Method for obtaining a galactosidase affibody and corresponding affibody and applications

Abstract

This invention provides a method for obtaining an affinity for galactosidase, which utilizes the affinity expressed by bacteria to effectively identify galactosidase.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, specifically to a method for screening galactosidase affinity molecules and the affinity molecules screened therefrom. Background Technology

[0002] Galactosidase, as one of the hallmark functional enzymes of the gut microbiota, directly reflects the balance of the gut microecology through changes in its activity and abundance, and is a potential biomarker for early screening of intestinal diseases. Clinical intervention and treatment of lactose intolerance: Approximately 68% of adults worldwide suffer from lactose intolerance. β-galactosidase (commonly known as lactase) can directly hydrolyze indigestible lactose into glucose and galactose. Exogenous lactase is formulated into over-the-counter (OTC) oral preparations (such as Tilactase), which, when taken before meals, can significantly alleviate symptoms such as bloating, diarrhea, and borborygmus caused by lactase deficiency. Furthermore, it is widely used as an adjunct treatment for lactose intolerance in infants and young children. Enzyme replacement therapy (ERT) for lysosomal storage diseases is the most representative application of galactosidase in the treatment of severe cases. Fabry disease, a rare X-linked inherited disorder, is an example. Patients are born with a deficiency of α-galactosidase A, leading to the accumulation of lipid substrates in vascular endothelium and various organs. Intravenous injection of recombinant human α-galactosidase (such as galactosidase β / agalsidase β) can fundamentally replenish the missing enzyme, effectively clearing accumulated metabolic products and improving the patient's quality of life and prognosis.

[0003] Affibody is an artificial mutant derived from the staphylococcal protein A (SPA) B domain. It is a novel protein ligand with the ability to bind to other proteins. Composed of 58 amino acids, forming three α-helical structures, the affibody has a molecular weight of approximately 6.5 kDa, only 1 / 20th that of conventional antibodies and half that of single-domain antibodies (sdAbs). Due to its small molecular weight, it has strong permeability and tissue penetration, easily penetrating into the interstitial space in dense tissues such as solid tumors to specifically bind to target proteins on the cell surface or intracellularly. Unbound affibody in the blood can be rapidly filtered out by the glomeruli. Fluorescent / radioisotope-labeled affibody can be used as a tracer for in vivo imaging diagnosis with lower background. Furthermore, affibody has weak immunogenicity, reducing the likelihood of stimulating an immune response. Conventional antibodies contain multiple domains. Upon binding to the antigen, the Fab fragment alters the antibody's conformation, potentially activating complement and inducing Fc fragment immune effects such as opsonization and antibody-dependent cell-mediated cytotoxicity (ADCC), leading to unintended immunotoxic side effects. In contrast, affinities have a very simple structure, consisting of only three α-helices and lacking an Fc domain, thus avoiding Fc-terminal biological effects. The amino acid residues at specific sites (9, 10, 11, 13, 14, 17, 18, 24, 25, 27, 28, 32, 35) in the first and second helices have no significant impact on the affinity's conformation. These sites can be randomly mutated to create affinities that theoretically can bind to any target molecule. Affinities possess high affinity for target proteins and are structurally stable, making them extremely versatile for the detection, separation, and purification of target proteins. Affinities, as a class of artificially designed small molecule binding proteins, have advantages such as small molecular weight, high stability, ease of modification and large-scale expression. They will become a new type of recognition element to replace traditional antibodies and show broad application prospects in the fields of biological detection and targeted delivery.

[0004] The main detection methods for galactosidase include: For different applications (such as microbial identification, gene expression analysis, and clinical sample testing), various detection methods have been developed, such as: The ONPG method (the gold standard for quantitative detection) works by using o-nitrophenyl-β-D-galactosidase (ONPG) as a substrate, which is hydrolyzed by galactosidase to produce yellow o-nitrophenol (ONP). It is commonly used for the identification of slow fermentation in bacteria such as Escherichia coli, and for the precise quantitative analysis of β-galactosidase activity in cell lysates or bacterial cultures by measuring absorbance at 420 nm (or 405 nm). The X-Gal method (qualitative / in situ staining) works by using 5-bromo-4-chloro-3-indole-β-D-galactosidase (X-Gal) as a substrate, which produces an insoluble blue precipitate after enzymatic digestion. This is a core technology in molecular biology for blue-white screening, used to rapidly select recombinant colonies with successfully cloned genes. In addition, modified X-Gal staining (SA-β-Gal staining) is also a classic method for detecting cellular senescence. The principle of medical colorimetric / spectrophotometry: Using specific substrates such as p-nitrobenzene-α-D-galactopyranoside, the enzymatically hydrolyzed p-nitrophenol exhibits a characteristic absorption peak at 400 nm. It is mainly used in clinical testing, for example, by measuring changes in absorbance in patient serum or tissue samples to calculate the content of α-galactosidase, aiding in the diagnosis of related genetic metabolic diseases.

[0005] The above detection methods are all based on the activity detection of galactosidase. Currently, the detection of galactosidase itself is mainly based on antibody detection. There is a lack of effective supplements to the antibody detection system. Effectively overcoming the shortage of antibody prices and large-scale antibody preparation technology are the main factors restricting the detection of galactosidase itself. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for screening affinity molecules that bind to target proteins. The method includes constructing a template peptide, preparing the target protein structure, performing molecular docking, optimizing the docking conformation, establishing a virtual peptide library, molecular dynamics verification, expressing the affinity molecule, and determining the affinity between the affinity molecule and the target protein. This invention uses lactosidase as a target, integrating molecular docking, virtual mutagenesis, and experimental verification techniques to design, prepare, and functionally characterize high-affinity affinity molecules. The aim is to establish a comprehensive affinity molecule development technology system from computer simulation to experimental verification, providing core biorecognition elements for the subsequent construction of affinity-based galactosidase detection platforms, and offering methodological references for the modification of affinity molecules for glycoside hydrolase targets.

[0007] In one embodiment of the present invention, the affinity template polypeptide is the affinity template numbered 2B88 in the PDB (Protein Data Bank) structural database.

[0008] In one embodiment of the present invention, the preparation of the target protein structure involves downloading the crystal structure of β-galactosidase (three-dimensional crystal structure of β-galactosidase) with the accession number PDB ID: 1DP0 (Protein Data Bank accession number 1DP0, abbreviated as PDB ID: 1DP0, resolution 0.25 nm) from the PDB database, preprocessing it using the Prepare Protein tool in DS 2020 to remove co-crystallized small molecule ligands and water molecules, adding polar hydrogen atoms, and completing the energy minimization optimization of the protein structure.

