A multi-target vertical fishing screening and recovery identification method for natural product or traditional Chinese medicine extract target discovery

Abstract

The present application relates to the technical field of biosensing detection and natural product drug discovery, and particularly relates to a multi-target vertical fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts. The method comprises the following steps: constructing a target panel related to a target disease or biological process, and fixing at least two different decoy molecules in the panel to different channels of a bio-layer interferometry (BLI) sensor array respectively, while at least one reference channel is set; placing the sensor array in the same portion of a natural product or traditional Chinese medicine extract solution to be tested, and collecting the binding / dissociation kinetics curves in parallel; performing reference deduction, drift correction and normalization processing on the data of each channel; discriminating the corrected curves according to a preset kinetics gating criterion, outputting the decoy channel set passing the gating, and generating a confidence score to form a Top-N candidate target list; cleaning and micro-eluting the sensor surface of the candidate channel, and performing mass spectrometry, sequencing or immunology and other downstream identification on the elution recovery to establish a correspondence between the target and the binding molecule.

Description

Technical Field

[0001] This invention relates to the field of biosensing detection and natural product drug discovery technology, specifically a multi-target fishing screening and recovery identification method for the discovery of targets in natural products or traditional Chinese medicine extracts. Background Technology

[0002] Biolayer interferometry (BLI) can monitor molecular binding processes in real time without labeling and is widely used for one-to-one kinetic measurements between known ligands and known targets. However, when conducting "target discovery" in complex mixed systems such as natural products or traditional Chinese medicine extracts, the following challenges are still faced: (1) Non-specific adsorption, refractive / viscosity changes and baseline drift caused by complex matrices make it difficult to distinguish specific binding from background adsorption based solely on response amplitude; (2) Target discovery usually requires parallel screening of multiple candidate targets, but traditional one-by-one testing methods consume large amounts of samples and are inefficient; (3) The lack of reproducible discrimination criteria and standardized threshold systems results in insufficient consistency of results between different batches and different operators; (4) Without the closed loop of recovery and identification of the conjugate, it is difficult to further confirm the "identity of the bound molecule" from the "curve hints", thus limiting the transformation of target discovery to the mechanism and component level.

[0003] Therefore, there is an urgent need for a multi-target parallel fishing scheme for natural products or traditional Chinese medicine extracts, which can improve the reliability of discrimination through reference subtraction and kinetic gating in complex matrix backgrounds and achieve a closed loop for recovery and identification. Summary of the Invention

[0004] To address the aforementioned technical problems in the existing technology, this invention provides a multi-target fishing screening and recovery identification method for the discovery of targets in natural products or traditional Chinese medicine extracts, as detailed below: A multi-target fishing screening and recovery identification method for the discovery of targets in natural products or traditional Chinese medicine extracts, characterized by comprising the following steps: (1) Construction of target panel and probe array: Construct a target panel related to the target disease or biological process, fix at least two different decoy molecules in the panel to different channels of the biological layer interference sensor array to form decoy channels, and set at least one reference channel; (2) Parallel fishing and dynamic acquisition: The sensor array is placed in the same solution of the natural product or Chinese medicine extract to be tested, and the binding phase curve of each channel is acquired in real time and in parallel. After the sample is removed, the dissociation phase curve is acquired to obtain the dynamic data of each bait channel and the reference channel. (3) Data correction: The dynamic data is subjected to at least one reference subtraction and drift correction and normalization to obtain the corrected channel response curve; (4) Dynamic gating discrimination and target output: Based on the preset dynamic gating criteria, the corrected channel response curve is discriminated, the set of decoy channels that pass the gating is output and a confidence score is generated to form a candidate target list; (5) Recovery and downstream identification: The sensor surface of the channel corresponding to the candidate target is cleaned and a small amount of the conjugate is eluted and recovered. The eluted and recovered conjugate is then subjected to downstream identification analysis to determine the identity of the conjugate molecule and establish the correspondence between the candidate target and the conjugate molecule.

[0005] Furthermore, the target panel in step (1) is determined by at least one of the following information sources: disease-related pathways or biological processes, literature and patent reports, known drug target databases, protein interaction networks or omics data, and a set of candidate targets obtained based on computational prediction; the target panel contains 2 to 200 decoy molecules, preferably 4 to 96.

