulated values between the inactive enzyme group and the blank group were calculated. That is, the migration rates of active enzyme and inactive enzyme were obtained respectively. The migration rates of active enzyme and inactive enzyme were compared, and the separation of urease and β-galactosidase was achieved based on the difference in migration rates.
[0134] The migration rate change rate of active enzyme = (simulated value of active enzyme - simulated value of blank group) / simulated value of blank group; the migration rate of inactive enzyme = (simulated value of inactive enzyme - simulated value of blank group) / simulated value of blank group; the migration rate of weakly active enzyme = (simulated value of weakly active enzyme - simulated value of blank group) / simulated value of blank group; the blank group refers to the group where only urease is introduced into the three channel entrances, i.e., the substrate urea is not present.
[0135] The separation of active and inactive enzymes based on their different migration rates is specifically achieved as follows: A second microchannel is used, with urea solution introduced into the upper inlet and a mixed solution of urease and β-galactosidase introduced into the lower inlet. The concentrations of both urease and β-galactosidase are maintained at 200 nM, and the urea concentration is maintained at 1 M. The fluorescence distribution at the collection port (the end of the channel) is observed; urease migrates towards the substrate channel, and its fluorescence accumulates at the upper collection port (…). Figure 10 β-galactosidase showed no significant migration except for its own Fick diffusion; its fluorescence was concentrated at the lower collection port. Figure 11 In this way, urease and β-galactosidase can be separated.
[0136] In one embodiment, experimental samples are prepared, including the fluorescently labeled active enzyme solution and inactive enzyme solution, urea solution (concentration gradient: 300mM, 500mM, 700mM, 1M), a urease and urea mixture, and a β-galactosidase and urea mixture (maintaining urease and β-galactosidase concentrations at 200 nM and urea concentration at 1 M in the mixture). A saturated bovine serum albumin solution is pre-blocked by passing it through the microchannel. After blocking, residual free bovine serum albumin is rinsed off with PBS buffer. After blocking, the sample solution is introduced into the microchannel at a rate of 50 μL / min according to different configurations. After introducing 0.2 ml, the rate is changed to 50 μL / h. After the laminar flow stabilizes, fluorescence distribution is collected at the Upper and Lower positions, respectively. Figure 5 , Figure 6The specific configuration schemes are as follows: active enzyme and substrate mixed solution / active enzyme / active enzyme and substrate mixed solution; buffer / active enzyme / substrate; active enzyme and substrate 300 mM mixed solution / active enzyme / active enzyme and substrate 300 mM mixed solution; active enzyme and substrate 500 mM mixed solution / active enzyme / active enzyme and substrate 500 mM mixed solution; active enzyme and substrate 700 mM mixed solution / active enzyme / active enzyme and substrate 700 mM mixed solution; active enzyme and substrate 1 M mixed solution / active enzyme / active enzyme and substrate 1 M mixed solution.
[0137] Similarly, replacing inactive enzymes with weakly active enzymes can also achieve the separation of active and inactive enzymes, which will not be elaborated here.
[0138] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.
Claims
1. An enzyme separation method based on enzyme catalytic activity, characterized in that, The method for separating active and inactive enzymes includes the following steps: Describe the diffusion flux of an active enzyme in the presence of a substrate: (1) Describe the diffusion flux of inactive enzymes in the presence of substrate: (2) Where D1 represents the Fick's law diffusion coefficient of the active enzyme, and D2 represents the Fick's law diffusion coefficient of the inactive enzyme. Indicates the cross-diffusion coefficient of the active enzyme. Indicates the cross-diffusion coefficient of inactive enzymes; , These represent the concentration gradients of active and inactive enzymes, respectively. Substrate concentration gradient; The cross-diffusion coefficient of the active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D1 of the active enzyme. (3) The cross-diffusion coefficient of the inactive enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D2 of the inactive enzyme. (4) in, Indicates the concentration of active enzyme. k1 represents the binding constant of the active enzyme, k2 represents the binding constant of the inactive enzyme; Substituting equation (3) into equation (1), we obtain the coefficient of the cross-diffusion flow driven by the active enzyme: ） (5) Substituting equation (4) into equation (2) respectively, we obtain the coefficients for the cross-diffusion flow driven by inactive enzymes: (6) Modeling the reaction-diffusion equation for active enzyme group Q1: (7) Modeling the reaction-diffusion equation for inactive enzyme group Q2: (8) This represents the diffusion coefficient of the active enzyme group Q1. R(Q1) represents the diffusion coefficient of the inactive enzyme group Q2, R(Q1) represents the catalytic reaction rate of the active enzyme, XD(Q1,S) is the cross-diffusion term of the active enzyme group Q1 with respect to the substrate concentration gradient, and XD(Q2,S) is the cross-diffusion term of the inactive enzyme group Q2 with respect to the substrate concentration gradient, where S represents the substrate. The cross-diffusion term of the active enzyme group Q1 is simulated according to formula (5): (9) The cross-diffusion term of the inactive enzyme group Q2 is simulated according to formula (6): (10) in, Indicates the concentration of active enzyme. Indicates the concentration of inactive enzyme; Each substrate concentration corresponds to a partial differential equation. After obtaining a set of partial differential equations, the rate of change of function values is calculated in the discrete space. The odeint function is used for time numerical integration to solve the discrete equation set, obtaining the simulated value of the normalized intensity. Based on the simulated value of normalized intensity The rate of change of simulated values between the active enzyme group and the blank group, and the rate of change of simulated values between the inactive enzyme group and the blank group are calculated. That is, the migration rate of active enzyme and inactive enzyme is obtained respectively. The migration rates of active enzyme and inactive enzyme are compared, and the active enzyme and inactive enzyme are separated based on the difference in migration rate.
