Modular orthogonal eukaryotic transcription factor model construction method

Abstract

The present invention provides a modular orthogonal eukaryotic transcription factor model construction method, comprising the following steps: S1, establishing the basic architecture DBD-LBD-AD of a transcription factor; S2, constructing a domain screening vector and system to screen effective DBD, LBD and AD; S3, screening out corresponding parameters of each DBD and LBD domain; S4, on the basis of the corresponding parameters of each DBD and LBD domain, characterizing a transcription factor regulation process, and establishing a quantitative model of transcription regulation; predicting the transcription factor under conditions of different DBD and LBD combinations by means of the quantitative model of transcription regulation, to obtain a transcription factor having optimized performance. The modular orthogonal eukaryotic transcription factor model construction method of the present invention, by means of determining the basic architecture of a modular transcription factor, ultimately establishes a quantitative model of transcription regulation, achieving modular domain parameter optimization of the transcription factor, and obtaining a transcription factor having optimized performance.

Description

A modular orthogonal eukaryotic transcription factor model construction method Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a method for constructing a modular orthogonal eukaryotic transcription factor model. Background Art

[0002] Cells can receive external chemical or physical signals and convert them into biological signals, thereby responding to the environment. Transcription factors can act as a signal converter, by recognizing and binding to specific sites on DNA, called operator sequences, affecting the probability of RNA polymerase binding to the promoter, thereby affecting gene transcription. After sensing external chemical or physical signal stimulation, transcription factors undergo a series of structural changes to produce different operator sequence binding affinities, bind to the promoter or fall off the promoter, thereby affecting the transcription and expression of downstream genes. Transcription factors that bind to DNA must have three characteristics: ① ability to bind to specific DNA sequences; ② ability to respond to signals; ③ ability to control transcription (Schleif, RF, Modulation of DNA binding by gene-specific transcription factors. Biochemistry, 2013. 52(39): p.6755-65.). These characteristics can be respectively implemented by different structural and functional modular domains. According to the functions they perform, transcription factors are composed of DNA binding domains, ligand binding domains, and transcription activation domains. Each domain can be described by several reaction equations when performing its function. Each reaction equation can be abstracted into a parameter—the reaction equilibrium constant. Therefore, in our Saccharomyces cerevisiae host, a specific sequence domain can be abstracted into a specific parameter. Different domains with different parameters together constitute a complete transcription factor with specific transcriptional regulatory behavior. The behavior of the transcription factor in a specific transcription system is described by the reaction equation.

[0003] Currently, the transcription factors commonly used in eukaryotic cell transcription regulation can be divided into two categories according to their sources: prokaryotic natural host transcription factors and artificially synthesized transcription factors. LacI, TetR and XylR from prokaryotes are induced by IPTG, aTc and xylose respectively and applied to eukaryotic cells (Chen, Y., et al., Genetic circuit design automation for yeast. Nature Microbiology, 2020.5(11): p.1349-1360.). Artificially synthesized transcription factors often use zinc fingers, TALEs and CRISPR-dCas9 as customized DNA binding modules, and connect functional domains such as ligand binding domains or transcription activation domains for construction [9]. For example, Khalil established a series of DBD-operator systems based on three zinc finger structures binding to 9bp DNA (Khalil, AS, et al., A synthetic biology framework for programming eukaryotic transcription functions. Cell, 2012.150(3): p.647-58.). In addition, the modular DBD, LBD, and AD were assembled separately to form the artificial transcription factor XEV, lexA-ER-VP16, which is commonly used in eukaryotic systems (Louvion, JF, B. Havaux-Copf, and D. Picard, Fusion of GAL4-VP16 to a steroid-binding domain provides a tool for gratuitous induction of galactose-responsive genes in yeast. Gene, 1993. 131(1): p. 129-34.).

[0004] The use of multiple different induction systems in complex gene circuits requires multiple pairs of transcription factors that are mutually orthogonal in both receiving induction signals and binding to DNA sequences. Existing eukaryotic transcription factors struggle to meet this requirement. The main reasons are: first, the number of well-characterized and commonly used eukaryotic transcription systems is insufficient, and their strength is insufficient, requiring optimization through the use of multiple operators or increased synergistic interactions; second, in most existing eukaryotic transcription regulatory systems, transcription factors act as a whole, resulting in high expression due to background leakage, insufficient induction strength, and poor dynamic range; third, crosstalk occurs between eukaryotic transcription systems or with existing host systems. Therefore, to address these issues, it is necessary to develop a set of universal, designable, orthogonal eukaryotic transcription systems.

[0005] The optimization of the transcriptional regulation process can be divided into two aspects: the optimization of the performance parameters of the transcription factor itself and the optimization of the transcriptional regulation mode. Khalil et al. proposed a series of mutually orthogonal zinc finger libraries with different regulatory activities corresponding to different transcription factor binding affinity parameters for DNA. By using high-affinity zinc finger DNA binding modules, the performance of the transcription factor itself can be optimized (Khalil, AS, et al., A synthetic biology framework for programming eukaryotic transcription functions. Cell, 2012. 150(3): p. 647-58.). In addition, by using multiple operators to increase the potential Hill coefficient, the output promoter using 8 operators can achieve a regulation factor of ~60 times, which is ~10 times that of a single operator promoter (Khalil, AS, et al., A synthetic biology framework for programming eukaryotic transcription functions. Cell, 2012. 150(3): p. 647-58.). In addition to optimizing the transcriptional regulation process of a single transcription factor, changing the transcriptional regulation mode also provides an optimization strategy. In 2019 and 2023, Khalil et al. also increased the cooperativity of transcriptional regulation by increasing the expression of a "scaffold" that binds multiple zinc finger transcription activators, thereby optimizing transcriptional regulation under the condition of low affinity of a single transcription factor (Bashor, CJ, et al., Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies. Science, 2019.364(6440): p.593-597.)(Bragdon, MDJ, et al., Cooperative assembly confers regulatory specificity and long-term genetic circuit stability. Cell, 2023.186(18): p.3810-3825.e18.).In 2018, Rob Phillips et al. used the Monod-Wyman-Changeux model to quantitatively describe the changes in transcription factor concentration, inducer concentration and transcription factor-operator binding energy during the allosteric regulation of transcription factors. However, their allosteric system can only adjust the transcription factor-operator binding energy through mutations, and cannot achieve modularization of domains and effective replacement optimization (Razo-Mejia M, Barnes SL, Belliveau NM, et al. Tuning Transcriptional Regulation through Signaling: A Predictive Theory of Allosteric Induction. 2017[2023-11-10]. DOI: 10.1016 / j.cels.2018.02.004).