[0006] Furthermore, the reference channel is any one or a combination of the following: a blank sensor channel, a channel for immobilizing irrelevant proteins / nucleic acids, a channel for immobilizing only carrier / tag proteins, or a buffer baseline channel that does not contact the sample; the reference subtraction includes at least single reference subtraction or double reference subtraction.

[0007] Furthermore, the normalization process includes at least one of normalizing the channel response based on the decoy fixation amount, baseline noise, or initial response amplitude.

[0008] Furthermore, the kinetic gating criteria include at least two or more of the following criteria: a) the terminal response value of the binding phase exceeds a preset threshold R0; b) the residual proportion of the dissociated phase at a preset time point t0 exceeds a preset threshold P0; c) the goodness of fit of the kinetic model meets a preset threshold F0; d) the consistency of the response of the same decoy molecule in the repeating channel meets a preset threshold C0; e) under the condition of competitive addition of a known ligand / inhibitor or deactivation / denaturation control, the response reduction meets a preset threshold.

[0009] Furthermore, R0 is 0.02–5.00 nm, preferably 0.05–2.00 nm; t0 is 30–600 s, preferably 60–300 s; P0 is 5%–90%, preferably 10%–80%; when the goodness of fit is expressed as the coefficient of determination R², F0 is 0.85–0.999, preferably 0.90–0.99; when the repeatability consistency is expressed as the coefficient of variation CV, C0 is 5%–50%, preferably 10%–40%.

[0010] Furthermore, step (2) employs a cyclic fishing enrichment process, which includes at least 2 to 50 cycles of "binding-dissociation-rebinding" to improve the capture signal-to-noise ratio of low-abundance bound molecules and enhance the stability of candidate target output.

[0011] Furthermore, the decoy molecule is selected from any one or more of proteins, antibodies, antigens, receptor proteins, enzymes, nucleic acids, nucleic acid aptamers, polypeptides, or tag fusion proteins; its immobilization method is selected from any one or more of streptavidin-biotin immobilization, Ni-NTA / His tag capture, antibody capture, and amino / thiol covalent coupling.

[0012] Furthermore, the natural product or traditional Chinese medicine extract to be tested is pretreated by filtration, desalination, dilution, or the addition of at least one of a blocking agent / surfactant before entering step S2).

[0013] Furthermore, the elution and recovery in step (1) adopts an elution system containing organic solvents and / or an acidic elution system, wherein the volume fraction of the organic solvent is 10% to 90% and the elution volume is 5 to 100 μL.

[0014] Furthermore, the downstream identification analysis includes at least one of the following: liquid chromatography-mass spectrometry / tandem mass spectrometry, protein / peptide sequencing, immunological identification, or nucleic acid sequencing; and outputs the correspondence between "candidate target - binding molecule".

[0015] Furthermore, the confidence score is obtained by weighting at least two or more of the following: response amplitude, dissociation retention features, goodness of fit, and repeatability consistency. The candidate targets are then sorted according to the scores to output a Top-N candidate target list, where N is 1 to 50, preferably 3 to 20.

[0016] A multi-target fishing detection system for performing the above method includes: A) a biolayer interferometer host having at least four parallel detection channels; B) a sensor array and probe construction assembly for fixing at least two types of bait molecules in the target panel to different sensor surfaces and setting reference channels; C) a program control module for executing a fishing program including binding and dissociation phases and an optional cyclic fishing enrichment program; D) a data processing module for performing reference subtraction, drift correction and normalization on the data of each channel, and outputting a candidate target list according to a preset kinetic gating criterion; E) a micro-recovery component or interface for cleaning, eluting and collecting the eluted material from the sensor surfaces of the candidate target channels for downstream identification.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs data correction and dynamic gating discrimination and output of a candidate target list in steps (3) and (4) of the above method.

