Soybean seed proteome identification method and application

Abstract

The invention relates to the technical field of proteomics analysis, and particularly discloses a soybean seed proteome identification method and application. The soybean seed proteome identification method comprises the following steps: (1) mixing extracted protein of a soybean seed to be detected with protamine, and removing precipitate to obtain a sample to be subjected to enzymolysis; (2) performing enzymolysis on the sample to be subjected to enzymolysis to obtain a peptide fragment; (3) fractionating the peptide fragment by using a C18-Tip scheme, an SDB-RPS-Tip scheme and an SCX-Tip scheme, and then carrying out mass spectrometric detection to obtain a soybean seed proteome spectrogram library; (4) acquiring mass spectrum data of the peptide fragment acquired in the step (2) by using a BoxCar mass spectrum acquisition strategy; and (5) analyzing the BoxCar mass spectrum data obtained in the step (4) by using MaxQuant in combination with the soybean seed proteome spectrum library obtained in the step (3). The method provided by the invention can improve the identification depth, and is beneficial to research of soybean seed protein.

Description

Technical Field

[0001] The invention relates to the technical field of proteomics analysis, and in particular to a method and application for identifying soybean seed proteome. Background Art

[0002] Soybean seeds contain 18%-20% oil and 38%-56% plant protein, which can be used for human food and animal feed. With the growth of population, the demand for oil and protein is increasing. Therefore, improving the oil and protein content of soybean seeds is one of the goals of breeding research. In recent years, with the rapid development of mass spectrometry technology, mass spectrometry-based proteomics technology has been increasingly used in plant research. The analysis of soybean seed proteome has made significant progress. However, since 60-70% of the proteins in soybean seeds are storage proteins, the presence of high-abundance proteins will inhibit the identification of low-abundance proteins, which poses a challenge to the depth of soybean seed proteome identification. Proteomics usually uses data-dependent acquisition (DDA) mode for mass spectrometry analysis. In short, proteins are first digested into peptides using specific proteases, and then enter the mass spectrometer for primary mass spectrometry analysis after liquid phase separation and electrospray ionization to obtain the mass-to-charge ratio and signal intensity of the peptides. Subsequently, in one detection cycle, the "Top N" rule is used to select precursor ions with higher signal intensity for secondary mass spectrometry analysis to determine the amino acid fragment information of the peptides. The "Top N" rule limits the sensitivity and stability of mass spectrometry detection, resulting in a large number of missing values ​​in the identification results and biasing quantification towards high-abundance peptides. Therefore, for proteome analysis of samples with a high dynamic range (such as soybean seeds), the data-dependent acquisition (DDA) mass spectrometry data acquisition strategy is flawed. Therefore, it is necessary to further study how to improve the identification depth of soybean seed proteome. Summary of the invention

[0003] One of the objectives of the present invention is to provide a method for improving the depth of soybean seed proteome identification.

[0004] The present invention provides a method for identifying soybean seed proteome, comprising: (1) After mixing the extracted soybean seed protein with protamine, the precipitate is removed to obtain a sample to be hydrolyzed; (2) using Trypsin to enzymatically hydrolyze the sample to be hydrolyzed to obtain peptide fragments; (3) using C18-Tip, SDB-RPS-Tip and SCX-Tip schemes to fractionate the peptides, combining the fractions obtained by the three schemes and performing mass spectrometry detection to obtain a soybean seed proteome spectral library; (4) using the BoxCar mass spectrometry acquisition strategy to acquire the mass spectrometry data of the peptides obtained in step (2); (5) Using MaxQuant to combine the soybean seed proteome spectral library obtained in step (3) with the BoxCar mass spectrometry data obtained in step (4).

[0005] The present invention has found that protamine can effectively remove storage proteins and, to a certain extent, can effectively reduce the inhibitory effect of high-abundance peptides on low-abundance peptides in mass spectrometry. In addition, the present invention has also found that the BoxCar mass spectrometry acquisition strategy can improve the identification depth of soybean seed proteome. For the Obitrap mass spectrometer, the precursor ions need to be aggregated in the C-Trap and reach the maximum capacity before entering the Orbitrap for detection before the primary mass spectrometry detection. It takes 1% of the transient time (128 ms) to generate a high-resolution mass spectrum, while for the current conventional proteomics analysis, the C-Trap (maximum charge capacity of about 1 million) can be filled in <1 ms, so more than 99% of the ions entering the mass spectrometer are not used for mass spectrometry analysis. The BoxCar mass spectrometry acquisition strategy uses segmented accumulation during the primary scan and uses multiplexed full scans to avoid signal suppression of low-abundance peptides by high-abundance peptides in the same full scan, thereby improving the dynamic range of the primary scan and the sensitivity of triggering the secondary scan.

