CN111220749A - Analysis method of O-linked glycopeptide - Google Patents
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Abstract
The invention relates to a novel method for identifying O-linked glycosylation. By carrying out computer simulation deglycosylation treatment on a glycopeptide mass spectrogram, the variable modification of an amino acid site level is improved to a peptide segment level, so that the variable modification of glycosylation is avoided during database retrieval, the problems of large retrieval space, long search time, high false positive rate and the like of the traditional method are solved, and the high-sensitivity identification of the O-linked glycopeptide is realized. The invention has the capability of analyzing the O-linked glycopeptide in a large scale and has important application potential in screening disease markers based on glycoprotein.
Description
Technical Field
The invention belongs to the field of glycosylated proteomics in the proteomics research direction, and particularly relates to a novel high-sensitivity analysis method for O-linked glycopeptide mass spectrum data.
Background
Glycosylation (glycosylation) is a very common post-translational modification of proteins, more than 50% of which expressed in cells are glycoproteins involved in many important biological processes such as cell recognition, cell differentiation, signal transduction, cell adhesion, apoptosis, immune response, etc. (You, x., Qin, H. & Ye, m.j.sep.sci.41, 248-261 (2018)). Since abnormalities in protein glycosylation can lead to many diseases such as cancer and neurodegenerative diseases, glycosylation can be used as a biomarker for some diseases. Most of the current clinical disease markers are glycosylated proteins, such as liver cancer marker-Alpha Fetoprotein (AFP), prostate cancer marker-Prostate Specific Antigen (PSA), etc. Therefore, it is of great importance to the intensive study of glycosylation.
In recent years, mass spectrometry technology is rapidly developed, which mainly shows faster scanning speed, higher resolution and higher quality precision, so that the high performance liquid chromatography-mass spectrometry (LC-MS/MS) technology is widely applied to large-scale analysis of glycosylation. Glycosylation primarily involves N-linked sugars and O-linked sugars. The peptide fragment sequence of the N-linked glycopeptide has conserved sequence characteristics (Asn-Xxx-Ser/Thr, Xxx ≠ Pro), and the sugar chain part has a fixed pentasaccharide core (Levery, S.B.et. acta-Gen. Subj.1850, 33-42 (2015)), so that the mass spectrogram analysis of the N-linked glycopeptide is relatively easy, the conserved sequence characteristics can be used for filtering the identified peptide fragment sequence, and the fixed pentasaccharide core can generate a characteristic mass spectrum peak which can be used for identifying the N-linked glycopeptide spectrogram and the sugar chain part. O-linked glycopeptides, on the other hand, have no conserved peptide sequence and have high macroscopic heterogeneity (site occupancy varies widely) and microscopic heterogeneity (one site can be modified by multiple glycoforms) (Darula, Z. & Medzihradszky, k.f. mol. cell. proteomics 17, 2-17 (2018)). At present, the mass spectrometry data analysis of O-linked glycosylation mainly adopts a database retrieval mode, glycosylation is set as variable modification to retrieve a protein sequence library, because the O-linked glycosylation has multiple types, the retrieval space is exponentially increased, the retrieval space is increased, on one hand, the retrieval time is increased, on the other hand, the probability of random matching is increased, the false positive results are increased, and the identification sensitivity is reduced.
Disclosure of Invention
The invention aims to provide a method for analyzing an O-linked glycosylated peptide fragment with high sensitivity. By carrying out deglycosylation treatment on the glycopeptide spectrogram, the glycosylation variable modification is promoted from the amino acid site level to the peptide segment level, the problem of overlarge retrieval space of the traditional database retrieval method is solved, and the high-sensitivity identification of the O-glycosylation mass spectrogram is realized.
The invention adopts the following technical scheme:
taking a protein sample, carrying out enzymolysis on the protein, then removing N-sugar chains by PNGase F enzyme, enriching O-linked glycopeptides by using a hydrophilic material (HILICTIP), then analyzing the glycopeptides by using LC-MS/MS, and finally carrying out high-sensitivity identification on O-linked glycopeptides on mass spectrum data.