[0018] Compared with the prior art, the technical effects of this invention are reflected in: This invention is a multi-target screening method for the discovery of targets in natural products or traditional Chinese medicine extracts. It involves setting a reference channel and performing reference subtraction, drift correction, and normalization. Combined with kinetic gating (R0 / P0 / F0 / C0 range thresholds), a Top-N candidate target list is output. The candidate channels are then subjected to trace elution recovery and downstream identification to establish a "candidate target—binding molecule" correspondence. Specific technical effects are as follows: (1) This application outputs a set of gated decoy channels in parallel in a single sample to form a Top-N candidate target list, which significantly improves the target discovery throughput and reduces sample consumption; (2) This application reduces false positives caused by nonspecific adsorption by reference subtraction and kinetic gating, thereby improving the reliability and reproducibility of the results; (3) This application enhances the capture capacity of low-abundance conjugates through cyclic fishing enrichment, and is suitable for weak binding and low-abundance scenarios of natural products / Chinese herbal extracts; (4) This application establishes a “candidate target-binding molecule” correspondence through micro-elution recovery and downstream identification, which promotes subsequent mechanism research and active ingredient confirmation. Attached Figure Description

[0019] Figure 1 The decoy molecules TNF-α, COX-2, and IL-6 are immobilized on the sensor.

[0020] Figure 2 Real-time response graph of multi-target cyclic fishing; Note: Green curve (TNF-α); Light blue curve (COX-2); Red curve (IL-6); Dark blue curve (blank control).

[0021] Figure 3 The binding and dissociation curves of catechins and TNF-α.

[0022] Figure 4 The binding and dissociation curves of proanthocyanidin A2 and TNF-α.

[0023] Figure 5 The binding and dissociation curves of daidzein and TNF-α.

[0024] Figure 6 The binding and dissociation curve of magnoflorine and TNF-α.

[0025] Figure 7 SPR assays were performed to detect the binding of catechins to TNF-α.

[0026] Figure 8 SPR assay was used to detect the binding of genistein to TNF-α.

[0027] Figure 9 SPR detection of the binding of proanthocyanidin A2 to TNF-α.

[0028] Figure 10 SPR assays showed that magnoflorine binds to TNF-α.

[0029] Figure 11 CETSA was used to detect the binding ability of catechins to TNF-α.

[0030] Figure 12 CETSA was used to detect the binding ability of genistein to TNF-α.

[0031] Figure 13 CETSA was used to detect the binding capacity of proanthocyanidin A2 to TNF-α.

[0032] Figure 14 CETSA was used to detect the binding ability of magnoflorine to TNF-α.

[0033] Figure 15 Effects of catechins, daidzein, proanthocyanidins A2 and magnoflorine on chemiluminescence of NF-κB-RE-luc2P luciferase reporter gene.

[0034] Figure 16 Effects of catechins, daidzein, proanthocyanidins A2 and magnoflorine on TNF-α-induced survival of L929 cells.

[0035] Figure 17 Catechins reduce TNF-α-induced migration rate of MH7A cells.

[0036] Figure 18 Soy flavonoids reduce TNF-α-induced migration rate of MH7A cells.

[0037] Figure 19 Proanthocyanidin A2 reduces TNF-α-induced migration rate of MH7A cells.

[0038] Figure 20 Magnolia alkaloids reduce TNF-α-induced migration rate of MH7A cells.

[0039] Figure 21 Effects of catechins on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0040] Figure 22Effects of genistein on TNF-α-induced expression of inflammatory factors in MH7A cells.

[0041] Figure 23 Effects of proanthocyanidin A2 on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0042] Figure 24 Effects of magnoflorine on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0043] Figure 25 Effects of catechins on TNF-α-induced nuclear translocation in MH7A cells.

[0044] Figure 26 Effects of genistein on TNF-α-induced nuclear translocation in MH7A cells.

[0045] Figure 27 Effects of proanthocyanidin A2 on TNF-α-induced nuclear translocation in MH7A cells.

[0046] Figure 28 Effects of magnoflorine on TNF-α-induced nuclear translocation in MH7A cells.

[0047] Figure 29 Effects of catechins on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0048] Figure 30 Effects of genistein on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0049] Figure 31 Effects of proanthocyanidin A2 on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0050] Figure 32 Effects of magnoflorine on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0051] Figure 33 The therapeutic effect of magnoflorine on LPS-induced acute inflammation in mice.

[0052] Figure 34 The therapeutic effect of magnoflorine on DSS-induced inflammatory bowel disease in mice.

[0053] Figure 35 The therapeutic effect of magnoflorine on IMQ-induced psoriasis in mice. Detailed Implementation

[0054] The technical solution of the present invention will be further defined below with reference to specific embodiments, but the scope of protection is not limited to the description made.

[0055] Terms and Definitions (1) Target panel: A set of candidate targets pre-determined for target discovery, with corresponding decoy molecules fixed in different BLI channels.