[0006] The present invention combines protamine to remove storage proteins with the BoxCar mass spectrometry acquisition scheme, optimizes the soybean seed proteome mass spectrometry analysis scheme from two aspects of sample preparation and mass spectrometry analysis, thereby achieving the purpose of improving the identification depth of the soybean seed proteome.

[0007] The soybean seed protein of the present invention can be extracted by methods known in the art.

[0008] In step (1) of the method of the present invention, in the system after the protein of the soybean seeds to be tested is mixed with protamine, the final concentration of protamine is 0.045-0.055% (W / V), preferably 0.05% (W / V).

[0009] In step (1) of the method of the present invention, the precipitate is removed by centrifugation, and the centrifugation conditions are: 4°C, 12050-12100 r / min, 9-11 min; preferably, the centrifugation conditions are: 4°C, 12084 r / min, 10 min.

[0010] The mass spectrometry detection in step (3) of the method of the present invention adopts a DDA mass spectrometry acquisition strategy; Preferably, the DDA mass spectrometry acquisition strategy in step (3) includes: peptide separation and DDA mass spectrometry detection; the BoxCar mass spectrometry acquisition strategy in step (4) includes: peptide separation and BoxCar mass spectrometry detection; The parameters of the DDA mass spectrometry detection are as follows: both MS and MS / MS use Orbitrap as the mass analyzer; the full scan range is set to 350-1550 m / z, the resolution is 120000; the automatic gain control of the full scan is set to 4×10 5 , the maximum ion injection time was 50 ms; precursor ions were selected from each full scan with an isolation width of 1.6 m / z and fragmented in a multistage ion trap with a normalized collision energy of 30%; the resolution of MS2 was 15000, and the AGC was set to 5×10 4 , MIT is 30 ms; dynamic exclusion of precursor ions is enabled and lasts for 18 s; The parameters of the BoxCar mass spectrometry detection are as follows: both MS and MS / MS are measured using an Orbitrap mass spectrometer; the full scan range is set to 350-1650 m / z, the resolution is 240000; for the full scan, the AGC is set to 1×10 6 MIT is set to Aotu; BoxCar scan is set to 3, Box is set to 10, and box parameter information is shown in Table 1; adjacent boxes overlap 1th; MS2 resolution is 15000, AGC is set to 5×10 4 , MIT was set to Dynamic; precursor ions were selected with an isolation width of 1.4 m / z and fragmented in a multistage ion trap with a normalized collision energy of 27%; dynamic exclusion of precursor ions was enabled with a duration of 30 s.

[0011] When constructing the soybean seed proteome spectral library in step (3) of the method of the present invention, in addition to the mass spectrum information of the protein of the soybean seeds to be tested, the mass spectrum information of the protein of other different varieties of soybean seeds is also included.

[0012] When performing soybean seed proteome identification, the present invention can use only the soybean seeds to be tested to construct the soybean seed proteome spectral library for subsequent BoxCar mass spectrometry data analysis. It is preferred to mix soybean seeds of other varieties for library construction and analysis, so as to obtain a more comprehensive soybean seed proteome spectral library, which is beneficial to the subsequent BoxCar data analysis.

[0013] The mass spectrometry information of proteins from other different varieties of soybean seeds is obtained in the same manner as the mass spectrometry information of proteins from the soybean seeds to be tested (see steps (1)-(3)); Preferably, the peptide segments of proteins from soybean seeds of other different varieties are mixed with the peptide segments of proteins from the soybean seeds to be tested, and then fractionated and mass spectrometrically detected in the manner described in step (3) to obtain a soybean seed proteome spectrum library.

[0014] In step (2) of the method of the present invention, the enzymatic hydrolysis is performed using filter-aided sample preparation (FASP).

[0015] In the method of the present invention, the operation steps of the filter-assisted sample preparation technique include: 1) The sample to be hydrolyzed is centrifuged using a 30 kDa ultrafiltration tube, and after removing the waste liquid, it is washed with a Tris-HCl solution containing urea; 2) Incubate with a mixed solution of tri(2-carboxyethyl)phosphine and chloroacetamide; 3) Rinse with Tris-HCl solution and NH4HCO3 solution containing urea in sequence; 4) Mix with trypsin and incubate; 5) Centrifuge and dry to collect peptide fragments.

[0016] In the method of the present invention, the urea-containing Tris-HCl solution is obtained by mixing urea with a Tris-HCl solution, the concentration of the Tris-HCl solution is 100 mM, the pH is 8.0, and the concentration of urea in the urea-containing Tris-HCl solution is 8 M; In the mixed solution of tri(2-carboxyethyl)phosphine and chloroacetamide, the concentration of tri(2-carboxyethyl)phosphine is 5 mM, and the concentration of chloroacetamide is 25 mM; the incubation condition of step 2) is: 1 hour at room temperature; The concentration of the NH4HCO3 solution is 50 mM; the incubation conditions in step 4) are: 37°C, 16 hours; The centrifugal conditions are: 13000-13100 r / min, 14-16 min; preferably, 13053 r / min, 15 min.