[0006] Among the existing eukaryotic transcription regulatory coefficients, transcription factors mostly act in a holistic form, with a poor regulatory range, high background leakage and low induction strength; there is a lack of orthogonality considerations required for multi-induction systems, and there are no multiple pairs of transcription factors with high induction ranges that exist simultaneously and do not interact with each other or the host; the existing model is based on a conformational regulatory system, which cannot achieve modularity and effective replacement of structural domains, and has a narrow range of use.

[0007] Summary of the Invention

[0008] The present invention provides a modular orthogonal eukaryotic transcription factor model construction method to solve the shortcomings of the prior art that eukaryotic transcription regulation is based on conformational regulation, cannot achieve modularization and effective replacement of domains, and has a narrow range of applications.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A method for constructing a modular orthogonal eukaryotic transcription factor model comprises the following steps:

[0011] S1. Establish the basic structure of transcription factors consisting of three domains: DNA binding domain (DBD), ligand binding domain (LBD), and transcription activation domain (AD): DBD-LBD-AD;

[0012] S2. Based on the basic transcription factor architecture DBD-LBD-AD, construct three domains of screening Saccharomyces cerevisiae strains: DNA binding domain (DBD), ligand binding domain (LBD), and transcription activation domain (AD). Then, screen and test the screening Saccharomyces cerevisiae strains to obtain effective DBD, LBD, and AD.

[0013] S3. Based on the basic transcription factor architecture DBD-LBD-AD, quantitative test Saccharomyces cerevisiae strains for quantitative testing of domain parameters were constructed using the screened DBD and LBD. The quantitative test Saccharomyces cerevisiae strains were cultured to obtain the regulatory induction range and regulation fold under the expression level of the transcription factor. The obtained regulatory induction range and regulation fold data were fitted to obtain the corresponding parameters of each of the screened DBD and LBD domains.

[0014] S4. Characterize the transcription factor regulation process based on the corresponding parameters of each DBD and LBD domain and establish a quantitative model of transcription regulation. Use the quantitative model of transcription regulation to predict transcription factors under different DBD and LBD combinations to obtain transcription factors with optimized performance.

[0015] In the present invention, the basic structure DBD-LBD-AD uses 1-87aa of the E. coli SOS regulatory protein lexA as DBD, 1-179aa of the Rhodopseudomonas palustris quorum sensing transcription factor RpaR as LBD, and VP16 as AD.

[0016] In the present invention, a screening yeast strain for the DNA binding domain (DBD) in S2 is obtained by the following method: constructing a DBD screening transcription factor expression vector, constructing a corresponding reporter gene expression vector, enzymatically cutting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain a DBD screening yeast strain.

[0017] Furthermore, the DBD screening transcription factor expression vector is obtained by using aTc-induced ptet as a promoter, tENO2 as a terminator, and replacing the DBD domain in the basic transcription factor structure DBD-LBD-AD with CCDB.

[0018] Furthermore, the DBD screening transcription factor expression vector sequence is as SEQ ID NO: 1.

[0019] Furthermore, a DBD-corresponding reporter gene expression vector was constructed in S2, and the corresponding reporter gene was controlled by a promoter containing two corresponding DBD-regulated operators to control the expression of yellow fluorescent protein YFP, with tENO2 as a terminator; CCDB was placed at the promoter position of the reporter gene plasmid.

[0020] In the present invention, the screening yielded effective DBD sequences such as SEQ ID NOs: 7-18.

[0021] In the present invention, a screening Saccharomyces cerevisiae strain for the ligand binding domain (LBD) in S2 is obtained by the following method: constructing a LBD screening transcription factor expression vector, constructing a corresponding reporter gene expression vector, enzymatically cutting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain an LBD screening Saccharomyces cerevisiae strain.

[0022] Furthermore, the LBD screening transcription factor expression vector is obtained by using aTc-induced ptet as a promoter, tENO2 as a terminator, and replacing the LBD domain in the basic transcription factor structure DBD-LBD-AD with CCDB.

[0023] Furthermore, the sequence of the LBD screening transcription factor expression vector is as shown in SEQ ID NO: 2.

[0024] Furthermore, the reporter gene corresponding to the LBD-corresponding reporter gene expression vector constructed in S2 uses a promoter containing two corresponding DBD-regulated operators to control the expression of yellow fluorescent protein YFP, with tENO2 as the terminator.

[0025] In the present invention, the screening yielded effective LBD sequences such as SEQ ID NOs: 19-31.

[0026] In the present invention, a screening Saccharomyces cerevisiae strain for the transcriptional activation domain (AD) in S2 is obtained by the following method: constructing an AD screening transcription factor expression vector, constructing a corresponding reporter gene expression vector, enzymatically cutting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain an AD screening Saccharomyces cerevisiae strain.

[0027] Furthermore, the AD screening transcription factor expression vector is obtained by using aTc-induced ptet as a promoter, tENO2 as a terminator, and replacing the AD domain in the basic transcription factor structure DBD-LBD-AD with CCDB.

[0028] Furthermore, the sequence of the AD screening transcription factor expression vector is as shown in SEQ ID NO: 3.

[0029] Furthermore, the reporter gene corresponding to the AD reporter gene expression vector constructed in S2 uses a promoter containing two corresponding DBD-regulated operators to control the expression of yellow fluorescent protein YFP, with tENO2 as the terminator.

[0030] In the present invention, the screening yielded effective AD sequences such as SEQ ID NOs: 32-40.

[0031] In the present invention, the specific process of screening test culture is as follows: the constructed screening Saccharomyces cerevisiae strain is inoculated into the corresponding defective SD culture medium and cultured for a period of time, then transferred to a new SD culture medium, a control group and different experimental groups are set up, and the culture is continued. The bacterial liquid is taken and diluted, and the expression level of the fluorescent protein YFP in each group is detected, and the relative fluorescence expression level is calculated.

[0032] In the present invention, a Saccharomyces cerevisiae strain for quantitative testing of the DNA binding domain (DBD) in S3 is obtained by the following method: constructing a transcription factor expression vector for quantitative testing of DBD parameters, then constructing a corresponding reporter gene vector, enzymatically cutting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain a Saccharomyces cerevisiae strain for quantitative testing of DBD.

[0033] Furthermore, the DBD parameter quantitative test transcription factor expression vector is obtained by using xylose-induced pxyluas as a promoter, tENO2 as a terminator, and replacing the DBD domain with CCDB.

[0034] Furthermore, the DBD parameter quantitative test transcription factor expression vector sequence is such as SEQ ID NO: 4.