The analysis process of the mass spectrum data comprises the following steps:
(1) carrying out centroiding (centroiding) operation on the mass spectrogram;
(2) performing permutation and combination on the O-linked saccharides to be identified to generate all possible combinations of glycosylation on the peptide fragments;
(3) filtering the mass spectrogram, and reserving the glycopeptide spectrogram;
(4) performing computer simulation deglycosylation treatment on the glycopeptide spectrogram obtained in the step (3), and generating a deglycosylation spectrogram according to the glycosylation combination form in the step (2);
(5) performing database retrieval on the processed spectrogram, and identifying the peptide fragment;
(6) and carrying out post-processing operation on the searched peptide fragment to obtain the final identified O-glycopeptide.
The combination method of the O-linked sugar in the step (2) comprises the following steps: all O-linked sugars that are desired to be identified are combined regardless of order according to the maximum number of glycosylation permitted on a given single peptide stretch of 0-50, resulting in all forms of glycosylation combinations that may be present on the peptide stretch, and all sugars of each combination plus the corresponding chemical composition.
And (3) filtering according to the intensity of the onium ions in the spectrogram, and if the intensity of the selected onium ions is greater than a specified threshold value, determining the spectrogram as the glycopeptide spectrogram.
The computer simulation deglycosylation process in the step (4) comprises the following steps: for each glycosylation combination, the valence state of the parent ion of the spectrogram is kept unchanged, the mass of the parent ion is subtracted by the mass of the sugar composition to generate new parent ions, each new parent ion corresponds to a new spectrogram, and the Y ions in the spectrogram are detected according to the new parent ions, and are removed to simplify the spectrogram.
Downloading the protein sequence library searched by the database from the UniProt database; the variable glycosylation modification is not needed during retrieval, the conventional variable modification is set to comprise deamidation (Deamidated) and Oxidation (Oxidation), the modified ureidomethyl (Carbammidomethyl) is fixed, and the spectrogram retrieval result retains the 1-10 peptide segment spectrogram matches with the highest score.
In the step (6), considering the possibility of a composite spectrogram (a spectrogram generated by fragmentation of various peptide fragments), 1-5 deglycosylation spectrograms with the best matching effect are reserved for identification results of all deglycosylation spectrograms generated by an original spectrogram, and then the false positive rate of matching (PSM) of the peptide fragment spectrograms is controlled to be 0.1% -10%.
The onium ions used for spectrum filtration in step (3) include: 109.02,115.04,126.05,127.04,129.05,138.05,144.07,145.05,147.07,163.06,168.07,186.08,204.09,274.09,292.10,325.11,350.14,366.14,454.16,495.18,512.20,528.19,657.23,690.25, respectively; the accuracy of the onium ion matching is 0.001-0.5 Da; the intensity threshold of the onium ion adopts the intensity of a relative spectrogram base peak of 0.0-1.0; after detection of the onium ion, the onium ion may optionally be removed from the spectrum to simplify the spectrum.
The invention has the advantages that:
the invention carries out deglycosylation treatment on the glycopeptide spectrogram by using the computer theory, avoids setting variable modification of glycosylation during database retrieval, exponentially reduces the search space of database retrieval, greatly reduces the retrieval time, reduces the probability of spectrogram random matching and realizes high identification sensitivity identification of the O-linked glycosylation peptide segment compared with the traditional database retrieval method.
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FIG. 1 is a schematic diagram of a data analysis procedure for a protein glycosylated glycopeptide.
FIG. 2 is a schematic diagram of the traditional way of setting glycosylation variable modifications to a database (T-Search) and the present invention (O-Search) Search data space.
FIG. 3 is a comparison of the number of glycopeptide spectra, glycopeptide number and peptide fragment sequence number identified by O-Search and T-Search under the same parameters.
FIG. 4 is the three most abundant O-linked carbohydrates in human serum used for analysis (green boxes) and additional other more common 9O-linked carbohydrates.
FIG. 5 is a comparison of the glycopeptide spectra, glycopeptides and peptide fragment sequences identified by O-Search for 3 and 12O-linked saccharides, respectively.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
Firstly, carrying out proteolysis: human serum was denatured by dissolving in 50mM HEPS/8M urea buffer (pH 7.4) containing 20mM DTT and incubated at 37 ℃ for 2 h. The denatured proteins were then alkylated using 50mM IAA for 30min in dark. Then 50mM HEPS (pH 7.4) is added to dilute the urea concentration to 1M, trypsin is added to carry out enzymolysis on the protein at an enzyme-to-protein mass ratio of 1:20 for 20h, and then the peptide fragment after enzymolysis is desalted by using Oasis HLB.