[0009] In one embodiment of the present invention, the molecular docking is specifically performed by: performing molecular docking between the template polypeptide and the target protein, and outputting the top 2000 docking conformations with the best binding ability.

[0010] In a preferred embodiment of the invention, the molecular docking is performed using one or more of the following software: Discovery Studio™, Rosetta, FoldIt, HADDOCK, HEX, and LightDock; preferably, the RDOCKR or ZDOCK tool in Discovery Studio™ is used, with ZDOCK being the preferred tool.

[0011] In one embodiment of the present invention, the optimization of the docking conformation specifically involves using the ZRANK tool for cluster analysis, taking the top 30 conformations with the highest ranking, applying the RDOCK method to re-optimize the docking conformation, calculating the binding free energy (ΔG) through E-Rdock, selecting conformations with low free energy that meet the purpose of modification, and screening out the docking conformation with the strongest binding ability.

[0012] In one embodiment of the present invention, the establishment of the virtual peptide library specifically involves performing saturation mutations on 13 amino acid sites on the affinity of the docking conformation to establish the virtual peptide library and preliminarily evaluating the affinity and stability of the screened affinity to the target protein.

[0013] In one embodiment of the present invention, the molecular dynamics verification specifically involves re-dating the selected affinity molecules with the target protein and performing static simulation verification at a temperature of 300 K and a pressure of 1 Bar.

[0014] In one embodiment of the present invention, expressing the affinity includes directly synthesizing the polypeptide of the affinity and expressing the mutant by fusing the mutant gene into an expression vector.

[0015] In a preferred embodiment of the present invention, the expression vector is selected from pET vectors.

[0016] In a preferred embodiment of the present invention, the expression of the affinity further includes purifying the expressed affinity, preferably using a nickel ion exchange resin, and more preferably using His60 Ni Superflow Resin resin.

[0017] In one embodiment of the present invention, the affinity between the affinity compound and the target protein is determined using microthermophoresis (MST), biomembrane interference (BLI), or surface plasmon resonance (SPR), preferably microthermophoresis (MST).

[0018] In another aspect, the present invention provides an affinity strain screened by the aforementioned method, the amino acid sequence of which has more than 95% identity with the amino acid sequence shown in any one of SEQ ID NO: 2-4; preferably, the amino acid sequence is shown in any one of SEQ ID NO: 2-4.

[0019] In another aspect, the present invention provides a nucleic acid molecule encoding the affinity.

[0020] In one embodiment of the present invention, the nucleotide sequence of the nucleic acid molecule is shown in any one of SEQ ID NO: 6-8.

[0021] In another aspect, the present invention provides an expression vector comprising the nucleic acid molecule.

[0022] In another aspect, the present invention provides a pharmaceutical composition comprising the aforementioned affinity or nucleic acid molecule or expression vector and a pharmaceutically acceptable carrier.

[0023] In another aspect, the present invention provides the use of the aforementioned affinity molecules, nucleic acid molecules, expression vectors, and pharmaceutical compositions in the preparation of chips or kits for detecting galactosidase antigen levels. Beneficial effects

[0024] The galactosidase-binding affinity elicitor provided by this invention exhibits good affinity and specificity for galactosidase, and can be applied to the antigen-antibody detection of galactosidase, thereby improving the detection efficiency of galactosidase. This invention optimizes the coding sequence of the provided galactosidase-binding affinity elicitor, making it easier to express in bacteria.

[0025] The galactosidase-binding affinity provided by this invention can be produced using bacterial fermentation technology, which has a much higher production capacity than the galactosidase antibody production model. It is also easy to establish a platform system for the construction, expression, purification, and application of this affinity, effectively accelerating the construction of the affinity industry platform. Attached Figure Description

[0026] Figure 1 The protein structure diagrams of β-galactosidase and its affinity are shown, including: (a) the optimized β-galactosidase model and (b) the optimized affinity model.

[0027] Figure 2 The diagram shows the binding of β-galactosidase to its affinity, including: (a) a schematic diagram of the docking site; and (b) the structure of the β-galactosidase-affinity complex Pose184 selected from the samples.

[0028] Figure 3 The diagram shows the structures of the three mutant affinities.

[0029] Figure 4 The diagram shows the binding surfaces of β-galactosidase and its affinity, including: (a) molecular dynamics simulation of galactosidase with affinity 1 (the left side shows the conformation before simulation, and the right side shows the conformation after simulation), (b) molecular dynamics simulation of galactosidase with affinity 2 (the left side shows the conformation before simulation, and the right side shows the conformation after simulation), (c) molecular dynamics simulation of galactosidase with affinity 3 (the left side shows the conformation before simulation, and the right side shows the conformation after simulation), and (d) RMSD distribution plot, where a mean fluctuation of no more than 0.2 indicates a stable conformation.

[0030] Figure 5 The SDS-PAGE results for β-galactosidase affinity 2 under different induction conditions include: (a) induction expression results of affinity A1 at different IPTG concentrations, (b) induction expression results of affinity A2 at different IPTG concentrations, (c) results of affinity A2 at different induction times, (d) results of affinity A2 at different induction temperatures, and (ef) the target protein content corresponding to the above figure.

[0031] Figure 6 The SDS-PAGE results for β-galactosidase affinity 3 under different induction conditions include: (a) induction expression results of affinity A2 with different competent cells, (b) induction expression results of affinity A2 with different IPTG concentrations, (c) results of affinity A3 with different induction times, (d) results of affinity A3 with different induction temperatures, and (ef) the target protein content corresponding to the above figure.

[0032] Figure 7 The SDS-PAGE results for purified β-galactosidase affinity 2 and affinity 3 are shown below, including: (a) nickel column purification results for affinity A2, (b) gel filtration chromatography absorption peak curve for affinity A2, (c) gel filtration chromatography concentration results for affinity A2, (d) nickel column purification results for affinity A3, (e) gel filtration chromatography absorption peak curve for affinity A3, and (f) gel filtration chromatography concentration results for affinity A3.

[0033] Figure 8 The results of thermal stability curves for affinity A2 and affinity A3 are as follows: (a) thermal stability curve of affinity A2, (b) thermal stability curve of affinity A3.

[0034] Figure 9 The circular dichroism spectral results for affinity A2 and affinity A3 include: (a) the circular dichroism spectral curve of affinity A2, and (b) the circular dichroism spectral curve of affinity A3.