[0035] Furthermore, the reporter gene corresponding to the DBD reporter gene expression vector constructed in S3 uses a promoter containing an operator corresponding to DBD regulation to control the expression of yellow fluorescent protein YFP, with tENO2 as a terminator; CCDB is placed at the promoter position of the reporter gene plasmid.

[0036] In the present invention, a Saccharomyces cerevisiae strain for quantitative testing of the ligand binding domain (LBD) in S3 is obtained by the following method: constructing a transcription factor expression vector for quantitative testing of LBD parameters, then constructing a corresponding reporter gene vector, enzymatically cutting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain a Saccharomyces cerevisiae strain for quantitative testing of LBD.

[0037] Furthermore, the parameter quantitative test of LBD is obtained by using a transcription factor expression vector with xylose-induced pxyluas as a promoter, tENO2 as a terminator, and CCDB to replace the LBD domain.

[0038] Furthermore, the LBD parameter quantitative test transcription factor expression vector is such as SEQ ID NO: 5.

[0039] Furthermore, the reporter gene corresponding to the LBD reporter gene expression vector constructed in S3 uses a promoter containing an operator corresponding to DBD regulation to control the expression of yellow fluorescent protein YFP, with tENO2 as the terminator.

[0040] In the present invention, the specific process of quantitative test culture is as follows: the constructed screening Saccharomyces cerevisiae strain is inoculated into the corresponding defective SD culture medium and cultured for a period of time, transferred to a new SD culture medium for the first time to perform an induction range test, set different xylose concentration gradients to obtain different transcription factor expression levels, set two groups of inducers with ±maximum working concentrations at different xylose concentrations, and obtain the regulatory induction range under each transcription factor expression level; select the transcription factor expression level with a suitable induction range, test the induction curve under different inducer concentration gradients, continue to culture in the SD culture medium containing the corresponding xylose concentration for a period of time, set a control group and different experimental groups, transfer to the SD culture medium containing different inducer concentrations at the corresponding xylose concentration for a second time, take the bacterial solution, dilute it, detect the expression level of fluorescent protein YFP in each group, and calculate the relative fluorescence expression level and the regulation multiple of the transcription factor.

[0041] Furthermore, the relative fluorescence expression of each experimental group was calculated as follows: RPU = (YFP 测试 -YFP CYE72 ) / (YFP CYE72 / CY637 -YFP CYE72 ), CYE72 was a negative control, and CYE72 / CY637 was a ptet-induced control.

[0042] The calculation method for the regulation fold of transcription factors is: Fold Change = RPU 诱导后 / RPU 诱导前 .

[0043] In the present invention, the quantitative transcriptional regulation model includes a non-nuclear receptor dimerization transcriptional activation model (not considering the nuclear entry process) and a nuclear receptor dimerization transcriptional activation model (considering the nuclear entry process);

[0044] The model equation for transcriptional activation by dimerization of non-nuclear receptors is as follows:

[0045] The model equation for nuclear receptor dimerization transcription activation is as follows:

[0046] Among them, F1 factor is a parameter that describes the performance of transcription factors under a specific DBD-LBD combination; c is the concentration of LBD in different forms, I0 is the background leakage expression intensity of the promoter of a specific sequence, and Imax is the theoretical maximum activation strength of a specific sequence promoter, I is the inducer concentration, L tot is the sum of the expression levels of all forms of the transcription factor monomer, Δε DBD Describes the behavior of DBD, K1, K2, and K3 describe the behavior of LBD, and K* and K*' describe the nuclear entry process of transcription factors.

[0047] The present invention has the following beneficial effects:

[0048] (1) The modular orthogonal eukaryotic transcription factor model construction method of the present invention determines the basic architecture of the modular transcription factor, ultimately establishes a quantitative model of transcription regulation, and parameterizes the modular structural domain of the transcription factor; the modular structural domain parameters of the transcription factor can be optimized through the quantitative model, and a transcription factor with optimized performance is obtained.

[0049] (2) The present invention determines the optimization effect of introducing the cooperative dimerization process into the transcriptional regulation process on transcriptional regulation, establishes a domain library with different parameter ranges for each domain, and establishes a domain library with mutually orthogonal DBD×operator and LBD×inducer levels, providing tools for the implementation of complex gene regulatory circuits. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and specific implementation methods.

[0051] FIG1 is a schematic diagram of the basic structure of the modular domain transcription factor of the present invention;

[0052] FIG2 is a schematic diagram of a DBD domain screening test vector and system constructed in the present invention;

[0053] FIG3 is a schematic diagram of the LBD domain screening test vector and system constructed in the present invention;

[0054] FIG4 is a schematic diagram of the AD domain screening test vector and system constructed by the present invention;

[0055] FIG5 is a DBD domain quantitative test vector constructed by the present invention;

[0056] FIG6 is a quantitative test vector for the LBD domain constructed by the present invention;

[0057] FIG7 is a transcription factor quantitative test vector constructed by the present invention;

[0058] FIG8 is a diagram showing the effective DBD test results and orthogonality of the present invention;

[0059] FIG9 is an effective LBD test result of the present invention;

[0060] FIG10 is an effective AD test result of the present invention;

[0061] FIG11 is a schematic diagram of a quantitative model of non-nuclear receptor transcriptional regulation according to the present invention;

[0062] FIG12 is a quantitative test result of the present invention;

[0063] FIG13 is an orthogonal table of LBD×inducer of the present invention;

[0064] FIG14 is a crosstalk induction curve of the present invention;

[0065] FIG15 is a partial parameter value of the preliminary fitting of the model of the present invention;

[0066] FIG16 shows the optimization guided by the model of the present invention. DETAILED DESCRIPTION

[0067] Unless otherwise specified, the experimental methods in the following examples are conventional methods and were performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials and reagents used in the following examples, unless otherwise specified, were all commercially available.

[0068] The golden gate system and conditions used in the following embodiment examples are as follows:

[0069] The yeast screening test culture conditions in the following examples are as follows:

[0070] After obtaining the yeast transformants, they were inoculated into a 96-deep-well plate containing 500 μl of the corresponding defective SD medium and cultured at 30°C and 800 rpm for 24 hours. Then, they were transferred to a new 96-deep-well plate with SD medium at a ratio of 1:200. At the same time, each experimental group was set up with non-induced -, adding inducer anhydrotetracycline aTc+- (working concentration: 100 ng / ml) or the corresponding inducer at the maximum working concentration -+, or both adding ++ group. After culturing for 16 hours, an appropriate amount of bacterial solution was taken and diluted with 1xPBS solution. The results were analyzed using a flow cytometer BD FACSCelesta TM The FITC-A channel was used to detect the expression of the fluorescent protein YFP in 10,000 cells. The data were processed using FlowJo, and the median was taken as the fluorescence value of each sample well. Using CYE72 / CY671 as the expression control, the relative fluorescence expression of each experimental group was calculated as follows: RPU = (YFP 测试 -YFP CYE72 ) / (YFP CYE72 / CY637 -YFP CYE72 ).