[0017] In the method of the present invention, the C18-Tip fractionation method is: placing C18 Beads in a Tip equipped with a C8 membrane, flushing the C18-Tip with acetonitrile and then filling it with acetonitrile, and standing at room temperature for 2 hours; centrifuging to remove the acetonitrile, then using NH3·H2O to clean the C18-Tip, and then transferring the peptides dissolved in NH3·H2O to the C18-Tip, centrifuging to remove the waste liquid, and then adding NH3·H2O for desalting, and finally using ACN-NH3·H2O solution with acetonitrile concentrations of 6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, 35% and 50% to gradiently elute the peptides; the pH of NH3·H2O is 10.

[0018] In the method of the present invention, the SCX-Tip fractionation method is as follows: the peptide segment is dissolved in 1% TFA and placed in a 100 μL Tip equipped with 5 layers of SCX membrane with a pore size of 80 Å, after centrifugation to remove the waste liquid, the peptide segment is washed with 0.2% TFA, and then, the peptide segment is eluted in sequence with 50 mM ammonium acetate / 20% acetonitrile / 0.5% formic acid, 75 mM AA / 20% ACN / 0.5% FA, 125 mM AA / 20% ACN / 0.5% FA, 200 mM AA / 20% ACN / 0.5% FA, 300 mM AA / 20% ACN / 0.5% FA and 80% ACN / 5% NH3·H2O.

[0019] In the method of the present invention, the SDB-RPS-Tip fractionation method is as follows: take 5 layers of SDB-RPS membrane with a pore size of 80 Å in 100 μL Tip, dissolve the peptides with 0.1% TFA and transfer them to the SDB-RPS-Tip column, centrifuge to remove the waste liquid, and wash once with 0.2% TFA; then, elute the peptides with 100 mM ammonium formate / 40% ACN / 0.5% FA, 150 mM AF / 60% ACN / 0.5% FA, and 80% ACN / 5% NH3·H2O in sequence.

[0020] The present invention uses C18-Tip, SDB-RPS-Tip and SCX-Tip schemes to fractionate peptide segments for establishing a soybean seed proteome spectrum library.

[0021] The present invention also provides application of the above method in improving the identification depth of soybean seed proteome.

[0022] The beneficial effects of the present invention are at least: The present invention can improve the soybean seed proteome identification depth, identify more soybean seed proteomes, and facilitate soybean seed proteome analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The results of SDS-PAGE analysis of soybean seed proteome before (control group) and after (experimental group) treatment with protamine.

[0024] Figure 2 The results of soybean seed proteome analysis before and after protamine treatment. A. Statistical results of the number of soybean seed proteome identifications before and after protamine treatment; B. Intersection analysis results of the identified proteomes in the experimental group and the control group; C. Abundance ranking analysis results of the identified proteomes in the experimental group and the control group.

[0025] Figure 3The results of the proteome database of high-oil and high-protein soybean seeds are shown in Figure 1. A. The intersection analysis results of the proteome identified by the three fractionation strategies; B. The intersection analysis results of the peptides identified by the three fractionation strategies; C. The linear correlation results of the three fractionation strategies based on the quantitative information of the identified proteome; D. The linear correlation results of the three fractionation strategies based on the quantitative information of the identified peptides.

[0026] Figure 4 The results of proteome analysis of high-oil and high-protein soybean seeds. A. The number of peptides identified by DDA and BoxCar in high-oil and high-protein soybean seeds; B. The number of proteomes identified by DDA and BoxCar in high-oil and high-protein soybean seeds; C. The intersection analysis results of peptides identified by DDA and BoxCar in high-oil and high-protein soybean seeds; D. The intersection analysis results of proteomes identified by DDA and BoxCar in high-oil and high-protein soybean seeds.

[0027] Figure 5 Comparative analysis results of soybean seed proteome identification by BoxCar and DDA. A. Amino acid sequence coverage analysis results of proteome identification by DDA and BoxCar in high-oil and high-protein soybean seeds; B. Frequency analysis results of proteome identification by DDA and BoxCar in high-oil and high-protein soybean seeds; C. Coefficient of variation analysis results of proteome identification by DDA and BoxCar in high-oil and high-protein soybean seeds; D. Scatter plot of abundance ranking of proteome identification by DDA and BoxCar in high-oil and high-protein soybean seeds. DETAILED DESCRIPTION

[0028] The preferred embodiments of the present invention will be described in detail below in conjunction with examples. It should be understood that the following examples are provided only for the purpose of illustration and are not intended to limit the scope of the present invention. Those skilled in the art may make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0029] The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, etc. used in the following examples are commercially available or prepared according to conventional methods in the art unless otherwise specified.