2. The method according to claim 1, characterized in that, The active enzyme includes urease; the inactive enzyme includes β-galactosidase; and the substrate includes urea.
3. The method according to claim 1, characterized in that, A series of substrate solutions with varying concentrations were prepared and mixed with the active enzyme solution, followed by further mixing with the substrate solution. DLS measurements were then performed to obtain the particle size distribution of the active enzyme. Finally, the diffusion coefficient D1 was calculated using the Stokes-Einstein equation. The Stokes-Einstein equation is as follows: D= ； in, k Represents Boltzmann's constant. T Represents absolute temperature. η Indicates viscosity. R h This represents the average radius of the enzyme in the solution.
4. The method according to claim 3, characterized in that, The DLS measurement was completed within 30 seconds after mixing the substrate solution.
5. The method according to claim 1, characterized in that, The separation of active and inactive enzymes based on their different migration rates is specifically achieved by using a second microchannel, which is provided with an upper inlet, a lower inlet, a reaction chamber, an upper collection port, and a lower collection port; the upper inlet, lower inlet, upper collection port, and lower collection port are all connected to the reaction chamber; a substrate solution is introduced into the upper inlet, and a mixed solution of active and inactive enzymes is introduced into the lower inlet; The enzyme flowing out of the upper collection port is active, while the enzyme flowing out of the lower collection port is inactive.
6. An enzyme separation method based on enzyme catalytic activity, characterized in that, The method for separating active and weakly active enzymes includes the following steps: Describe the diffusion flux of an active enzyme in the presence of a substrate: (1) Describe the diffusion flux of a weakly active enzyme in the presence of a substrate: (2) Where D1 represents the Fick's law diffusion coefficient of the active enzyme, and D3 represents the Fick's law diffusion coefficient of the weakly active enzyme. Indicates the cross-diffusion coefficient of the active enzyme. Indicates the cross-diffusion coefficient of a weakly active enzyme; , These represent the concentration gradients of active and weakly active enzymes, respectively. Substrate concentration gradient; The cross-diffusion coefficient of the active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D1 of the active enzyme. (3) The cross-diffusion coefficient of the weakly active enzyme was calculated by comparing each substrate concentration Cs with the diffusion coefficient D3 of the weakly active enzyme. (4) in, Indicates the concentration of active enzyme. The concentration of the weakly active enzyme is represented by k1, the binding constant of the active enzyme is represented by k3, and the binding constant of the weakly active enzyme is represented by k3. Substituting equation (3) into equation (1), we obtain the coefficient of the cross-diffusion flow driven by the active enzyme: ） (5) Substituting equation (4) into equation (2), we obtain the coefficients for the cross-diffusion flow driven by the weakly active enzyme: (6) Modeling the reaction-diffusion equation for active enzyme group Q1: (7) Modeling the Q3 reaction-diffusion equation for the weakly active enzyme group: (8) This represents the diffusion coefficient of the active enzyme group Q1. R(Q1) represents the diffusion coefficient of the weakly active enzyme group Q3, R(Q3) represents the catalytic reaction rate of the active enzyme, XD(Q1,S) is the cross-diffusion term of the active enzyme group Q1 with respect to the substrate concentration gradient, XD(Q3S) is the cross-diffusion term of the weakly active enzyme group Q3 with respect to the substrate concentration gradient, and S represents the substrate. The cross-diffusion term of the active enzyme group Q1 is simulated according to formula (5): (9) The Q3 cross-diffusion term of the weakly active enzyme group is simulated according to formula (6): (10) in, Indicates the concentration of active enzyme. Indicates the concentration of a weakly active enzyme; Each substrate concentration corresponds to a partial differential equation. After obtaining a set of partial differential equations, the rate of change of function values is calculated in the discrete space. The odeint function is used for time numerical integration to solve the discrete equation set, obtaining the simulated value of the normalized intensity. Based on the simulated value of normalized intensity The rate of change of simulated values between the active enzyme group and the blank group, and the rate of change of simulated values between the weak active enzyme group and the blank group are calculated. That is, the migration rate of active enzyme and weak active enzyme are obtained respectively. The migration rates of active enzyme and weak active enzyme are compared, and the active enzyme and weak active enzyme are separated based on the difference in migration rate.
7. The method according to claim 6, characterized in that, The active enzyme includes urease; the weakly active enzyme includes β-galactosidase; and the substrate includes urea.
8. The method according to claim 6, characterized in that, A series of substrate solutions with varying concentrations were prepared and mixed with the active enzyme solution, followed by further mixing with the substrate solution. DLS measurements were then performed to obtain the particle size distribution of the active enzyme. Finally, the diffusion coefficient D1 of the active enzyme was calculated using the Stokes-Einstein equation. Similarly, the diffusion coefficient D3 of the weakly active enzyme was calculated. The Stokes-Einstein equation is as follows: D= ； in, k Represents Boltzmann's constant. T Represents absolute temperature. η Indicates viscosity. R h This represents the average radius of the enzyme in the solution.
9. The method according to claim 8, characterized in that, The DLS measurement was completed within 30 seconds after mixing the substrate solution.
10. The method according to claim 6, characterized in that, The separation of active and weakly active enzymes based on their different migration rates is specifically achieved by using a second microchannel, which is provided with an upper inlet, a lower inlet, a reaction chamber, an upper collection port, and a lower collection port; the upper inlet, lower inlet, upper collection port, and lower collection port are all connected to the reaction chamber; a substrate solution is introduced into the upper inlet, and a mixed solution of active and weakly active enzymes is introduced into the lower inlet; The enzyme flowing out of the upper collection port is active, while the enzyme flowing out of the lower collection port is weakly active.