[0071] The yeast quantitative test culture conditions in the following examples are as follows:

[0072] After obtaining yeast transformants, they were inoculated into 96-deep-well plates containing 500 μl of the corresponding defective SD medium and cultured at 30°C and 800 rpm for 24 hours. The plates were then transferred to fresh 96-deep-well plates with SD medium at a ratio of 1:200 for the first time. The induction range was tested: a different xylose concentration gradient (0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 0.9, 1, 1.5, 2, 5, 10, and 20 mM) was set based on the input promoter induction curve to obtain different transcription factor expression levels. Furthermore, two groups of inducers were set at ± the maximum working concentration at different xylose concentrations. Under these experimental conditions, the 96-deep-well plates were cultured at 30°C and 800 rpm for 16 hours, and the first sample was taken for testing. This determined the regulatory induction range for each transcription factor expression level.

[0073] Select transcription factor expression levels within an appropriate induction range (generally, a gradient of 0.4, 0.6, 1, 2, and 10 mM xylose concentrations) and test the induction curves under different inducer concentration gradients: After the first transfer, culture at 30°C, 800 rpm for 24 hours. Select the corresponding +xylose group as the stock solution and transfer it to a new 96-deep-well plate with SD medium at a ratio of 1:200. At the same time, different inducer concentration gradients under these different xylose concentrations are set. After 16 hours of culture at 30°C, 800 rpm, sample the cells for the second test.

[0074] For the two transfers, a blank control group CYE72 and a constitutive expression control group CYE72 / CY671 were set up for each transfer; the input promoter inducible expression control bacteria CYE72 / LXR347 were used to calibrate the expression level of the input promoter at the corresponding xylose concentration for each test. For the two sampling tests, when sampling, the appropriate amount of bacterial solution was diluted with 1xPBS solution and analyzed using a flow cytometer BD FACSCelesta TM The FITC-A channel was used to detect the expression of the fluorescent protein YFP in 10,000 cells. The data were processed using FlowJo, and the median was taken as the fluorescence value of each sample well. Using CYE72 / CY671 as the expression control, the relative fluorescence expression of each experimental group was calculated as follows: RPU = (YFP 测试 -YFP CYE72 ) / (YFP CYE72 / CY671 -YFP CYE72 ). Thus, the regulatory induction range of each transcription factor expression level is obtained.

[0075] Example 1: Screening for effective DNA binding domains

[0076] 1. Construction of DNA binding domain screening vector LXR66

[0077] The existing plasmid pXJH119 (ptet-lexAec 1-87 -RpaR 1-179 -VP16-tENO2) was used as a template and primers 66-F (5'-GGTTCTAAGGATATCTCTGCTGGAGACATGA-3') and 66-R (5'-CATTTTTTATTTATTTTTGTAGCTTGATATTCTCTATCAC-3') were used to amplify PCR fragment 1. Plasmid CY386 (containing the lethal gene ccdb) available in the laboratory was used as a template and primers Homo-CCDB-66-F (5'-ACAAAAATAAATAAAAAATGAGGTCTTCTTATATTCCCCAGAACATCAGGTTAATGG-3') and Homo-CCDB-66-R (5'-GCAGAGATATCCTTAGAACCAGGTCTTCGGCTTACTAAAAGCCAGATAACAGTATGC-3') were used to amplify PCR fragment 2 of the CCDB lethal gene. The two PCR fragments were homologously recombined using II One Step Cloning Kit C112 to obtain the Saccharomyces cerevisiae transcription activator DBD screening vector LXR66, as shown in SEQ ID NO: 1.

[0078] 2. Construction of DNA Binding Domain Screening System

[0079] Based on the sequence of the vector LXR66, the Golden Gate method was used to digest it with BpiI, and the scar sequences at the two sticky ends, AATG and GGTT, were used to replace the CCDB of different DBDs. A series of transcription factors derived from prokaryotes or viruses were truncated according to their structural characteristics to obtain the amino acid and nucleotide sequences of their DBDs (see SEQ ID NOs: 7-18). Different DBD sequences were obtained through gene synthesis, genomic sequences, or existing plasmid sequences. AATG and GGTT scars, as well as BpiI recognition sites and protective bases, were added to both ends through PCR. Transcription factor plasmids containing different DBDs were constructed using the BpiI Golden Gate method. Correspondingly, using the existing plasmid pXJH1 in the laboratory as a vector, reporter gene plasmids containing the corresponding DBD regulatory operators were obtained through segmented long primer PCR and overlap PCR. After obtaining the desired construct, the restriction endonuclease BsaI was used to digest the transformed enzyme fragments. The zymo Frozen-EZ Yeast Transformation II Kit was used. TM The yeast transformation kit was used to prepare the CYE72 competent strain, and the enzyme-digested fragments were then transferred into the CYE72 competent strain in two steps to obtain yeast transformants.

[0080] The plasmids containing the DBD and operator required for the yeast screening test system are shown in Table 1:

[0081] Table 1 Plasmids of DBD and operators required for the yeast screening test system

[0082] 3. Effects of different DNA binding domains on transcriptional regulation

[0083] After obtaining yeast test transformants, the test bacterial liquid was obtained by yeast screening test culture and the BD FACSCelesta TM Flow cytometry was used to analyze each DBD, with two groups set up: non-inducible and induced expression with induced dimerization. The test results for the 12 valid DBDs screened are shown in Figure 8. As shown in Figure 8, promoters containing different operators exhibit varying degrees of basal expression strength, and activation strength also varies with the DBD under the expression conditions tested.

[0084] Example 2: Screening of effective ligand-binding domains

[0085] 1. Construction of the ligand binding domain screening vector LXR77

[0086] The existing plasmid pXJH119 (ptet-lexAec 1-87-RpaR 1-179 -VP16-tENO2) was used as a template and primers 77-F (5'-GGTAGCCCTAAGAAAAAGAGAAAAGTGG-3') and 77-R (5'-GATCATGAGCGGGCTTGGCCA-3') were used to amplify PCR fragment 1. Using the existing plasmid CY386 in the laboratory as a template, primers Homo-CCDB-77-F (5'-GGCCAAGCCCGCTCATGATCAGGTCTTCTTATATTCCCCAGAACATCAG-3') and Homo-CCDB-77-R (5'-CTCTTTTTCTTAGGGCTACCAGGTCTTCGGCTTACTAAAAGCCAGATAAC-3') were used to amplify the CCDB lethal gene. The two PCR fragments were homologously recombined using II One Step Cloning Kit C112 to obtain the Saccharomyces cerevisiae transcription activator ligand binding domain screening vector LXR77, see SEQ ID NO: 2.