[0056] (2) Decoy molecules: biomolecules immobilized on the surface of the BLI sensor to capture the conjugate, including proteins, antibodies, receptors, enzymes, nucleic acids, aptamers, peptides or tagged fusion proteins, etc.

[0057] (3) Reference channel: A control channel used to reflect nonspecific adsorption and baseline drift, including blank sensors or sensors with immobilized irrelevant molecules.

[0058] (4) Candidate target: The target corresponding to the decoy channel output after reference subtraction and dynamic gating discrimination.

[0059] (5) Bound molecules: molecules derived from natural products or traditional Chinese medicine extracts that are captured in the candidate target channel, can be eluted and recovered and identified.

[0060] Principles of Target Panel Construction To better align with the target discovery applications of natural products or traditional Chinese medicine extracts, this invention preferably adopts a "target panelization" strategy: a set of candidate targets related to the target disease or biological process is used as panel input, and parallel fishing and screening are performed on a panel-by-pane basis.

[0061] The target panel can be constructed based on at least one of the following information sources: (a) biological mechanisms such as disease-related signaling pathways, inflammation / immune processes, and metabolic processes; (b) validated or strongly evidenced targets reported in literature, patents, and clinical / pharmacological studies; (c) known drug target databases or drug-target association knowledge bases; (d) differential analysis of omics data such as protein-protein interaction networks, transcriptome / proteome / metabolome; and (e) a set of candidate targets based on computational predictions (e.g., network pharmacology, machine learning, or structural prediction).

[0062] The target panel can be 2 to 200 types, preferably 4 to 96 types; different sub-panels (such as inflammatory factor sub-panels, receptor sub-panels, key enzyme sub-panels) can be set according to the number of parallel channels in BLI and the research objectives, and internal consistency assessment can be achieved through repeated channel settings.

[0063] Range-based expression of dynamic gating parameters (R0 / P0 / F0 / C0) To accommodate different BLI platforms, different bait molecules, and different natural product / traditional Chinese medicine extract matrices, this invention expresses the kinetic gating parameters in a range-based manner. Without limiting the scope of this invention, the gating parameters can be set within the following ranges and can be adaptively adjusted based on noise levels, sample viscosity / refractive changes, bait fixation amount, and repeatability: (1) Response threshold R0: refers to the corrected response value at the end of the binding phase (unit nm or equivalent response unit), which can be selected in the range of 0.02 to 5.00 nm, preferably 0.05 to 2.00 nm.

[0064] (2) Dissociation residual ratio threshold P0: refers to the ratio of the residual response at the preset time point t0 of the dissociated phase to the terminal response of the bound phase (expressed as %), and the selectable range is 5% to 90%, preferably 10% to 80%; the selectable range of t0 is 30 to 600 s, preferably 60 to 300 s.

[0065] (3) Fitting goodness threshold F0: refers to the goodness of fit index obtained by fitting the corrected curve according to the selected dynamic model; when expressed as the coefficient of determination R², F0 can be selected in the range of 0.85 to 0.999, preferably 0.90 to 0.99.

[0066] (4) Repeatability threshold C0: refers to the consistency constraint of the same decoy molecule in repeated channels or repeated batches; when expressed as the coefficient of variation CV, C0 can be selected in the range of 5% to 50%, preferably 10% to 40%.

[0067] The above R0 / P0 / F0 / C0 can be used in combination. Meeting at least two of them is sufficient to output a candidate target. Preferably, meeting three or more of them is required to improve specificity. This invention can also construct a confidence score based on indicators such as R0, P0, F0, and CV, sort the candidate targets, and output a Top-N candidate target list.

[0068] Example 1: Top-N Output of Candidate Targets for Natural Products / Traditional Chinese Medicine Extracts (1) Target panel construction: Taking inflammation-related mechanisms as an example, candidate target proteins TNF-α, COX-2, and IL-6 were selected as bait molecules; a blank sensor channel was set up as a reference. The bait molecules TNF-α, COX-2, and IL-6 were immobilized on the sensor, as detailed in the following section. Figure 1 .

[0069] (2) Parallel fishing and collection: The sensor array is placed in the same solution of natural product or Chinese medicine extract (e.g., TCM-Extract-001) to collect the binding phase curve, and then moved to the buffer well to collect the dissociation phase curve.

[0070] (3) Data correction: Perform dual reference subtraction and drift correction; and normalize according to the bait fixation or baseline noise.