Then enriching glycopeptides: the peptide fragment obtained in the previous step was dissolved in GlycoBuffer 2(pH 7.5) and PNGase F enzyme was added overnight at 37 ℃ to release the N-linked sugar. The glycopeptides were then enriched using HILIC tip according to literature (Qin, h.et al.anal.chem.89, 1469-1476 (2017)), roughly following the scheme: re-dissolving the de-N-glycosylated peptide fragment in loading buffer (80% ACN/1% TFA), and adding 60. mu.L of the solution (corresponding to 50. mu.L of serum hydrolysate) into HILIC tip containing 5mg of click-maltose (click-maltose) material; after capture, wash once with 60 μ L loading buffer and twice with 20 μ L loading buffer; finally, the captured glycopeptides were eluted by washing twice with 100. mu.L of 30% ACN/1% TFA. The eluate was lyophilized and analyzed by RPLC-MS/MS.
RPLC-MS/MS analysis parameters and procedures:
mass spectrometry: thermo Q-active;
an ion source: nanospray;
liquid phase system: u3000RSLC nano-system (Thermo);
first-order spectral resolution: 70,000;
first-order spectral scan range: 400-2000;
mass spectrum acquisition mode: data-dependent acquisition (DDA);
selecting the 15 ions with the highest abundance from the full scans for secondary fragmentation;
second-order spectral resolution: 17,500;
secondary spectral fragmentation pattern: HCD;
secondary spectrum fragmentation energy: gradient Normalized Collision Energy (NCE)23,25,27
The glycopeptide obtained in the previous step was redissolved in 0.1% formic acid/water and then injected into a trap column (4 cm long, 200 μm diameter) packed with C18AQ beads (5 μm, Daison, Osaka, Japan). The separation capillary column was 15cm long, 150 μm in diameter and the packing material was C18AQ beads (1.9 μm, Dr. Maisch, Germany). Chromatographic gradient 120min, mobile phase a (98 water/2% acetonitrile/0.1% formic acid): 10min 3% mobile phase B (80% acetonitrile/20% water/0.1% formic acid), 5min 3-7% mobile phase B, 88min 7-45% mobile phase B, 2min 45-90% mobile phase B, and the whole system is balanced with mobile phase A for 15 min.
Data analysis flow:
(1) the spectrogram raw file format is converted to mzML format using the protome scanner (version 1.4.0.0) using default parameters, the conversion performing a centroiding operation by default.
(2) The three most abundant O-sugars in human serum were selected as sugars to be analyzed this time, including NeuAc-Gal-GalNAc (60%), NeuAc-Gal- (NeuAc-) GalNAc (20%) and Gal-GalNAc (10%) (Darula, Z., Sarnyai, F. & Medzihradzky, K.F. Glycoconj.J.33, 435-445 (2016)). The maximum number of glycosylation allowed on a single peptide fragment was designated 3, and the above three saccharides were combined to obtain all combinations shown in the following table, for a total of 19 combinations corresponding to 15 saccharide compositions:
an represents GalNAc, H represents Gal, S represents NeuAc, and F represents Fuc, so that NeuAc-Gal-GalNAc is simplified to N1H1S1, NeuAc-Gal- (NeuAc-) GalNAc is simplified to N1H1S2, and Gal-GalNAc is simplified to N1H1, and sugar combinations having the same sugar composition are combined together.
As shown in fig. 2, under this condition, the search space of the conventional database search method is 25 times that of the present invention, which illustrates that the present invention can greatly reduce the database search space.
(3) Filtering the mass spectrum and retaining the glycopeptide spectrum. Detecting onium ions 138.05 and 204.09 in the spectrum, the maximum error of matching is allowed to be 0.05Da, i.e. if the absolute value of the difference between the mass-to-charge ratio m of the mass spectrum peak and the mass-to-charge ratio mb of the onium ion satisfies | m-mb | ≦ 0.05, the onium ion mb is considered to be detected in the spectrum (the following process of searching all mass spectrum peaks is performed in this way). The threshold value of the onium ion intensity is set to 0, namely any one of the spectrum peaks 138.05 or 204.09 is detected, namely the spectrum is regarded as a glycopeptide spectrum. As shown in fig. 1A, the spectra have high intensity 138.05 and 204.09 peaks. All the onium ions detected were then removed to simplify the spectra.