[0035] Figure 10 The results of circular dichroism spectroscopy detection of the interaction between affinity A2 and affinity A3 with β-galactosidase are shown, including: (a) circular dichroism spectral curves of the interaction between affinity A2 and β-galactosidase at different concentrations, and (b) circular dichroism spectral curves of the interaction between affinity A3 and β-galactosidase at different concentrations.

[0036] Figure 11 The MST results of affinity A2 and affinity A3 with β-galactosidase are shown, including: (a) MST curve of affinity A2 with β-galactosidase, and (b) MST curve of affinity A3 with β-galactosidase.

[0037] Figure 12 This serves as proof that the β-galactosidase affinity A2 cannot specifically bind to glucosidase.

[0038] Figure 13 This serves as proof that the β-galactosidase affinity A3 cannot specifically bind to glucosidase. Detailed Implementation

[0039] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art, and the raw materials used are all commercially available products.

[0040] This application relates to a list of nucleotide and / or amino acid sequences (WIPO standard XML format). Specifically, SEQ ID NO: 1-4 are the corresponding amino acid sequences, and SEQ ID NO: 5-8 are the nucleotide sequences encoding the Affibody. This XML format sequence list forms an integral part of this application.

[0041] Example 1: Molecular docking of β-galactosidase with its affinity and target screening

[0042] 1. Protein preparation and pretreatment The 3D crystal structure of β-galactosidase (PDB ID: 1DP0) was downloaded from the RCSB PDB database and preprocessed using the Prepare Protein tool in DS2020. Small ligands and water molecules from the co-crystallization were removed, and polar hydrogen atoms were added to optimize the protein structure by minimizing energy. The crystal structure of the affinity protein (PDB ID: 2B88) was downloaded and preprocessed using the same procedure to ensure that both protein structures were in a reasonable force field energy state.

[0043] 2. Molecular docking and conformation screening Using the ZDOCKER (CHARMM simulation docking) module in DS 2020, pretreated affinity molecules were used as ligands for flexible docking with β-galactosidase. Specific parameters: During the calculation, default CHARMM force field parameters were set, and a fully flexible docking mode was adopted, allowing conformational sampling of the ligand within the active pocket. An Euler angle of 6 degrees was used for sampling the binding conformations, resulting in a final docking sample size of 54,000 conformations. The top 2,000 docking conformations with the best binding ability were then output. After docking, based on the DOCKER Score (docking binding energy) and the global binding mode, the top 10 complex conformations with the lowest binding free energy (ΔG) and the most stable conformations were selected.

[0044] 3. Combined with pattern analysis Using the Ligand Interactions and Protein-Ligand Interaction Fingerprints tools in DS 2020, a detailed analysis was conducted on the selected optimal conformation. The types and quantities of non-covalent interactions such as hydrogen bonds, salt bridges, and hydrophobic stacking between interfaces were statistically analyzed and visualized. Ultimately, the key residue sites for β-galactosidase binding to its affinity variant were determined, providing a structural basis for subsequent virtual mutagenesis.

[0045] Example 2: Screening and Molecular Modification of Mutant Amino Acids in Affinity Proteins

[0046] 1. Selection of mutation sites Based on the results of the binding mode analysis, key residues on the complex interface that participate in hydrogen bonding, salt bridging, or hydrophobic interactions were selected as candidate mutation sites.

[0047] 2. Single-point mutation construction The optimal complex conformation was imported into DS 2020, and single-point saturation mutations were performed on candidate sites using the Build Mutations tool. The target residues were then replaced with the remaining 19 standard amino acids, and the affinity protein three-dimensional structure was automatically generated. The Minimize Protein module was used to minimize the local energy of the mutated region and eliminate atomic spatial conflicts.

[0048] 3. Combined with free energy (ΔG) measurement: The Calculate Binding Energy module (based on the CHARMM force field) was used to calculate the binding free energy (ΔG) of the complexes of wild type and each mutant. Using the wild type binding free energy as a reference, positive mutation sites with significantly reduced binding free energy (ΔG) (i.e., more stable binding) were screened out, while negative mutations that led to increased binding free energy (ΔG) were excluded.

[0049] 4. Multi-point combination mutation and verification The positive mutation sites identified through screening were combined with other mutations to construct multi-point mutant models. The local energy minimization and binding free energy (ΔG) calculation process was repeated, and the binding free energy (ΔG) and structural stability of different combined mutants were compared. Finally, the candidate affinity sequence with the optimal binding affinity was determined, providing a basis for subsequent experimental expression and functional verification.

[0050] Example 3 Molecular dynamics simulation of mutant affinity

[0051] Using GROMACS molecular dynamics simulation software (version 2023), we imported the energy-minimized wild-type and mutant complex conformations. We chose the OPLS position to describe the interatomic interactions of the protein. We constructed a periodic water box using the SPC / E model, placing the complex at the center of the box and ensuring that the distance between the protein surface and the box boundary was no less than 1 nm. We used the steepest descent method to minimize the energy of the simulation system, iterating until energy convergence to eliminate unreasonable steric hindrance and van der Waals interactions between atoms. Subsequently, we performed a two-step kinetic equilibrium. The first step was a 100 ps NVT equilibrium, where the system temperature was controlled to 300 K to ensure system temperature stability. The second step was a 100 ps NPT equilibrium, maintaining a system pressure of 1 Bar and a temperature of 300 K to bring the system volume and density to equilibrium. The running time was 100 ns.

[0052] Example 4: Optimization and purification of prokaryotic expression conditions for the affinity strain

[0053] 1. Induced expression The bacterial culture, after being cultured overnight, was inoculated at a 1% ratio into 1L of liquid LB medium containing kanamycin (50 μg / mL). The culture was incubated at 37 ℃ and 180 rpm until OD600≈0.6. IPTG was then added to a final concentration of 0.5 mmol / L to induce expression. The culture conditions were adjusted to 18 ℃, 180 rpm, and incubated for 18 h. The bacteria were harvested by centrifugation at 6,000 xg, the supernatant was discarded, and the bacterial cells were resuspended in 30 mL of resuspension buffer. Simultaneously, gradient experiments were designed with different induction conditions, including IPTG concentration, induction time, and induction temperature.