[0071] (4) Kinetic Gating and Target Output: A threshold is selected within the range of R0 / P0 / F0 / C0 for gating discrimination. The confidence score is calculated and the decoy channels that pass the gating are sorted, and a Top-N candidate target list is output. Among them, the candidate target is defined as "the target protein corresponding to the decoy channel that passes the gating"; channels that do not pass the gating are not output as candidate targets.

[0072] Example 2: Enrichment through cyclic fishing to improve target detection stability Based on Example 1, a cyclic enrichment procedure was employed: a cycle consisting of the bound phase and the dissociated phase, with the number of cycles n ranging from 2 to 50 (e.g., 30 cycles). By comparing the gating pass rate and repeatability at different cycle counts, a suitable cyclic strategy for the target sample can be determined. Cyclic enrichment can improve the stability and repeatability of candidate target output in complex matrix backgrounds. See details... Figure 2 .

[0073] Example 3: Elution and recovery of candidate target channels and identification of binding molecules After cleaning the sensor surface of the channel corresponding to the Top-N candidate targets in Example 1, elution and recovery were performed using a micro-elution buffer. The elution system can be a system containing organic solvents and / or an acidic system, wherein the volume fraction of the organic solvent is 10% to 90%, and the elution volume is 5 to 100 μL. For example, elution can be performed using 50% methanol-water solution with the addition of 0.1% acid, and the elution volume is 10 to 50 μL.

[0074] The eluent was collected and subjected to downstream identification processes such as LC-MS / MS to determine the identity of the bound molecules. The identified bound molecules were then matched with the channel information of candidate targets to establish a "candidate target-bound molecule" correspondence, providing a basis for subsequent functional verification, lead compound screening, and mechanism studies.

[0075] A series of components were successfully identified through comparison with compound databases and secondary mass spectrometry analysis: TNF-α sensor: caffeic acid, protohematoxylin B, syringic acid, catechin, vanillin, salicylic acid, proanthocyanidin A2, vanillin, glycyrrhizin, 3-O-caffeoyl-4-O-sinioylquinic acid, magnoflorine, 4-O-feruloylquinic acid, 3-p-coumarylquinic acid, fraxin, daidzein, styracin, 3-indoleacrylic acid, dihydroquercetin; COX-2 sensor: Citric acid, betaine, 4-aminophenol, dihydroquercetin, sinomenine, gallic acid, erythrine, sinomenine, scopolamine, succinic acid, o-methoxybenzoic acid, sinapic acid, magnoflorine, Siberian polygalactosyl glycoside A1, phenylacetaldehyde, genipin, ligustrazine H, dihydrocaffeic acid, succinate-9'-O-glucoside, safrole, 3,5-dimethoxybenzoic acid, curcuminone diol, succinate, citric acid; IL-6 sensor: Citric acid, betaine, protohematoxylin B, 4-aminophenol, erythrine, scopolamine, succinic acid, o-methoxybenzoic acid, sinapic acid, Siberian polygalactosyl glycoside A1, phenylacetaldehyde, genipin, ligustrol H, dihydrocaffeic acid, senna resin phenol-9'-O-glucoside, safrole, 3,5-dimethoxybenzoic acid, proanthocyanidins A2, isochlorogenic acid B, eleutheroside E, isochlorogenic acid C, curcuminone diol, strophanthidin, strophanthidin, strophanthidin, strophanthidin, o-hydroxycinnamic acid (o-coumaric acid), glycyrrhetinic acid.

[0076] Example 4: Verification of fishing results (using TNF-α target sensor as an example) Kinetic analysis of the fish caught using a sensor immobilized with TNF-α successfully screened out components that specifically bind to the TNF-α target. See the results below. Figures 3-6 .

[0077] Figure 3 : The binding and dissociation curves of catechins and TNF-α.

[0078] Figure 4 : Binding and dissociation curves of proanthocyanidin A2 and TNF-α.

[0079] Figure 5 : The binding and dissociation curves of daidzein and TNF-α.

[0080] Figure 6 : The binding and dissociation curves of magnoflorine and TNF-α.

[0081] SPR was used to further verify the molecular interactions of catechins, daidzein, proanthocyanidins A2, and magnoflorine with TNF-α. Specific results can be found in [link to results]. Figures 7-10 .