[0087] 2. Construction of ligand binding domain screening system

[0088] According to the sequence of the vector LXR77, BpiI was used for enzyme digestion through the Golden Gate method, and the CCDB replacement of different LBDs was achieved through the scar sequences at the two sticky ends: GATC and GGTA. According to literature research, a series of proteins with chemically induced dimerization were intercepted by ligand binding-dimerization domain to obtain their amino acid and nucleotide sequences (valid LBD sequences are shown in SEQ ID NO: 19-31). Different LBD sequences were obtained by gene synthesis or existing plasmid sequences. GATC and GGTA scar as well as BpiI recognition sites and protective bases were added to both ends through the PCR process. The transcription factor plasmids containing different LBDs were constructed by the BpiI Golden Gate method. It should be noted that most LBDs were screened and constructed using LXR77 as a vector, matching lexAec 1-87 As a DBD. However, the overall performance of the transcription factor is affected by all domains simultaneously, so the effectiveness of some LBDs is also verified in other DBD situations. The constructs that screened for LBD effectiveness are shown in Table 2:

[0089] Table 2 Screening results of LBD validity construction

[0090] After obtaining the desired construct, the fragment with the restriction endonuclease BsaI was obtained. TM The competent cells of strain CYE72 / LXR152 were prepared using a yeast transformation kit, and the enzyme-digested fragments were then transformed to obtain yeast transformants.

[0091] 3. Effects of different ligand-binding domains on transcriptional regulation

[0092] After obtaining yeast test transformants, the test bacterial liquid was obtained by yeast screening test culture and the BD FACSCelesta TM For flow cytometry detection, each LBD was set up in three groups: non-induced, induced expression, and induced expression with induced dimerization. The test results of the 13 valid LBDs screened are shown in Figure 9. As shown in Figure 9, LBDs mainly come from three types of proteins: single-domain antibodies, prokaryotic quorum sensing transcription regulatory proteins, and mammalian nuclear receptors. Under the expression levels under the test conditions, different LBDs will lead to different degrees of background activation strength caused by background dimerization of transcription factors. Different LBDs correspond to different inducer concentrations, and the maximum activation strength that different LBDs can achieve at the maximum working concentration of their inducers is also different.

[0093] Example 3: Screening for effective transcription activation domains

[0094] 1. Construction of the transcriptional activation domain screening vector LXR233

[0095] The existing plasmid pXJH119 (ptet-lexAec 1-87 -RpaR 1-179-VP16-tENO2) was used as a template and primers 233-F (5'-TAAAAGCTTTTGATTAAGCCTTCTAGTCCAAAAAAC-3') and 233-R (5'-CACTTTTCTCTTTTTCTTAGGGCTACCATTAC-3') were used to amplify PCR fragment 1. Using the existing plasmid CY386 in the laboratory as a template, primers Homo-GGS5-CCDB-233-F (5'-CTAAGAAAAAGAGAAAAGTGGGTGGCAGTGGCGGAAGCGGGGGATCAGGTGGTTCTGGAGGGTCCAGGTCTTCTTATATTCCCCAGAACATCAGGTTAATGG-3') and Homo-CCDB-233-R (5'-GGCTTAATCAAAAGCTTTTAAGGTCTTCGGCTTACTAAAAGCCAGATAACAGTATGCA-3') were used to amplify the CCDB lethal gene. The two PCR fragments were homologously recombined using II One Step Cloning Kit C112 to obtain the Saccharomyces cerevisiae transcription activator ligand binding domain screening vector LXR233, see SEQ ID NO: 3.

[0096] 2. Construction of a transcriptional activation domain screening system

[0097] Through literature research, Ariel Erijman et al. proposed a model for predicting the structure and function of an AD in 2020, predicting a series of amino acid sequences with transcriptional activation function. We selected seven amino acid sequences with good predicted transcriptional activation functions and tested them against several commonly used yeast transcriptional activation domains to obtain multiple ADs that matched multiple transcription factors. Different AD sequences were obtained through gene synthesis or existing plasmid sequences. GTCC and TAAA scar residues matching the vector LXR233, as well as the BpiI recognition site and protective bases, were added to both ends through PCR. The BpiI Golden Gate method was then used.

[0098] After obtaining the desired construct, the fragment with the restriction endonuclease BsaI was obtained. TM The competent cells of strain CYE72 / LXR152 were prepared using a yeast transformation kit, and the enzyme-digested fragments were then transformed to obtain yeast transformants.

[0099] 3. Effects of different transcriptional activation domains on transcriptional regulation

[0100] After obtaining yeast test transformants, the test bacterial liquid was obtained by yeast screening test culture and the BD FACSCelesta TM Flow cytometry was used to analyze each DBD, with two groups set up: non-induced and induced expression with induced dimerization. The results are shown in Figure 10. As can be seen from the results in Figure 10, different ADs have varying degrees of influence on the maximum activation intensity of transcription factors. Of the 13 ADs screened, nine exhibited significant transcriptional activation (SEQ ID NOs: 32-40), with VP16 showing the strongest maximum activation intensity.

[0101] Example 4: Construction of activation-type transcriptional regulation model

[0102] 1. Non-nuclear receptor dimerization transcription activation model (not considering the nuclear entry process)

[0103] The transcriptional regulation process in this method can be described as follows (parameters are shown in Figure 11): ① The input promoter expresses the transcription factor monomer at a certain expression level; ② The transcription factor monomer can form transcription factor dimers to varying degrees, either spontaneously or under the action of an inducer; ③ The transcription factor dimer recognizes and binds to the operator sequence on the promoter with a certain affinity. LBD represents the transcription factor monomer protein, LBD2 represents the dimer formed by two transcription factor monomer proteins, I represents the inducer molecule, LBD:I represents the intermediate form of the binding between the monomer protein and the inducer molecule, LBD2:I2 represents the dimer formed by two protein monomers and two inducer molecules, P0 represents the inactive state promoter, and P1 and P2 represent the active state promoter formed by the dimer formed by different binding pathways; this process can be represented by the following equation:

[0104] During regulation, the possible states of the promoter include: ① empty promoter; ② only bound to RNAP; ③ simultaneously bound to RNAP and the background dimer of the transcription factor; ④ simultaneously bound to RNAP and the induced dimer of the transcription factor. ③ and ④ correspond to different states of promoter activity, respectively. According to the partition equation, the model equation can be described as follows:

[0105] Among them, F1 factor is a parameter that describes the performance of transcription factors under a specific DBD-LBD combination; c is the concentration of LBD in different forms, I0 is the background leakage expression intensity of the promoter of a specific sequence, and I max is the theoretical maximum activation strength of a specific sequence promoter, I is the inducer concentration, L totis the sum of the expression levels of all forms of the transcription factor monomer, Δε DBD (K4, K5) describe the behavior of DBD, and K1, K2, and K3 describe the behavior of LBD.