[0030] Example 1 Study on the effect of protamine on the removal of storage protein In this example, two groups were set up, including the control group without removing storage protein and the experimental group with protamine, and the experiment was repeated three times for each group. The control group used liquid nitrogen grinding and acetone precipitation (-20°C, 2 h) to extract soybean seed protein. In the experimental group, protamine (final concentration 0.05% (W / V)) was added to the soybean seed protein extracted by liquid nitrogen grinding and acetone precipitation in the same way, and placed in a 4°C centrifuge at 12084 r / min for 10 min to remove storage protein. 20 μg of soybean seed protein was taken from the control group and the experimental group respectively, and supplemented with 3.6 μL 5× loading buffer (1 M Tris-HCl (pH 6.8), sodium dodecyl sulfate (SDS), bromophenol blue, glycerol, 5M DTT), 1 μL β-mercaptoethanol and ddH2O (a total of 20 μL of protein and loading solution), and heated at 90°C for 10 min to denature the protein; then, it was transferred to SDS-PAGE with a stacking gel of 5% and a separation gel of 12%, and the stacking gel voltage was set to 80 V for 20 min, and the separation gel voltage was set to 120 V for 100 min. After SDS-PAGE was completed, it was stained with 12.5% ​​Coomassie brilliant blue staining solution (containing 30% methanol and 10% acetic acid) for 30 min, and then destained with a decolorizing solution until the bands were completely clear (ethanol: acetic acid: water = 20:10:70). The results are as follows Figure 1 As shown, the amount of high-abundance proteins decreased sharply after protamine treatment.

[0031] Example 2 Comparative analysis of soybean seed proteomes with and without storage protein removal In this example, soybean seed proteins prepared according to the method of Example 1 with or without storage protein removed (experimental group) and without storage protein removed (control group) were respectively taken, and peptides were obtained using the FASP enzyme digestion scheme. The detailed operation is as follows: 500 μg of protein was taken from each of the control group and the experimental group in a 30 kDa ultrafiltration tube, and the experiment was repeated three times for each group. The waste liquid was removed by centrifugation, and 200 μL of 8 M urea solution (prepared with 100 mM Tris-HCl, pH = 8.0, hereinafter referred to as UA) was added to wash the protein; then, a mixed solution with a final concentration of 5 mM tris(2-carboxyethyl)phosphine (TCEP) and 25 mM chloroacetamide (CAA) was added, and incubated at room temperature for 1 h. After the incubation was completed, the protein was washed with 8 M UA and 50 mM NH4HCO3 solutions in turn. Subsequently, 20 μg of trypsin was added and incubated in a 37°C incubator for 16 h. After incubation, the peptides were collected by centrifugation and dried in a vacuum centrifugal concentrator; the dried peptides were stored at -80 °C for mass spectrometry analysis. In all centrifugation steps throughout the experiment, the centrifugation conditions were set to 13053 r / min and the duration was 15 min. 1 μg of peptides were taken from each sample for mass spectrometry analysis, and the acquisition scheme was data dependent acquisition (DDA). DDA mass spectrometry data were analyzed using MaxQuant software (default parameters).

[0032] The results are as follows Figure 2 shown. Figure 2 A in the figure is the statistical result of the number of protein groups identified in soybean seeds before and after protamine treatment. It can be seen that the average number of protein groups identified in each repeated experiment in the protamine treatment group (experimental group, A) is 1360, and the average number of protein groups identified in each repeated experiment in the group not treated with protamine (control group, B) is 628. The number of protein groups identified in the experimental group is 2.17 times that of the control group, and there is a significant difference between the two groups. Figure 2 B in the figure is the intersection analysis result of the identified protein groups of the experimental group and the control group. It can be seen that only 64 proteins in the control group were not identified in the experimental group, accounting for 7.05% of the number of proteins identified in the control group. Figure 2 C in the figure is the abundance ranking analysis result of the identified proteomes in the experimental group and the control group. It can be seen that the experimental group can identify more proteins with relatively low abundance. The above results show that protamine can effectively remove storage proteins in soybean seeds and improve the identification results of the proteome.

[0033] Example 3 Establishing a soybean seed proteome mass spectrometry library This example uses protamine to remove storage proteins from high-oil and high-protein soybean seed proteins (see Example 1 for specific methods) as samples for subsequent research. The high-oil soybean seeds are Huaxia No. 2, with a crude protein content of 41.66% and a crude fat content of 21.91%. The high-protein soybean seeds are Huaxia 14, with a crude protein content of 45.40% and a crude fat content of 18.35%.