[0082] Figure 7 SPR assay was used to detect the binding of catechins to TNF-α.

[0083] Figure 8 SPR assay was used to detect the binding of genistein to TNF-α.

[0084] Figure 9 SPR detection of the binding of proanthocyanidin A2 to TNF-α.

[0085] Figure 10 SPR assay detects the binding of magnoflorine to TNF-α.

[0086] CETSA was used to detect the binding affinity of catechins, daidzein, proanthocyanidins A2, and magnoflorine to TNF-α. See the results below. Figures 11-14 .

[0087] Figure 11 CETSA was used to detect the binding ability of catechins to TNF-α.

[0088] Figure 12 CETSA was used to detect the binding ability of genistein to TNF-α.

[0089] Figure 13 CETSA was used to detect the binding ability of proanthocyanidin A2 to TNF-α.

[0090] Figure 14 CETSA was used to detect the binding ability of magnoflorine to TNF-α.

[0091] The anti-TNF-α activities of catechins, daidzein, proanthocyanidins A2, and magnoflorine were investigated in cells using the NF-κB-RE-luc2P luciferase reporter gene assay. Specific results are detailed in […]. Figure 15 .

[0092] The anti-TNF-α activities of catechins, genistein, proanthocyanidins A2, and magnoflorine were investigated in a TNF-α-induced L929 cell injury model. Specific results can be found in [link to results]. Figure 16 .

[0093] The effects of catechins, daidzein, proanthocyanidins A2, and magnoflorine on TNF-α-induced migration rate of MH7A cells are detailed in the table below. Figures 17-20 .

[0094] Figure 17 Catechins reduce TNF-α-induced migration rate of MH7A cells.

[0095] Figure 18 Soybean flavonoids reduce TNF-α-induced migration rate of MH7A cells.

[0096] Figure 19 Proanthocyanidin A2 reduces TNF-α-induced migration rate of MH7A cells.

[0097] Figure 20 Magnolia alkaloids reduce TNF-α-induced migration rate of MH7A cells.

[0098] The effects of catechins, daidzein, proanthocyanidins A2, and magnoflorine on the expression of inflammatory factors in TNF-α-induced MH7A cells are detailed in the table below. Figures 21-24 .

[0099] Figure 21 Effects of catechins on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0100] Figure 22 Effects of stigmata-2 on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0101] Figure 23 Effects of proanthocyanidin A2 on the expression of inflammatory factors in TNF-α-induced MH7A cells.

[0102] Figure 24 Effects of magnoflorine on TNF-α-induced expression of inflammatory factors in MH7A cells.

[0103] The effects of catechins, daidzein, proanthocyanidins A2, and magnoflorine on TNF-α-induced nuclear translocation in MH7A cells are detailed in the following results. Figures 25-28 .

[0104] Figure 25 Effects of catechins on TNF-α-induced nuclear translocation in MH7A cells.

[0105] Figure 26 Effects of stigmataein on TNF-α-induced nuclear translocation in MH7A cells.

[0106] Figure 27 Effects of proanthocyanidin A2 on TNF-α-induced nuclear translocation in MH7A cells.

[0107] Figure 28 Effects of magnoflorine on TNF-α-induced nuclear translocation in MH7A cells.

[0108] The effects of catechins, daidzein, proanthocyanidins A2, and magnoflorine on TNF-α-induced NF-κB signaling pathway expression in MH7A cells are detailed in the table below. Figures 29-32 .

[0109] Figure 29 Effects of catechins on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0110] Figure 30 Effects of stigmata-2 on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0111] Figure 31 Effects of proanthocyanidin A2 on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0112] Figure 32Effects of magnoflorine on TNF-α-induced NF-κB signaling pathway expression in MH7A cells.

[0113] The effects of magnoflorine on an LPS-induced acute inflammation mouse model are detailed in the following results. Figure 33 .

[0114] The effects of magnoflorin on a DSS-induced inflammatory bowel disease mouse model are detailed in the following results. Figure 34 .

[0115] The effects of magnoflorine on an IMQ-induced mouse model of psoriasis are detailed in the following results. Figure 35 .

[0116] Finally, it should be noted that the above embodiments are merely representative examples of the present invention. Obviously, the technical solution of the present invention is not limited to the above embodiments, and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should be considered within the scope of protection of this invention.