[0106] 2. Nuclear receptor dimerization transcription activation model (considering the nuclear entry process)

[0107] The nuclear receptor transcriptional regulation process increases the nuclear entry process of various transcription factor proteins. LBD and LBD* represent the extranuclear and intranuclear states of transcription factor monomer proteins, respectively. LBD2 and LBD2* represent the extranuclear and intranuclear states of the dimer formed by two transcription factor monomer proteins, respectively. I represents the inducer molecule. LBD:I and LBD*:I represent the intermediate states of the binding between the monomer protein and the inducer molecule in the extranuclear and intranuclear states, respectively. LBD2:I2 and LBD2*:I2 represent the extranuclear and intranuclear states of the dimer formed by two protein monomers and two inducer molecules. This process can be expressed by the following equation:

[0108] Out of core:

[0109] Entering the nucleus:

[0110] Inside the core:

[0111] According to the partition equation, the model equation can be described as follows:

[0112] Among them, F1 factor is a parameter that describes the performance of transcription factors under a specific DBD-LBD combination; c is the concentration of LBD in different forms, I0 is the background leakage expression intensity of the promoter of a specific sequence, and I max is the theoretical maximum activation strength of a specific sequence promoter, I is the inducer concentration, L tot is the sum of the expression levels of all forms of the transcription factor monomer, Δε DBD (K4, K5) describe the behavior of DBD, K1, K2, K3 describe the behavior of LBD, and K* and K*' describe the nuclear import process of transcription factors.

[0113] Example 5: Quantitative testing of structural domain parameters

[0114] 1. Construction of DNA binding domain parameter quantitative test vector LXR322

[0115] The constructed plasmid LXR319 (pxyluas-lexAec 1-87 -CarHc-VP16-tENO2) was used as a template, and primers 322-F (5'-CAGTGAGAAGACCTGTAGCCCTAAGAAAAAGAGAAAAGTGGG-3') and 322-R (5'-CAGTACGAAGACTACATTTTTTATTTATTTTTGTAGCTTGATATTCTAGTTTGTTG-3') were used to amplify PCR fragment 1. PCR fragment 2 was obtained by amplifying the plasmid pXJH119 available in the laboratory with primers RpaR-322-F (5'-CAGTGAGAAGACCTGGTTCTAAGGATATCATTGTGGGTGAAGATCAGCTGTGG-3') and RpaR-322-R (5'-TAAGCCGAAGACCTCTACCATTACGACGAATCGGTTTCG-3'). 1-179 LBD domain. Using the laboratory-available plasmid CY386 as a template, primers CCDB-322-F (5'-CAGTGAAATGAGGTCTTCTTATATTCCCCAGAACATCAG-3') and CCDB-322-R (5'-TAAGCCAACCAGGTCTTCGGCTTACTAAAAGCCAGATAAC-3') were used to amplify the CCDB lethal gene. Using the Golden Gate technique with the BpiI restriction endonuclease, the Saccharomyces cerevisiae transcription activator DNA binding domain parameter quantitative test vector LXR322 was obtained (see SEQ ID NO: 4).

[0116] 2. Construction of a quantitative test system for DNA binding domain parameters

[0117] The 12 DBDs screened were amplified by PCR and the AATG and GGTT scars matching the vector LXR322 as well as the BpiI recognition site and protection bases were added at both ends. The plasmids for quantitative testing of different DBD parameters were constructed by the BpiI Golden Gate method. In order to simplify the parameter testing, the regulatory promoters only contained one operator, lexAec 1-87 Taking the corresponding lexO as an example, the promoter sequence is: The bolded positions represent the promoter's polyA, TATA box, TSS, and Kozak sequence. The uppercase, non-bold positions are lexO. By substituting different operators at these positions, a series of regulated promoter plasmids were constructed. Promoter fragments with restriction sites at both ends were obtained through long-primer PCR and overlap PCR. Using the existing laboratory plasmid pXJH1 as a vector, reporter gene plasmids corresponding to different DBD quantitative tests were obtained using the BpiI Golden Gate method. Information on the plasmids used for DBD quantitative testing is shown in Table 3:

[0118] Table 3 Plasmid information used for DBD quantitative test

[0119] After obtaining the desired construct, the fragment with the restriction endonuclease BsaI was obtained. TM The yeast transformation kit was used to prepare the CYE72 competent strain, and the enzyme-digested fragments were then transferred into the CYE72 competent strain in two steps to obtain yeast transformants.

[0120] 3. Construction of LXR321 vector for quantitative testing of ligand binding domain parameters

[0121] The constructed plasmid LXR319 (pxyluas-lexAec 1-87 -CarHc-VP16-tENO2) was used as a template and primers 321-F (5'-CAGTGAGAAGACCTGGTAGCCCTAAGAAAAAGAGAAAAGTGG-3') and 321-R (5'-CAGTACGAAGACTACATTTTTTATTTATTTTTGTAGCTTGATATTCTAGTTTGTTG-3') were used to amplify the PCR fragment No. 1. The constructed plasmid LXR36 was used as a template and primers lexAbs 1-94 -321-F(5'-CAGTGAGAAGACCTAATGTCTACGAAGCTATCAAAAAGGCAAC-3') and lexAbs 1-94 -321-R (5'-TAAGCCGAAGACCTGATATCCTTAGAACCAGGAGATCCCGCCGTGACTTTC-3') was amplified to obtain PCR fragment No. 2, lexAb 1-94DBD domain. Using the laboratory-available plasmid CY386 as a template, primers CCDB-321-F (5'-CAGTGATATCAGGTCTTCTTATATTCCCCAGAACATCAG-3') and CCDB-321-R (5'-TAAGCCTACCAGGTCTTCGGCTTACTAAAAGCCAGATAAC-3') were used to amplify the CCDB lethal gene. Using the Golden Gate technique with the BpiI restriction endonuclease, the Saccharomyces cerevisiae transcription activator ligand binding domain parameter quantitative test vector LXR321 was obtained (see SEQ ID NO: 5).