[0054] 2. Purification The bacterial culture was disrupted using an ultrasonic disruptor at 40% power, with a 2-second operation followed by a 3-second pause, for a total of 3 disruptions of 5 minutes each. Once the culture became clear and transparent, it was centrifuged at 13000 rpm for 30 minutes at 4°C. The supernatant was transferred to a gravity column containing Ni²⁺ packing material and incubated on ice with shaking for 30 minutes. The permeate was squeezed out, and the column was washed with 5 column volumes of Western blot (WB). The WB eluent was squeezed out completely, and 15 mL of EB solution containing different imidazole concentrations (200 mmol / L, 500 mmol / L) was added. The liquid was slowly squeezed out and collected, at which point the protein was eluted. The elution solution was concentrated and collected using an ultrafiltration tube, centrifuged at 5500 x g at 4°C until a concentrated volume of 1 mL was obtained. The ATKA program was set, and the chromatography system was equilibrated in equilibration mode using Lysis Buffer at a flow rate of 1 mL / min. Equilibration was complete when all three lines in the system were stable. Set to elution mode, then inject the concentrated protein into the injection port at a flow rate of 1 mL / min, collecting 1 mL per tube. Start collecting when the system shows a peak at UV 280 nm. After SDS-PAGE analysis, concentrate the protein using an ultrafiltration concentrator to approximately 500 μL. Measure the concentration, aliquot, and store at -80 ℃.

[0055] Example 5: Physicochemical characterization of purified affinity protein

[0056] 1. Thermal stability testing of Tycho technology Add 10 μL of the treated protein sample to a Tycho capillary tube. Set the instrument parameters as follows: temperature scan range 30–95 °C, heating rate 3 °C / min, and fluorescence detection wavelengths 330 nm and 350 nm. Use the 350 nm / 330 nm fluorescence ratio as the detection index and record the change curve of the fluorescence ratio with increasing temperature. The temperature at which the ratio shows a significant jump is taken as the protein melting temperature (Tm). Each sample is detected in triplicate, and the average value is taken.

[0057] 2. Secondary structure characterization using circular dichroism (CD) A circular dichroism spectroscopy (CD) instrument was set up for far-ultraviolet (190-260 nm) spectral scanning using 0.5 mm path length quartz cuvettes. Parameters were set as follows: scan speed 50 nm / min, bandwidth 1 nm, response time 1 s. Each sample was diluted to 1 mg / mL, scanned three times, and the average spectrum was taken, with the corresponding buffer blank baseline subtracted. Circular dichroism spectroscopy was performed on proteins in Tris-HCl and PBS buffer systems at 25 °C, analyzing changes in characteristic peaks. A temperature gradient scan was also performed, increasing the temperature from 20 °C to 95 °C, automatically measuring the secondary structure of the protein at 1 °C increments. The final data were processed using CD spectroscopy software to analyze and verify secondary structure changes during protein thermal denaturation. The protein Tm value was calculated based on the characteristic peak signals.

[0058] Example 6: Detection of binding affinity between β-galactosidase and its affinity derivative

[0059] Add 10 µL of 100 µmol / L β-galactosidase protein and 2.5 µL of 1200 µmol / L RED-Cy5 dye to 87.5 µL of buffer, incubate on ice in the dark for 30 min, and then remove excess dye using a B column. Before Kd value determination, dilute the fluorescently labeled protein to a final concentration of 20 nmol / L by adding 297 µL of buffer to a centrifuge tube, followed by 3 µL of 2 µmol / L RED-Cy5-labeled β-galactosidase protein, and mixing well. This operation is performed on ice. Next, perform a serial dilution of the ligand protein as follows: Prepare 16 PCR tubes, and add 10 µL of MST buffer to each tube from 2 to 16. Add 10 µL of the ligand protein to the first and second PCR tubes respectively. After mixing the MST buffer and ligand protein in the second PCR tube, add 10 µL of the mixture to the third PCR tube. Repeat this serial dilution process up to the 16th tube. After serial dilution, add 10 µL of fluorescently labeled β-galactosidase protein to each PCR tube, mix thoroughly, and let stand for 10 min. Use a capillary tube (NT.115.k002) to aspirate the sample, placing the capillary tubes sequentially into the MST instrument (Monolith NT.115 instrument, Nanotemper Technologies) according to the ligand concentration from highest to lowest. Select the Nano-Red channel, the affinity measurement option, the labeled fluorescent molecule concentration of 50 nmol / L, and the initial ligand concentration of 1 mmol / L. After the instrument completes its operation, analyze the data using the instrument's built-in Nanotemper Analysis software, v2.3.

[0060] Example 7 Molecular docking and interaction analysis between β-galactosidase and its affinity

[0061] Using the ZDOCK docking module in Discovery Studio, the interaction between β-galactosidase and candidate affinities was simulated. Through RDock and interface analysis, the binding force and bonding mode between β-galactosidase and affinities were revealed. Finally, the affinity between affinities and β-galactosidase was increased through the mutation module, completing the virtual screening. The aim was to screen for high-affinity ligand molecules that can specifically recognize β-galactosidase, providing key recognition elements for the subsequent construction of a detection platform based on lateral immunofluid chromatography.

[0062] like Figure 1 The results were obtained using Discovery Studio 2019 Client software. First, the software was used to optimize the 3D structure of β-galactosidase and its affinity by hydrogenating the β-galactosidase structure and eliminating inappropriate conformations between atoms, such as water molecules. The 3D structure clearly shows that the enzyme exhibits a typical homotetrameric assembly, with four identical subunits arranged in a highly symmetrical cage-like configuration around a central axis. The secondary structure of each subunit has a core framework of densely packed β-sheets (cyan), with α-helices (red) surrounding the periphery. The core catalytic domain is a typical α / βTIM barrel structure, with the active site located at the C-terminal opening of the barrel structure. The affinity exhibits typical α-helical characteristics, consisting of three α-helices (red) combined together.

[0063] like Figure 2 As shown in (a), the ZDOCK tool of the DS Protein Docking module was used to simulate the docking of the pretreated β-galactosidase and the affinity protein. β-galactosidase was set as the acceptor and the affinity protein as the ligand. The docking was simulated using an Euler angle of 6 degrees. Subsequently, the nucleophile translated and rotated in the space around the β-galactosidase protein to obtain the docking structure. The red ball represents the docking site where the affinity protein is located.