Claims

1. A multi-target fishing screening and recovery identification method for the discovery of targets in natural products or traditional Chinese medicine extracts, characterized in that, Includes the following steps: (1) Construction of target panel and probe array: Construct a target panel related to the target disease or biological process, fix at least two different decoy molecules in the panel to different channels of the biological layer interference sensor array to form decoy channels, and set at least one reference channel; (2) Parallel fishing and dynamic acquisition: The sensor array is placed in the same solution of the natural product or Chinese medicine extract to be tested, and the binding phase curve of each channel is acquired in real time and in parallel. After the sample is removed, the dissociation phase curve is acquired to obtain the dynamic data of each bait channel and the reference channel. (3) Data correction: The dynamic data is subjected to at least one reference subtraction and drift correction and normalization to obtain the corrected channel response curve; (4) Dynamic gating discrimination and target output: Based on the preset dynamic gating criteria, the corrected channel response curve is discriminated, the set of decoy channels that pass the gating is output and a confidence score is generated to form a candidate target list; (5) Recovery and downstream identification: The sensor surface of the channel corresponding to the candidate target is cleaned and a small amount of the conjugate is eluted and recovered. The eluted and recovered conjugate is then subjected to downstream identification analysis to determine the identity of the conjugate molecule and establish the correspondence between "candidate target - conjugate molecule".

2. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, The target panel in step (1) is determined by at least one of the following information sources: disease-related pathways or biological processes, literature and patent reports, known drug target databases, protein interaction networks or omics data, and a set of candidate targets obtained based on computational prediction; the target panel contains 2 to 200 decoy molecules.

3. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, The reference channel is any one or a combination of the following: a blank sensor channel, a channel for immobilizing irrelevant proteins / nucleic acids, a channel for immobilizing only the carrier / tag protein, or a buffer baseline channel that does not contact the sample; the reference subtraction includes at least single reference subtraction or double reference subtraction.

4. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, The normalization process includes at least one of normalizing the channel response based on the decoy fixation, baseline noise, or initial response amplitude.

5. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, The kinetic gating criterion includes at least two or more of the following discrimination indicators: a) the terminal response value of the bound phase exceeds a preset threshold R0; b) the residual proportion of the dissociated phase at a preset time point t0 exceeds a preset threshold P0; c) The goodness of fit of the kinetic model meets the preset threshold F0; d) The consistency of the response of the same decoy molecule in the repeat channel meets the preset threshold C0; e) Under the condition of competitive addition of a known ligand / inhibitor or deactivation / denaturation control, the response reduction meets the preset threshold.

6. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 5, characterized in that, The R0 ranges from 0.02 to 5.00 nm; t0 ranges from 30 to 600 s; P0 ranges from 5% to 90%; when the goodness of fit is expressed as the coefficient of determination R², F0 ranges from 0.85 to 0.999; when the repeatability is expressed as the coefficient of variation CV, C0 ranges from 5% to 50%.

7. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, Step (2) employs a cyclic fishing enrichment process, which includes at least 2 to 50 cycles of "combining-disintegrating-recombining".

8. The multi-target fishing screening and recovery identification method for target discovery of natural products or traditional Chinese medicine extracts according to claim 1, characterized in that, The decoy molecule is selected from any one or more of proteins, antibodies, antigens, receptor proteins, enzymes, nucleic acids, nucleic acid aptamers, polypeptides, or tag fusion proteins; its immobilization method is selected from any one or more of streptavidin-biotin immobilization, Ni-NTA / His tag capture, antibody capture, and amino / thiol covalent coupling.

9. A multi-target fishing detection system for performing the method according to any one of claims 1 to 12, characterized in that, include: A) A biolayer interferometer main unit with at least four parallel detection channels; B) A sensor array and probe construction assembly for fixing at least two types of decoy molecules in the target panel to different sensor surfaces and setting up reference channels; C) Program control module, used to execute fishing programs including bound and dissociated phases and optional cyclic fishing enrichment programs; D) Data processing module, used to perform reference subtraction, drift correction and normalization on data from each channel, and output a list of candidate targets according to preset kinetic gating criteria; E) Micro-recovery component or interface, used to clean and elute the sensor surface of the candidate target channel and collect the eluted material for downstream identification.

10. A computer-readable storage medium having a computer program stored thereon, the program, when executed by a processor, performing the data correction and dynamic gating discrimination and candidate target list output of steps (3) and (4) of the method of claim 1.

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