[0122] 4. Construction of a quantitative test system for ligand binding domain parameters

[0123] The 13 LBDs screened were amplified by PCR and TATC and GGTA scars matching the vector LXR321 as well as BpiI recognition sites and protective bases were added at both ends. The plasmids for quantitative testing of different LBD parameters were constructed using the BpiI Golden Gate method. It should be noted that most LBDs were constructed using LXR321 as the vector for quantitative testing and matched lexAbs. 1-94 As DBD. However, considering the influence of different DBDs on the induction curve, some LBDs were also quantitatively tested under other DBD conditions. The plasmid information used for LBD quantitative testing is shown in Table 4:

[0124] Table 4 Plasmid information used for LBD quantitative test

[0125] After obtaining the desired construct, the fragment with the restriction endonuclease BsaI was obtained. TM A yeast transformation kit was used to prepare competent cells of strain CYE72 / LXR349, and the enzyme-digested fragments were transformed into the competent cells to obtain yeast transformants.

[0126] 5. Domain Parameter Measurement and Fitting

[0127] In this method, the corresponding parameters Δε of the two structural domains DBD and LBD are mainly analyzed. DBD After obtaining yeast test transformants, the test bacterial solution was obtained by the yeast quantitative test culture method and the BD FACSCelesta TMFlow cytometry was used to process the data using FlowJo. The test data results are shown in Figure 12. The obtained test data were fitted with the model to preliminarily obtain the corresponding parameters of different DBDs and LBDs.

[0128] Example 6: Ligand Binding Domain Binding Ligand Orthogonality Test

[0129] 1. Orthogonal array measurement

[0130] Select effective transcription factors for LBDs other than PR, GR, and MR, and obtain yeast culture using the yeast quantitative test culture method. Set two transcription factor expression levels, RPU = 0.67 and RPU = 1.24, and perform full permutation orthogonality tests on all LBDs and inducers at the two expression levels. Among them, when adding inducers, set the induction group and non-induced expression group of the maximum working concentration of all inducers for each LBD, so as to obtain the induction range and regulation multiple of each LBD in response to different inducers at a specific expression level. The plasmid information and inducer information used for the orthogonal table test are shown in Table 5:

[0131] Table 5 Plasmid information and inducer information used for orthogonal array testing

[0132] At two expression levels, the logarithm of the regulation multiples of all LBDs obtained in the test to all inducers was taken to make a 10×10 orthogonal table, as shown in Figure 13. The color of each grid in the orthogonal table corresponds to the value of the logarithm of the regulation multiple. The larger the value, the darker the color. Only the dark diagonal line indicates a completely orthogonal situation with no interaction. As shown in Figure 13, crosstalk exists among the LBDs and inducers selected by this method, including optimizable crosstalk that is weaker than the natural response (such as CinRori 2-179 3OC12-HSL) and non-optimizable crosstalk (such as TraR) that is stronger than the native response 1-174 For 3OC12-HSL) two types.

[0133] 2. Crosstalk Induction Curve Test and Parameter Fitting

[0134] For the optimizable crosstalk portion obtained from the orthogonal array test, the corresponding LBD induction curve to the inducer was obtained through the yeast quantitative test culture method, as shown in Figure 14. This test result provides fitting data for the corresponding LBD response control parameters K2' and K3' to the inducer.

[0135] Example 7: Model-guided rational optimization

[0136] 1. Construction of the domain-modular transcription factor expression vector LXR370

[0137] The constructed plasmid LXR319 (pxyluas-lexAec 1-87 -CarHc-VP16-tENO2) was used as a template and primers 370-F (5'-CAGTGAGAAGACCTGGTAGCCCTAAGAAAAAGAGAAAAGTGG-3') and 370-R (5'-CAGTACGAAGACTACATTTTTTATTTATTTTTGTAGCTTGATATTCTAGTTTGTTG-3') were used to amplify PCR fragment 1. The CCDB lethal gene was amplified using the existing plasmid CY386 in the laboratory as a template and primers CCDB-370-F (5'-CAGTGAAATGAGGTCTTCTTATATTCCCCAGAACATCAG-3') and CCDB-370-R (5'-TAAGCCTACCAGGTCTTCGGCTTACTAAAAGCCAGATAAC-3'). The yeast transcription activator ligand binding domain parameter quantitative test vector LXR370 was obtained by Golden Gate technology using BpiI restriction enzyme, as shown in SEQ ID NO: 6.

[0138] 2. Model Validation

[0139] 14 different transcription factors and their corresponding expression levels in the model output were selected for experimental testing and verification of the model output. Using LXR370 as the vector, DBD with corresponding scar and linker at both ends was obtained by PCR as PCR fragment No. 1, and LBD was obtained as PCR fragment No. 2. The yeast transcription activator with VP16 as AD and different DBD and LBD combinations was constructed by the BpiI Golden Gate method for combination testing and parameterized model verification. After obtaining the relevant transcription factor plasmid, the restriction endonuclease BsaI was used to cut the fragment with transformation enzyme. Using the zymo Frozen-EZ Yeast Transformation II Kit TM Yeast competent cells containing the corresponding DBD-regulated promoter were prepared using a yeast transformation kit, and the enzyme-digested fragments were transformed into competent cells to obtain yeast transformants. The plasmid information, transformation competent cells, and transcription factor expression levels related to the model validation combination test are shown in Table 6:

[0140] Table 6 Plasmid information, transformation competence, and transcription factor expression levels related to the model validation combination test

[0141] After obtaining yeast transformants, yeast culture was obtained using a quantitative yeast culture method. An induction curve test was performed at the expression level specified by the model. The results were then compared with the theoretical model output. Some of the fitting results are shown in Figure 15. As shown in Figure 15, the experimental data generally conformed to the theoretical predictions, with an R² > 0.84, indicating that the model's prediction output was highly accurate.