[0064] Cluster analysis of the ZRANK docking results was performed using the ZRANK tool to group structurally similar docking conformations into one class, reducing interference from redundant conformations, quickly identifying the dominant binding mode, and improving the reliability of screening the optimal complex model. For the β-galactosidase and affinity interaction models selected above that meet the modification objectives, the top 30 conformations were ranked, and the docking conformations were re-optimized using the RDOCK method. Binding free energy (ΔG) was calculated using E-Rdock, and conformations with low free energy and meeting the modification objectives were selected, thus identifying the docking conformation with the strongest binding ability. The results were analyzed and summarized (Table 1), revealing that the docking conformation pose184... Figure 2As shown in (b), it has better binding force compared to other docking results. Its surface bonding mode and energy change are relatively high, and hydrogen bonding is the most common. It can form a stable conformation with β-galactosidase protein. Theoretically, its binding ability can be further enhanced by modifying the amino acids at the mutable sites, which is more in line with the design purpose. Therefore, the 184th docking conformation was selected as the most suitable β-galactosidase-affinity complex structure for virtual mutation.

[0065] Table 1. Interaction types and distances between Pose184 affinity and β-galactosidase A:LYS476:HZ1 - A:ASP36:O 2.5099 Hydrogen Bond B:LYS347:HZ2 - A:LYS58:OCT1 2.2221 Hydrogen Bond A:LYS7:HZ1 - C:GLU338:OE1 2.1063 Hydrogen Bond A:ASN21:HD21 - B:GLU338:OE2 2.3563 Hydrogen Bond A:ASN21:HD22 - B:GLU338:OE1 2.4358 Hydrogen Bond A:ASN21:HD22 - B:GLU338:OE2 2.4386 Hydrogen Bond A:ASN43:HD22 - A:GLU529:OE2 2.0648 Hydrogen Bond A:LYS50:HZ1 - A:GLU529:OE1 2.743 Hydrogen Bond A:LYS50:HZ2 - A:GLY528:O 2.1065 Hydrogen Bond A:LYS50:HZ3 - A:GLU529:OE1 2.8648 Hydrogen Bond A:GLY528:CA - A:ALA46:O 2.9598 Hydrogen Bond D:LYS476:CE - A:ASP32:OD2 3.3977 Hydrogen Bond A:ASN23:CA - D:GLU338:OE2 3.0824 Hydrogen Bond A:GLY24:CA - D:ILE337:O 2.9591 Hydrogen Bond A:LYS28:CE - D:ASP497:O 3.5968 Hydrogen Bond A:LYS50:CE - A:PRO527:O 3.2125 Hydrogen Bond A:PRO57:CA - B:ASP233:O 2.8238 Hydrogen Bond

[0066] Table 2. Results of Mutation Site Screening and Simulated Mutation of Affinity Proteins in Example 8

[0067] To improve the affinity of the screened affinity strains, the optimal docking conformation pose184 was selected as the mutation model. Using the Calculate Mutation Energy Binding module, single-point and saturation mutations were performed on 13 amino acid sites in the affinity strains to construct a mutant library, resulting in the mutation schemes shown in Table 2. The mutation results were categorized into three states: stable, intermediate, and unstable. A mutation free energy less than -0.5 indicates a stable state, meaning the mutated affinity strain has a stronger binding affinity to β-galactosidase; a mutation free energy between -0.5 and 0.5 indicates an intermediate state, meaning the binding affinity is comparable to β-galactosidase; and a mutation free energy greater than 0.5 indicates an unstable state, meaning the binding affinity is weaker. Two amino acids with stable mutation states and energy < -0.5 kcal / mol were selected for each site. Subsequently, based on the mutation results of the 13 amino acid sites, novel combinations of mutation sites for affinity strains were designed.

[0068] Table 2 Key amino acid mutation schemes A:LEU9>ARG -1.19 STABILIZING -0.48 -1.85 -0.03 A:LEU9>LYS -0.86 STABILIZING -0.1 -1.58 -0.02 A:GLY10>ARG -3.22 STABILIZING -10.5 0.45 2.26 A:GLY10>PHE -2.17 STABILIZING -6.38 0.87 0.73 A:TRP11>ARG -1.25 STABILIZING -0.37 -2.86 0.45 A:TRP11>LYS -1.11 STABILIZING 0.28 -2.54 0.02 A:THR13>LYS -1.05 STABILIZING -1.39 -1.31 0.37 A:THR13>TRP -0.92 STABILIZING -1.85 0.02 0 A:TRP14>ARG -1.27 STABILIZING -3.98 -0.94 1.49 A:TRP14>LEU -0.55 STABILIZING -2.03 -0.05 0.61 A:PHE17>ARG -2.56 STABILIZING -4.09 -1.81 0.49 A:PHE17>TRP -1.57 STABILIZING -3.67 0.19 0.21 A:ASN18>LYS -1.97 STABILIZING -0.88 -3.13 0.04 A:ASN18>TRP -1.43 STABILIZING -2.09 -1.3 0.33 A:VAL25>ARG -3.29 STABILIZING -7.53 -2 1.85 A:VAL25>TYR -3.13 STABILIZING -7.05 -0.05 0.53 A:VAL27>TRP -2.79 STABILIZING -5.4 0.27 -0.28 A:VAL27>LYS -2.72 STABILIZING -5.04 -0.37 -0.02 Affinity

[0069] Saturation mutations were performed on amino acid sites 9, 10, 11, 13, 14, 17, 18, 25, and 27 according to the above mutation directions. Based on the mutational free energy, three mutants with the strongest binding affinity to β-galactosidase were screened. The mutation sites and mutated amino acids of the three mutants are shown in Tables 3 and 4. The screened mutants showed strong binding affinity to β-galactosidase and stable overall structure; therefore, model construction and validation were performed on the three mutants (named affinity A1, affinity A2, and affinity A3, respectively). Figure 3 As shown, the mutated affinity molecules retain their original native conformation.

[0070] Table 3 Key amino acid mutation schemes Mutation 1 LEU9>ARG, GLY10>ARG, TRP11>LYS, THR13>TRP, TRP14>ARG, PHE17>ARG, ASN18>TRP, VAL25>TYR, VAL27>TRP -18.03 STABILIZING Mutation 2 LEU9>ARG, GLY10>ARG, TRP11>ARG, THR13>LYS, TRP14>LEU, PHE17>ARG, ASN18>LYS, VAL25>TYR, VAL27>TRP -17.76 STABILIZING Mutation 3 LEU9>LYS, GLY10>ARG, TRP11>ARG, THR13>TRP, TRP14>ARG, PHE17>TRP, ASN18>LYS, VAL25>TYR, VAL27>TRP -17.33 STABILIZING

[0071] Table 4 Key amino acid mutation schemes Mutation1 VDNKFNKERRKAWREIRWLPNLNGYQWKAFIDSLRDDPSQSANLLAEAKKLNDAQAPK Mutation2 VDNKFNKERRRAKLEIRKLPNLNGYQWKAFIDSLRDDPSQSANLLAEAKKLNDAQAPK Mutation3 VDNKFNKEKRRAWREIWKLPNLNGYQWKAFIDSLRDDPSQSANLLAEAKKLNDAQAPK