[0034] The treated high-oil and high-protein soybean seed proteins were hydrolyzed using the FASP enzymatic cleavage scheme (see Example 2 for the specific method), and then the peptides from the two varieties of soybean seeds were mixed in a 1:1 ratio. The mixed peptides were fractionated using three fractionation schemes (C18-Tip, SCX-Tip and SDB-RPS-Tip), and the fractions obtained from each scheme were analyzed by mass spectrometry using the DDA mass spectrometry acquisition strategy.

[0035] C18-Tip fractionation scheme. First, weigh 10 mg of C18 beads and transfer them to a 100 μL Tip equipped with a layer of C8 membrane. Then, rinse the C18-Tip three times with acetonitrile, fill it with acetonitrile for the last time, and let it stand at room temperature for 2 h. Centrifuge to remove acetonitrile, and then wash the C18-Tip with NH3·H2O (pH=10). Then, transfer the peptides dissolved in NH3·H2O (pH=10) to the C18-Tip. Centrifuge to remove the waste liquid, and then add NH3·H2O (pH=10) for desalting. Finally, use X ACN-NH3·H2O (pH=10) solution (X=6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, 35% and 50%) for gradient elution of peptides.

[0036] SCX-Tip fractionation protocol. First, the sample was dissolved in 1% TFA and transferred entirely to a 100 μL Tip equipped with 5 layers of SCX membrane (pore size 80 Å). After centrifugation to remove waste liquid, the peptides were washed with 0.2% TFA. Then, the peptides were eluted sequentially with 50 mM ammonium acetate (AA) / 20% acetonitrile (ACN) / 0.5% formic acid, 75 mM AA / 20% ACN / 0.5% FA, 125 mM AA / 20% ACN / 0.5% FA, 200 mM AA / 20% ACN / 0.5% FA, 300 mM AA / 20% ACN / 0.5% FA, and 80% ACN / 5% NH3·H2O.

[0037] SDB-RPS-Tip fractionation scheme. First, take 5 layers of SDB-RPS membrane (pore size 80 Å) in a 100 μL Tip; dissolve the peptides with 0.1% TFA and transfer to the SDB-RPS-Tip column; centrifuge to remove the waste liquid, wash once with 0.2% TFA; then, elute the peptides with 100 mM ammonium formate (AF) / 40% ACN / 0.5% FA, 150 mM AF / 60% ACN / 0.5% FA, and 80% ACN / 5% NH3·H2O in sequence.

[0038] The samples obtained from each fractionation scheme were dried using a vacuum centrifugal concentrator and then stored in a −80 °C freezer for subsequent LC-MS / MS analysis.

[0039] LC-MS / MS parameter settings: All samples were analyzed by a LC-MS system consisting of an EASY nLC 1200 ultra-high pressure liquid phase system (ThermoFisher Scientific) and an Orbitrap Fusion Lumos Tribrid mass spectrometer (ThermoFisherScientific) connected via a Nano electrospray ion source. A capillary C18 column (100 μm inner diameter × 30 cm length) was used to separate peptides. Mobile phase A was 0.1% FA and mobile phase B was 0.1% FA / 80% ACN. The peptides were separated using a linear gradient of 4-90% ACN (0.1% FA) at a flow rate of 600 nL / min in 78 min. The gradient elution conditions of mobile phase B were set as follows: 0–5 min (4%–10% B), 5–48 min (10%–22% B), 48–66 min (22%–35% B), 66–76 min (35%–90% B), and 76–78 min (90% B).

[0040] Orbitrap was used as the mass analyzer for both the primary (MS) and secondary (MS / MS) analyses. The full scan range was set to 350–1550 m / z with a resolution of 120,000. The automatic gain control (AGC) for the full scan was set to 4 × 10 5 The maximum ion injection time (MIT) was 50 ms. Precursor ions were selected from each full scan with an isolation width of 1.6 m / z and fragmented in a multistage ion trap with a normalized collision energy of 30%. The resolution of MS2 was 15000 and the AGC was set to 5×10 4 , MIT was 30 ms. Dynamic exclusion of precursor ions was enabled with a duration of 18 s.

[0041] The raw mass spectrometry data were analyzed using MaxQuant (default parameters). The results of the high-oil and high-protein soybean seed proteome database are shown in Figure 3 , where A is the intersection analysis result of proteome identification using the three fractionation strategies; B is the intersection analysis result of peptide identification using the three fractionation strategies; C is the linear correlation result of the three fractionation strategies based on the quantitative information analysis of proteome identification; D is the linear correlation result of the three fractionation strategies based on the quantitative information analysis of peptide identification.

[0042] It can be seen that the three fractionation strategies identified a total of 4122 proteomes and 17960 peptides, of which 1983 proteomes were identified in common, accounting for 48.11%; the C18 group characterized 1155 proteomes, accounting for 28.02% of the total identified proteins in the three groups; the SCX and SDB-RPS groups characterized 107 and 51 proteomes, respectively, accounting for 3.84% of the total identified proteins in the three groups. 91.91% of the total identified proteins in the SDB-RPS and SCX groups can be identified in the C18 group ( Figure 3 At the peptide level, a total of 3070 (17.09%) peptides in the SDB-RPS and SCX groups were not identified in the C18 group ( Figure 3 B in the figure).