[0142] 3. Model-guided optimization

[0143] This model describes the transcriptional regulation properties of specific transcriptional activators in Saccharomyces cerevisiae, as shown in Figure 16a. Generally speaking, the following are the key characteristics: ① The sequence of the operator corresponding to a specific DBD, as part of the promoter sequence, determines the theoretical minimum leakage and maximum transcriptional activation strength determined by the promoter; ② The background dimerization degree k1 of the LBD determines the leakage caused by background dimerization at a specific transcription factor expression level, i.e., the minimum value of the induction curve at that expression level; ③ The inducible dimerization capacities k2 and k3 of the LBD determine its transcriptional regulatory sensitivity and the achievable transcriptional activation strength, i.e., the curve's EC50 and maximum activation; in general, the LBD parameters determine the curve's shape; ④ The DBD-operator binding affinity k4 and k5, along with the transcription factor expression level, shift the induction curve in the coordinate system. Stronger affinity and higher transcription factor expression result in a more upward and leftward shift in the induction curve. Based on this, for a specific LBD that responds to a specific inducer small molecule, we can optimize the background leakage expression intensity, activation expression intensity, orthogonality, etc. by changing the DBD-operator and transcription factor expression level. As shown in Figure 16b, under the same transcription factor expression level, LasR 2-177 By replacing different DBDs for LBD, we can obtain different transcription factors with background dimerization leakage expression and induced activation expression, thereby optimizing the induction range. 1-101 Optimal regulation is achieved under DBD. In addition, as shown in Figures 16c and d, ER 282-595 Able to respond to the autoinducer β-estradiol and DHBR 282-595 There is an interaction between the inducer DHB and ER. 282-595 The responses to the two inducers were in a relatively orthogonal range (>10-fold difference in activation strength), and the lexAbs 1-94 In the case of DBD, low background, high activation, and relative orthogonality can be achieved by controlling the expression level within the range of 0.1 to 0.5 RPU. 1-92In the case of DBD, a good range of induction by autoinducers and mutual orthogonality can be achieved at a relatively appropriate transcription factor expression level (1-7 RPU). This allows for model-guided optimization of multiple indicators such as transcription factor regulation leakage, background expression, activation strength, dynamic range, sensitivity to inducers, and orthogonality among multiple transcription factors.

[0144] sequence:

[0145] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for constructing a modular orthogonal eukaryotic transcription factor model, comprising the following steps: S1. Establish a basic architecture of a transcription factor DBD-LBD-AD composed of three domains: a DNA binding domain, a ligand binding domain, and a transcriptional activation domain; S2. On the basis of the basic architecture of the transcription factor DBD-LBD-AD, screen Saccharomyces cerevisiae strains for the three domains of the DNA binding domain, the ligand binding domain, and the transcriptional activation domain respectively, and then conduct screening test cultures on the screened Saccharomyces cerevisiae strains to screen out effective DBD, LBD, and AD; S3. On the basis of the basic architecture of the transcription factor DBD-LBD-AD, construct quantitative test Saccharomyces cerevisiae strains for quantitative testing of domain parameters using the screened DBD and LBD respectively; conduct quantitative test cultures on the quantitative test Saccharomyces cerevisiae strains to obtain the regulation induction range and regulation fold under the transcription factor expression level; fit the obtained regulation induction range and regulation fold data to obtain the corresponding parameters of each domain of the screened DBD and LBD; S4. Characterize the transcription factor regulation process according to the corresponding parameters of each domain of DBD and LBD, and establish a transcriptional regulation quantitative model; predict transcription factors under different DBD and LBD combinations through the transcriptional regulation quantitative model to obtain transcription factors with optimized performance.

2. The modular orthogonal eukaryotic transcription factor model construction method according to claim 1, wherein In the basic architecture DBD-LBD-AD, 1-87aa of the E. coli SOS regulatory protein lexA is used as DBD, 1-179aa of the quorum sensing transcription factor RpaR of Rhodopseudomonas palustris is used as LBD, and VP16 is used as AD.

3. The modular orthogonal eukaryotic transcription factor model construction method according to claim 2, characterized in that The Saccharomyces cerevisiae strain for screening the DNA binding domain in S2 is obtained by the following method: construct a screening transcription factor expression vector for DBD, construct a corresponding reporter gene expression vector, digest the reporter gene expression vector to obtain a transcription factor expression fragment, and integrate the transcription factor expression fragment into yeast to obtain the Saccharomyces cerevisiae strain for screening DBD.

4. The modular orthogonal eukaryotic transcription factor model construction method according to claim 3, wherein The sequence of the screening transcription factor expression vector for DBD is as shown in SEQ ID NO: 1; the sequence of the effectively screened DBD is as shown in SEQ ID NO: 7-18.

5. The modular orthogonal eukaryotic transcription factor model construction method according to claim 3 or 4, characterized in that, The Saccharomyces cerevisiae strain for screening the ligand binding domain in S2 is obtained by the following method: construct a screening transcription factor expression vector for LBD, construct a corresponding reporter gene expression vector, digest the reporter gene expression vector to obtain a transcription factor expression fragment, and integrate the transcription factor expression fragment into yeast to obtain the Saccharomyces cerevisiae strain for screening LBD.

6. The modular orthogonal eukaryotic transcription factor model construction method according to claim 5, characterized in that The sequence of the screening transcription factor expression vector for LBD is as shown in SEQ ID NO: 2; the sequence of the effectively screened LBD is as shown in SEQ ID NO: 19-31.

7. The modular orthogonal eukaryotic transcription factor model construction method according to claim 1, characterized in that The Saccharomyces cerevisiae strain for quantitative testing of the DNA binding domain in S3 is obtained by the following method: constructing a transcription factor expression vector for quantitative testing of the DBD parameters, then constructing a corresponding reporter gene vector, digesting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain the Saccharomyces cerevisiae strain for quantitative testing of the DBD.

8. The modular orthogonal eukaryotic transcription factor model construction method according to claim 7, characterized in that The sequence of the transcription factor expression vector for quantitative testing of the DBD parameters is as shown in SEQ ID NO:

4.

9. The modular orthogonal eukaryotic transcription factor model construction method according to claim 7, characterized in that, The Saccharomyces cerevisiae strain for quantitative testing of the ligand binding domain in S3 is obtained by the following method: constructing a transcription factor expression vector for quantitative testing of the LBD parameters, then constructing a corresponding reporter gene vector, digesting the reporter gene expression vector to obtain a transcription factor expression fragment, and integrating the transcription factor expression fragment into yeast to obtain the Saccharomyces cerevisiae strain for quantitative testing of the LBD.

10. The modular orthogonal eukaryotic transcription factor model construction method according to claim 9, wherein The transcription factor expression vector for quantitative testing of the LBD parameters is as shown in SEQ ID NO:

5.

11. The modular orthogonal eukaryotic transcription factor model construction method according to claim 1, characterized in that The transcriptional regulation quantitative model includes a non-nuclear receptor dimerization transcriptional activation model and a nuclear receptor-like dimerization transcriptional activation model; The model equations for non-nuclear receptor dimerization transcriptional activation are as follows: The nuclear receptor dimerization transcriptional activation model equation is as follows: Among them, the F1 factor is a parameter describing the performance of the transcription factor under a specific DBD-LBD combination; c is the concentration of different forms of LBD, I0 is the background leakage expression intensity of the promoter of a specific sequence, I max is the theoretical maximum activation intensity of the promoter of a specific sequence, I is the inducer concentration, L tot is the expression level of all forms of the transcription factor The sum of the monomeric forms, Δε DBD describes the behavior of the DBD, and K1, K2, K3 describe the behavior of the LBD, K * and K * ’ describes the nuclear import process of the transcription factor.

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