[0072] Example 9: Stability and interfacial interaction analysis of affinity compounds in molecular dynamics To evaluate the conformational stability and interaction mechanism of the affinity complex obtained by molecular docking with β-galactosidase, this study conducted a 100 ns all-atom molecular dynamics (MD) simulation. Key kinetic parameters, such as root mean square deviation (RMSD), were calculated to quantitatively describe the degree of conformational fluctuation of the complex during the dynamic process. Figure 4 (a) to Figure 4 As shown in (c), at the end of the simulation, the three-dimensional structures of each complex did not deviate significantly from the initial conformation, and the affinities did not dissociate from the β-galactosidase surface, indicating that the three designed candidate affinities could maintain a stable binding state. The RMSD quantitative analysis results are as follows: Figure 4 As shown in (d), it is further demonstrated that the RMSD trajectories of each affinity complex (affinity A1, affinity A2, and affinity A3) throughout the simulation process all showed a trend of rapid increase followed by stabilization, eventually stabilizing at around 0.3 nm, with fluctuations controlled within 0.1 nm, confirming that the system had reached a sufficient equilibrium state in the later stage of the simulation; and the equilibrium RMSD values ​​of the three complexes were 0.1544 nm, 0.1662 nm, and 0.1665 nm, respectively, with good overall conformational stability, consistent with the structural characteristics of the affinity-target enzyme complex.

[0073] To further analyze the molecular binding mechanisms of each affinity, this study used PyMOL to perform binding interface analysis on the equilibrium conformations after the simulation, such as... Figure 4 (a) to Figure 4 As shown in (c), compared to the initial docking model, the simulated complex exhibits a more compact and specific interaction network, mainly manifested in the strengthening of the hydrogen bond network, the optimization of hydrophobic interactions, and the synergistic enhancement of electrostatic interactions. Specifically, in Figure 4In the complex structure shown in (a), the stability of the binding interface mainly depends on the synergistic effect of multiple hydrogen bonds: the affinity group Lys11 forms stable hydrogen bonds with the target enzyme Glu580, Arg35 with Asp82, and Glu117 with Asn23, respectively. Simultaneously, hydrophobic residues such as Trp27 and Tyr95 form hydrophobic clusters in the core region of the interface, effectively reducing solvent accessibility and collectively maintaining the conformational stability of the complex. Figure 4 In the complex shown in (b), a dense network of polar interactions forms at the binding interface: hydrogen bonds are formed between multiple residue pairs, such as Arg35 and Asp36, Asp32 and Gln969, and Ser971 and His972; simultaneously, positively charged residues such as Lys13 and Lys811 form electrostatic interactions with the acidic amino acids of the target enzyme, further enhancing the binding force at the interface. Figure 4 In the complex shown in (c), the stability of the binding interface depends on the synergistic effect of hydrogen bonding and hydrophobic interactions: the affinity Arg11 forms a key hydrogen bond with the target enzyme Glu580, while residues such as Arg10 and Lys7 form secondary interactions with surrounding polar amino acids. Simultaneously, hydrophobic residues construct a stable hydrophobic core at the interface, giving the entire binding interface both rigidity and conformational flexibility, ensuring the specificity and stability of the binding. In summary, the three affinities achieve stable binding with β-galactosidase through different molecular interaction modes, and the binding epitopes differ. Despite the different mechanisms of action, all three affinities exhibit excellent interfacial complementarity and conformational stability, validating the effectiveness of the rational design strategy of the affinities in this study and laying a theoretical foundation for subsequent experimental verification.

[0074] Example 10 Affinity protein induction and purification

[0075] The candidate affinity sequences screened and validated through molecular simulations were codon-optimized to construct a recombinant expression vector, which was then transformed into Rosetta (DE3) host bacteria to construct an engineered strain. High-purity affinity protein was obtained through induced expression and purification for subsequent experimental validation. To obtain sufficient soluble affinity protein, key expression conditions such as induction temperature, induction time, and IPTG concentration were systematically optimized.

[0076] like Figure 5 As shown in (a), under the conditions of 18 °C and 18 h of induction, the expression band of affinity protein A1 showed a relatively shallow change (approximately 8 kDa) and low expression level as the IPTG concentration increased from 0.2 mmol / L to 0.8 mmol / L, indicating unsuccessful expression. In contrast, the expression optimization results of affinity A2 were more ideal. In the IPTG concentration gradient experiment, as shown... Figure 5As shown in (b), when the IPTG concentration was increased from 0.2 mmol / L to 0.8 mmol / L, the target band brightness was significantly increased, but there was no obvious trend of enhanced expression, indicating that the IPTG concentration did not affect the protein expression level. From an economic perspective, 0.2 mmol / L is the optimal induction concentration. In the induction time gradient experiment, as shown... Figure 5 As shown in (c), the expression level of the target protein gradually increased with the extension of induction time from 14 h to 21 h, and the band brightness reached its peak at 18 h. Further investigation into the effect of induction temperature is as follows... Figure 5 As shown in (d), when the temperature increased from 16℃ to 20℃, the expression level of the target protein increased significantly. However, when the temperature continued to increase to 24℃, the band brightness did not increase significantly, but instead showed a certain degree of diffusion and extraneous bands.

[0077] Based on the above results, the optimal induction conditions for affinity A2 are: IPTG concentration of 0.2 mmol / L, induction temperature of 18 ℃, and induction time of 18 h. Under these conditions, a high expression level of the target protein can be obtained, providing a reliable basis for subsequent purification and functional verification. Affinity A1, however, failed to achieve effective expression in the expression system of this study. Further attempts could be made by changing the expression host or optimizing the codons.

[0078] Using the same method to optimize the soluble expression of affinity A3, this study further screened multi-dimensional induction conditions based on the pET-28a (+) E. coli expression system to directionally increase its soluble protein yield. As mentioned above, multiple variable gradients were set for induction expression, including: host bacterial type; IPTG induction concentration: 0.2, 0.4, 0.6, 0.8 mmol / L; induction time: 14, 16, 18, 20 h; and induction temperature: 16, 18, 20, 24 ℃. The results showed... Figure 6 As shown, Rosetta (DE3) was selected as the host bacterium to supplement rare codon tRNAs, eliminating translation elongation barriers and increasing the soluble concentration by 40% compared to BL21 (DE3). The IPTG concentration was determined to be 0.2 mmol / L, chosen for economic reasons as increasing IPTG concentration did not significantly increase expression levels. The induction time was set at 18 h to balance protein accumulation and degradation risk. The induction temperature was selected at 18 ℃ to simultaneously ensure bacterial metabolic activity and correct polypeptide chain folding. Therefore, the optimal induction conditions were: IPTG concentration 0.2 mmol / L, induction temperature 18 ℃, and induction time 18 h.