[0043] Correlation analysis between C18 and SCX / SDB-RPS groups, in terms of proteome 2 They are 0.81 / 0.77 ( Figure 3 C in the peptide segment 2 They are 0.67 / 0.65 ( Figure 3 D in Figure 1). The C18 group highly covers the proteomes identified by the SCX and SDB-RPS groups, but the SCX and SDB-RPS groups also provide additional peptide information. Therefore, all the raw files of the three fractions were merged to establish a soybean seed proteome database, which can provide more information for the subsequent BoxCar mass spectrometry data analysis.

[0044] Example 4 Comparative Analysis of DDA and BoxCar Methods In the comparative analysis of the DDA and BoxCar methods, this example uses the DDA and BoxCar methods to collect mass spectra of the peptide segments of the high-oil and high-protein soybean seed proteins prepared in Example 3, respectively.

[0045] Mass spectrometry acquisition parameter settings: All samples were analyzed by LC / MS system consisting of EASY nLC 1200 ultra-high pressure liquid chromatography system (ThermoFisher Scientific) and Orbitrap Fusion Lumos Tribrid mass spectrometer (ThermoFisher Scientific) connected by nano electrospray ionization source. Peptides were separated using a capillary C18 column (100 μm inner diameter × 30 cm length). Mobile phase A was 0.1% FA and mobile phase B was 0.1% FA / 80% ACN. Peptides were separated using a linear gradient of 4-90% ACN (0.1% FA) at a flow rate of 600 nL / min in 78 min. The gradient elution conditions of mobile phase B were set as follows: 0–5 min (4%–10% B), 5–48 min (10%–22% B), 48–66 min (22%–35% B), 66–76 min (35%–90% B), and 76–78 min (90% B).

[0046] DDA mass spectrometry parameter settings: Orbitrap was used as the mass analyzer for both the primary (MS) and secondary (MS / MS). The full scan range was set to 350-1550 m / z, and the resolution was 120,000. The automatic gain control (AGC) of the full scan was set to 4×10 5 The maximum ion injection time (MIT) was 50 ms. Precursor ions were selected from each full scan with an isolation width of 1.6 m / z and fragmented in a multistage ion trap with a normalized collision energy of 30%. The resolution of MS2 was 15000 and the AGC was set to 5×10 4 , MIT was 30 ms. Dynamic exclusion of precursor ions was enabled with a duration of 18 s.

[0047] BoxCar mass spectrometry parameter settings: Both MS and MS / MS were measured using an Orbitrap mass spectrometer. The full scan range was set to 350–1650 m / z with a resolution of 240,000. For the full scan, the AGC was set to 1 × 10 6 , MIT is set to Aotu. BoxCar scan is set to 3, Box is set to 10 (box parameter information is shown in Table 1). Adjacent boxes overlap 1 th. The resolution of MS2 is 15000, and AGC is set to 5×10 4 , MIT was set to Dynamic. Precursor ions were selected with an isolation width of 1.4 m / z and fragmented in a multistage ion trap with a normalized collision energy of 27%. Dynamic exclusion of precursor ions was enabled with a duration of 30 s.

[0048] Table 1 Information of 30 variable windows collected by BoxCar mass spectrometry MaxQuant software was used to process the raw mass spectrometry data obtained by each acquisition technology. The specific operation is as follows: For the information collected by BoxCar mass spectrometry, the DDA mass spectrometry data generated by "Establishing a soybean seed proteome mass spectrometry library" in Example 3 and the BoxCar mass spectrometry data of each soybean variety collected in this example were imported into MaxQuant at the same time; the DDA data was set to Group0, and the type was set to Standard; the BoxCar data was set to Group1, and the type was set to BoxCar; and Match between runs was checked to obtain the analysis results by comparing the total library data of Example 3 with the BoxCar data of each soybean variety obtained in this example. For the information collected by DDA mass spectrometry, it was directly imported into MaxQuant for analysis. In the analysis of the high-oil and high-protein groups, the BoxCar method was significantly superior to the DDA technology in the identification of peptides and proteins. Specifically, using the DDA method, an average of 4602 peptides and 1415 proteins were identified in each repeated experiment in the high-oil group; an average of 3683 peptides and 1155 proteins were identified in each repeated experiment in the high-protein group ( Figure 4 The BoxCar approach significantly increased the number of peptides and proteins identified in these groups. For example, in the high-oil group, BoxCar identified 5791 additional peptides and 1783 additional protein groups ( Figure 4 In the high-protein group, BoxCar identified 6239 additional peptides and 1872 proteins ( Figure 4 Overall, the BoxCar technique shows significant improvement over DDA.

[0049] Example 5 Evaluation of the stability of the DDA and BoxCar methods Amino acid sequence coverage can effectively reflect the accuracy and stability of protein identification. The comparative analysis results of soybean seed proteome identified by BoxCar and DDA are shown in Figure 5 , where A is the amino acid sequence coverage analysis result of DDA and BoxCar identifying proteomes in high-oil and high-protein soybean seeds; B is the frequency analysis result of DDA and BoxCar identifying proteomes in high-oil and high-protein soybean seeds; C is the coefficient of variation analysis result of DDA and BoxCar identifying proteomes in high-oil and high-protein soybean seeds; D is the abundance ranking scatter plot of DDA and BoxCar identifying proteomes in high-oil and high-protein soybean seeds. Figure 5As shown in A, in the high-oil group and high-protein group, the number of proteins with amino acid sequence coverage greater than 20% identified by the DDA and BoxCar methods were 488 and 777 (high-oil group), and 382 and 709 (high-protein group), respectively. Obviously, the DDA method is significantly lower than the BoxCar method.

[0050] In addition, this example performs statistical analysis on the protein identification frequency of DDA and BoxCar. Figure 5 As shown in Figure B, the BoxCar method not only ensures a high protein detection rate, but also maintains high identification stability. In five repeated experiments, BoxCar consistently identified a large number of proteins that were detected in all five trials. In the high-oil group, 1861 proteins were identified, while in the high-protein group, 1719 proteins were identified, which significantly exceeded the number identified by the DDA method.

[0051] In addition, this example also analyzes the coefficient of variation (CV) values ​​of proteins identified by the two methods. Figure 5 As shown in Figure C, for proteins with CV values ​​less than 5%, the BoxCar method identified 1930 proteins in high-oil seeds and 1759 proteins in high-protein seeds, which are much higher than the number of identifications achieved by the DDA method.

[0052] And the scatter plot of proteome abundance ranking identified by DDA and BoxCar in high-oil and high-protein soybean seeds ( Figure 5 From D) we can see that: 1. BoxCar identifies more proteomes than DDA; 2. BoxCar can identify more proteins with lower abundance.

[0053] Taken together, these results indicate that the BoxCar technology exhibits increased stability and offers significant advantages for proteomic analysis of soybean seeds.

[0054] Although the present invention has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements may be made thereto based on the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection claimed by the present invention.

Claims

1. A method for identifying soybean seed proteome, characterized in that: include: (1) After mixing the extracted soybean seed protein with protamine, the precipitate is removed to obtain a sample to be hydrolyzed; (2) using Trypsin to enzymatically hydrolyze the sample to be hydrolyzed to obtain peptide fragments; (3) using C18-Tip, SDB-RPS-Tip and SCX-Tip schemes to fractionate the peptides, combining the fractions obtained by the three schemes and performing mass spectrometry detection to obtain a soybean seed proteome spectral library; (4) using the BoxCar mass spectrometry acquisition strategy to acquire the mass spectrometry data of the peptides obtained in step (2); (5) Using MaxQuant to combine the soybean seed proteome spectral library obtained in step (3) with the BoxCar mass spectrometry data obtained in step (4).

2. The method according to claim 1, characterized in that In step (1), in the system after the protein of the soybean seeds to be tested is mixed with protamine, the final concentration of protamine is 0.045-0.055% (W / V), preferably 0.05% (W / V); And / or, remove the precipitate by centrifugation. The centrifugation conditions are: 4°C, 12050-12100 r / min, 9-11 min.

3. The method according to claim 1 or 2, characterized in that: The mass spectrometry detection in step (3) adopts a DDA mass spectrometry acquisition strategy; Preferably, the DDA mass spectrometry acquisition strategy in step (3) includes: peptide separation and DDA mass spectrometry detection; the BoxCar mass spectrometry acquisition strategy in step (4) includes: peptide separation and BoxCar mass spectrometry detection; The parameters of the DDA mass spectrometry detection were as follows: both MS and MS / MS used Orbitrap as the mass analyzer; the full scan range was set to 350-1550 m / z, the resolution was 120000; the full scan automatic gain control was set to 4×10 5 , the maximum ion injection time was 50 ms; precursor ions were selected from each full scan with an isolation width of 1.6 m / z and fragmented in a multistage ion trap with a normalized collision energy of 30%; the resolution of MS2 was 15000, and the AGC was set to 5×10 4 , MIT is 30 ms; dynamic exclusion of precursor ions is enabled and lasts for 18 s; The parameters of the BoxCar mass spectrometry detection are as follows: both MS and MS / MS are measured using an Orbitrap mass spectrometer; the full scan range is set to 350-1650 m / z, the resolution is 240000; for the full scan, the AGC is set to 1×10 6 MIT is set to Aotu; BoxCar scan is set to 3, Box is set to 10, and box parameter information is shown in Table 1; adjacent boxes overlap 1 th; MS2 resolution is 15000, AGC is set to 5×10 4 , MIT was set to Dynamic; precursor ions were selected with an isolation width of 1.4 m / z and fragmented in a multistage ion trap with a normalized collision energy of 27%; dynamic exclusion of precursor ions was enabled with a duration of 30 s.