[0079] To further obtain high-purity affinity proteins, this study employed a two-step purification strategy combining nickel column affinity chromatography and gel filtration chromatography. The results of the nickel column purification show… Figure 7 (a) Figure 7 As shown in (d), imidazole at a concentration of 100 mmol / L effectively removed impurity bands. As the imidazole concentration increased to 200-500 mmol / L, the target band was eluted and highly concentrated. The eluent containing the target protein was concentrated to 1 mL using an ultrafiltration tube and further purified by gel filtration chromatography. Figure 7 (b) Figure 7 As shown in (e), a UV absorption peak appears around 40 mL as the buffer is eluted, corresponding to the elution of a large amount of monomeric protein. Corresponding to the SDS-PAGE results, the samples corresponding to the single and uniform bands are concentrated and collected. The final concentration results of the two affinity proteins are shown below. Figure 7 (c) Figure 7 As shown in (f), high-purity protein was obtained.

[0080] Example 11 Thermal stability analysis of affinity proteins

[0081] To evaluate the physicochemical properties of the purified affinity proteins and better adapt them to the application requirements of LFIA recognition elements, this study used Tycho technology to perform thermal denaturation analysis on affinity proteins A2 and A3. The melting temperature (Tm) of the protein was calculated by monitoring the change in the 350 nm / 330 nm fluorescence ratio with increasing temperature.

[0082] like Figure 8 As shown in (a), in the Tris-HCl buffer system, the fluorescence ratio of affinity A2 significantly increased at 65.1 °C, indicating irreversible protein denaturation; while in the PBS buffer system, the Tm value increased to 67.6 °C. Thermostability curves of affinity A. Figure 8 (b) shows that the Tm value is 66.1 °C in the Tris-HCl buffer system and increases to 68 °C in the PBS buffer system. Overall, the Tm values ​​of both affinities are above 65 °C, indicating good thermal stability and the ability to maintain structural integrity at typical experimental temperatures. The buffer system has a certain impact on the thermal stability of the affinities; therefore, PBS can be chosen as the reaction buffer system in subsequent experiments.

[0083] Example 12 Spatial structure characterization of affinity proteins based on circular dichroism chromatography

[0084] To investigate the secondary structure characteristics of the affinity compounds in different buffer systems, this study used circular dichroism spectroscopy to characterize the structures of affinity compounds A2 and A3 and analyzed their spectral characteristics in the far ultraviolet region (190–260 nm).

[0085] like Figure 9As shown, both affinity proteins A2 and A3 exhibit typical CD spectral characteristics of α-helical proteins in both Tris-HCl and PBS buffer systems, showing a strong positive absorption peak at 190–200 nm and characteristic double negative absorption peaks near 208 nm and 222 nm. This is highly consistent with the circular dichroism spectral characteristics of α-helical structures. Comparing the two buffer systems, the positive peak intensity at 190–200 nm in the PBS group is slightly higher than that in the Tris-HCl group, while the positions and intensities of the negative peaks at 208 nm and 222 nm are basically consistent. This indicates that the buffer system did not significantly affect the overall secondary structure of affinity protein A2, but only slightly perturbed the local conformation, and the protein still uses α-helices as the main structural element.

[0086] In summary, the PBS buffer system is better than the Tris-HCl system, which is consistent with the results of the thermal stability experiment, providing a structurally stable protein sample guarantee for subsequent binding affinity detection.

[0087] Example 13: Binding affinity analysis of β-galactosidase and its affinity derivative

[0088] To investigate the interaction between β-galactosidase and its affinity and the conformational changes induced by binding, this study used circular dichroism spectroscopy to detect changes in the secondary structure of the complex at different affinity concentration gradients, thereby reflecting differences in binding affinity.

[0089] like Figure 10 As shown, with the concentrations of affinity A2 and affinity A3 increasing from 30 μmol / L to 120 μmol / L, the intensities of the negative absorption peaks of the complex at 208 nm and 222 nm gradually increased, and the peak positions did not shift significantly. This result indicates that the binding of the affinity to β-galactosidase does not alter the α-helical core structure of the protein, but rather enhances the orderliness of the secondary structure in a concentration-dependent manner. Furthermore, the binding strength gradually increases with increasing affinity concentration, indicating the presence of a binding reaction. Overall, both affinity molecules can specifically bind to β-galactosidase, and circular dichroism analysis suggests that the binding strength is around 100 μmol / L.

[0090] To quantitatively assess the binding affinity of the affinity mutant to β-galactosidase, microthermophoresis (MST) was used to measure the changes in fluorescence normalized value (FNorm) after different concentrations of the affinity mutant bound to the target enzyme, and the dissociation constant (Kd) was calculated by fitting the curve.

[0091] like Figure 11As shown in (a), as the concentration of the affinity compound gradually increases from 10⁻⁸ mol / L to 10⁻³ mol / L, the fluorescence normalization value remains stable in the low concentration range, but begins to rise significantly when the concentration exceeds 10⁻⁵ mol / L, exhibiting a typical saturation binding curve. Fitting calculations show that the dissociation constant Kd between affinity compound A2 and β-galactosidase is 131 μmol / L, indicating a moderate level of specific binding between the two. The MST curve of affinity compound A3 is shown below. Figure 11 As shown in (b), consistent with the trend of affinity A2, the fluorescence signal is stable in the low concentration range, and rises rapidly and tends to saturate when the concentration exceeds 10⁻⁴ mol / L. The fitting results show that the dissociation constant Kd of affinity A3 with β-galactosidase is 87 μmol / L, which is significantly lower than that of affinity A2, indicating that affinity A3 has a stronger binding affinity to the target enzyme.

[0092] Example 14 Binding analysis of the affinity compound to the control target glucosidase

[0093] Using the affinity of β-galactosidase, a comparative analysis of glucosidase was performed. The detection results of affinity A2 not binding to glucosidase are as follows: Figure 12 As shown, the detection results for affinity A3 not binding to glucosidase are shown in the figure. Figure 13 As shown, the experimental results indicate that neither affinity derivative A2 nor affinity derivative A3 specifically binds to glucosidase.