4. The method according to any one of claims 1 to 3, characterized in that: When constructing the soybean seed proteome spectral library in step (3), in addition to the mass spectrum information of the protein of the soybean seeds to be tested, the mass spectrum information of the protein of other different varieties of soybean seeds is also included; The method for obtaining the mass spectrum information of proteins from other different varieties of soybean seeds is the same as the method for obtaining the mass spectrum information of proteins from the soybean seeds to be tested; Preferably, the peptide segments of proteins from soybean seeds of other different varieties are mixed with the peptide segments of proteins from the soybean seeds to be tested, and then fractionated and mass spectrometrically detected in the manner described in step (3) to obtain a soybean seed proteome spectrum library.

5. The method according to any one of claims 1 to 4, characterized in that: In step (2), the enzymatic hydrolysis is performed using a filter-assisted sample preparation technique; Preferably, the operation steps of the filter-assisted sample preparation technique include: 1) The sample to be hydrolyzed is centrifuged using a 30 kDa ultrafiltration tube, and after removing the waste liquid, it is washed with a Tris-HCl solution containing urea; 2) Incubate with a mixed solution of tri(2-carboxyethyl)phosphine and chloroacetamide; 3) Rinse with Tris-HCl solution and NH4HCO3 solution containing urea in sequence; 4) Mix with trypsin and incubate; 5) Centrifuge and dry to collect peptide fragments.

6. The method according to claim 5, characterized in that The urea-containing Tris-HCl solution is obtained by mixing urea with a Tris-HCl solution, the concentration of the Tris-HCl solution is 100 mM, the pH is 8.0, and the concentration of urea in the urea-containing Tris-HCl solution is 8 M; In the mixed solution of tri(2-carboxyethyl)phosphine and chloroacetamide, the concentration of tri(2-carboxyethyl)phosphine is 5 mM, and the concentration of chloroacetamide is 25 mM; the incubation condition of step 2) is: 1 hour at room temperature; The concentration of the NH4HCO3 solution is 50 mM; the incubation conditions in step 4) are: 37°C, 16 hours; The centrifugal conditions are: 13000-13100 r / min, 14-16 min.

7. The method according to any one of claims 1 to 6, characterized in that: The C18-Tip fractionation method is as follows: C18Beads are placed in a Tip equipped with a layer of C8 membrane, the C18-Tip is rinsed with acetonitrile and then filled with acetonitrile, and allowed to stand at room temperature for 2 h; the acetonitrile is removed by centrifugation, and the C18-Tip is cleaned with NH3·H2O, and then the peptides dissolved in NH3·H2O are transferred to the C18-Tip, the waste liquid is removed by centrifugation, and NH3·H2O is added for desalting, and finally the peptides are gradiently eluted with ACN-NH3·H2O solutions with acetonitrile concentrations of 6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, 35% and 50%; the pH of NH3·H2O is 10.

8. The method according to any one of claims 1 to 7, characterized in that: The SCX-Tip fractionation method is as follows: the peptides were dissolved in 1% TFA and placed in a 100 μL Tip equipped with 5 layers of SCX membrane with a pore size of 80 Å. After centrifugation to remove the waste liquid, the peptides were washed with 0.2% TFA, and then the peptides were eluted with 50 mM ammonium acetate / 20% acetonitrile / 0.5% formic acid, 75 mM AA / 20% ACN / 0.5% FA, 125 mM AA / 20% ACN / 0.5% FA, 200 mM AA / 20% ACN / 0.5% FA, 300 mM AA / 20%ACN / 0.5% FA and 80% ACN / 5% NH3·H2O in sequence.

9. The method according to any one of claims 1 to 8, characterized in that: The SDB-RPS-Tip fractionation method is as follows: take 5 layers of SDB-RPS membrane with a pore size of 80 Å in a 100 μL Tip, dissolve the peptides with 0.1% TFA and transfer them to the SDB-RPS-Tip column, centrifuge to remove the waste liquid, and wash once with 0.2% TFA; then, elute the peptides with 100 mM ammonium formate / 40% ACN / 0.5% FA, 150mM AF / 60% ACN / 0.5% FA, and 80% ACN / 5% NH3·H2O in sequence.

10. Use of the method according to any one of claims 1 to 9 in improving the depth of soybean seed proteome identification.

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