[0094] This invention, targeting galactosidase, completed the entire process of molecular design, prokaryotic expression preparation, physicochemical characterization, and binding affinity verification of a high-affinity affinity compound, laying a core experimental foundation for the subsequent construction of an affinity-based intestinal disease detection platform. In the molecular design stage, using the crystal structures of β-galactosidase (PDBID: 1DP0) and its affinity compound (PDBID: 2B88) from the PDB database as templates, the target and ligand docking simulation was performed using ZDOCK and RDOCK molecular docking techniques after structural optimization. Pose184, with the lowest binding free energy (ΔG), was selected as the optimal complex conformation, and the key hydrogen bond and salt bridge interaction sites at the binding interface were identified. Based on this conformation, single-point and saturation mutations were performed on 13 mutagenic amino acid sites of the affinity. Using mutational free energy as the core screening criterion, a virtual mutant library was constructed, and three candidate affinity strains were obtained. Affinities A1, A2, and A3 had total mutational free energies of -18.03 kcal / mol, -17.76 kcal / mol, and -17.33 kcal / mol, respectively, all exhibiting stable binding effects. The amino acid sequence modification and model construction of the affinity strains were also completed. Molecular dynamics simulations were used to verify the stability of the three affinity strains. A 100 ns free energy simulation was completed at 300 K and 1 Bar, confirming the conformational stability of the complex formed by the affinity strain and β-galactosidase. The affinity sequence was subsequently cloned into the pET-28a(+) vector and transformed into an *E. coli* expression system. Key conditions such as IPTG concentration, induction temperature, and induction time were optimized. Results showed that affinity A1 failed to achieve effective expression in this system. The optimal expression conditions for affinity A2 and A3 were IPTG 0.2 mmol / L, induction temperature 18 ℃, and induction time 18 h, under which high levels of soluble target protein could be obtained. A two-step purification strategy using nickel column affinity chromatography and gel filtration chromatography was employed to separate and purify affinity A2 and A3, ultimately yielding a high-purity protein sample with a single band, meeting the requirements for subsequent functional validation. The physicochemical properties of the purified protein were characterized using Tycho spectroscopy and circular dichroism (CD). The results showed that the melting temperatures of both affinity derivatives A2 and A3 were above 65 °C, and both exhibited good thermal stability in Tris-HCl and PBS buffer systems, with the PBS buffer system being more conducive to maintaining protein structural stability. Both derivatives exhibited typical α-helical secondary structure characteristics, and changes in the buffer system only slightly disturbed the local conformation of the protein without altering the overall secondary structure, confirming that the protein could maintain structural integrity under experimental conditions.The binding affinity between the affinity derivative and β-galactosidase was verified by combining circular dichroism spectroscopy (CDS) and micro-thermophoresis (MST). CDS results showed that the intensity of the characteristic negative absorption peaks of the complex at 208 nm and 222 nm increased in a concentration-dependent manner with increasing affinity derivative concentration, and the peak positions did not shift, indicating that the two can specifically bind without altering the core structure of the target enzyme. MST quantitative detection results showed that the dissociation constant Kd between affinity derivative A2 and the target enzyme was 131 μmol / L, while that of affinity derivative A3 was 87 μmol / L, significantly lower than that of affinity derivative A2. This confirms that affinity derivative A3 has a stronger binding affinity to β-galactosidase and can be used as a preferred ligand for the construction of subsequent detection platforms.

[0095] This invention successfully obtained three affinity molecules that bind with high affinity to β-galactosidase by combining molecular simulation and experimental verification. It clarified the key conditions for their efficient expression and purification, verified their good structural stability and target binding specificity, provided a reference technical method for the design and preparation of other enzyme affinity molecules, and also provided a core biorecognition element for the construction of an affinity-based lateral immunochromatographic detection platform.

[0096] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for obtaining a galactosidase affinity, characterized in that, Includes the following steps: Based on the modified structure of the B domain of Staphylococcus A protein – the crystal structure of the Z domain affinity, saturation mutations were performed on 13 sites on the amino acid sequence of the Z domain affinity (7 sites on helix 1: Phe5, Gln9, Asn10, Phe13, Leu14, Tyr17, His18; 6 sites on helix 2: Asn23, Gln24, Asn27, Ala28, Ser31, Lys35; numbering based on the mature Z domain sequence, removing the position after the initiation Met). Using molecular dynamics verification, all the selected Z domain affinity mutants were re-docked with the galactosidase target protein, and static simulations were performed at 300 K and 1 Bar. Using a molecular docking software module, the pretreated affinity mutant was used as a ligand to perform a fully flexible docking mode on galactosidase, allowing the ligand to sample conformations within the active pocket. The Euler angle was set to 6 degrees to sample the binding conformations. Finally, all binding conformations of the samples were docked, and the top 2000 docking conformations with the best binding ability were output. Then, based on the docking binding energy and the global binding mode, the top 10 complex conformations with the lowest binding free energy (ΔG) and the most stable conformations were selected. Molecular dynamics simulation software and force field analysis software were used to describe the interatomic interactions of proteins. A periodic water box was constructed using relevant models, and the complex was placed at the center of the box, ensuring that the distance between the protein surface and the box boundary was not less than 1 nm. The steepest descent method was used to minimize the energy of the simulation system, and the iteration was continued until energy convergence was achieved, eliminating unreasonable steric hindrance and van der Waals interactions between atoms. Subsequently, a two-step kinetic equilibrium was performed. Finally, saturation mutations were performed on 13 amino acid sites on the Z-domain affinity to construct a virtual peptide library. The mutant amino acid sequences were then screened for expression, purification, and experimental data. The affinity series expressed and validated using genetic engineering technology is first synthesized according to the amino acid sequence of the affinity to be expressed (codon optimization is performed according to the expression host), cloned into the expression vector and transformed into the host cell. After bacterial amplification and target protein induction expression, the functional protein is isolated and purified from the host cell and its function is validated.

2. The affinity series screened by the galactosidase affinity screening method according to claim 1, wherein the amino acid sequence shown in any one of SEQ ID NO: 2-4 has more than 80% identity; preferably, it is shown in any one of SEQ ID NO: 2-4.

3. A nucleic acid molecule encoding the affinity of claim 2; preferably, the nucleotide sequence of the nucleic acid molecule is shown in any one of SEQ ID NO: 6-8.

4. The application of the affinity sequences of claim 2 and claim 3, and their compositions, in the preparation of galactosidase, and in the recognition and detection of galactosidase.

Images