(4) Computer modeling deglycosylation treatment. As shown in fig. 1B, taking the sugar composition N1H1S1 as an example, the mass is 656.23, the parent ion mass-to-charge ratio m/z is 938.78, charge is 3+, corresponding to mass 2813.32, the mass of sugar is subtracted, the remainder is 2813.32-656.23 being 2157.09, the conversion is 3+, corresponding to mass-to-charge ratio 720.03, that is, the mass-to-charge ratio of the new parent ion corresponding to the sugar composition. Then searching all Y ions with the valence less than or equal to 3 in the spectrogram, annotating spectral peaks in the spectrogram, [ Y0] (2+) corresponding to a parent ion (1079.55) of the 2+ peptide segment, [ Y1] (2+) corresponding to Y0+ GalNAc (1181.09) of the 2+ valence, [ Y2] (2+) corresponding to Y0+ GalNAc + Gal (1262.12) of the 2+ valence and [ Y1] (3+) corresponding to a parent ion (720.04) of the 3+ valence, wherein the Y ions have no use in peptide segment identification and have high universal strength, so that all the Y ions are removed to be beneficial to peptide segment identification.
(5) And (4) searching a protein sequence library by using the deglycosylation spectrogram obtained in the last step. Protein sequence libraries were downloaded from UniProt, version 2018-05-11, a reviewed human library, and protein repertoires were added to control false positive rates. MS-GF + (version20180605) is taken as a library searching engine, the mass precision of parent ions is 10p.p.m, the fragmentation mode is HCD, the type of the instrument is Q-Exactive, the enzyme is Trypsin, fixed modified urea methyl (Cys), variable modified Deamidated (Deamidated, Asn, Gln) and oxidized (Oxidation, Met) are added, only the best match is kept for each spectrogram, and the default value of MS-GF + is adopted. For the spectrogram identification result corresponding to each saccharide composition, only the best matching result is taken, as shown in fig. 1C, the peptide fragment sequence "TFVLSALQPSPTHSSSNTQR" corresponding to the N1H1F0S1 saccharide composition is the best result, i.e., the peptide fragment sequence and saccharide composition identified by the original spectrogram 1A.
(6) For the above results, the false positive rate of control spectrum level (PSM) was less than 1%.
For comparison, the same mass spectral data were identified in the conventional way: directly carrying out database retrieval by using an original spectrogram, and adding three sugars as variable modifications, namely NeuAc-Gal-GalNAc (Ser, Thr), NeuAc-Gal- (NeuAc-) GalNAc (Ser, Thr) and Gal-GalNAc (Ser, Thr), wherein other parameters are completely consistent with those of the invention.
The human serum used in this experiment was provided by the second hospital affiliated to university of Dalian medical (Dalian, China). The sample was completely legal for acquisition and use and met the relevant regulations of the institutional ethics committee.
As shown in FIG. 3, compared with the conventional database Search method (T-Search), the identification number of the invention (O-Search) is significantly increased in the Glycopeptide spectrogram matching hierarchy (GPSM), the Glycopeptide hierarchy (glycooptide) and the Peptide fragment sequence hierarchy (Peptide).
Example 2
For the mass spectrum data in example 1, 12O-saccharides (FIG. 4) are identified simultaneously, and as can be seen from FIG. 2, the search space of the conventional method is more than 100 times that of the present invention (18780/184>100), and the conventional database search method cannot set so many variable modifications at the same time for identification, but the present invention can solve the problem well. As shown in FIG. 5, many glycopeptide spectra were additionally identified at this time relative to 3O-saccharides, and these spectra are derived from additional 9O-saccharides, which indicates that the method can simultaneously identify more than 10O-saccharides, and that the original 3 saccharides have a reduced amount of spectra and an increased library space, which also indicates that the sensitivity is reduced. Therefore, it is recommended to identify a wide spectrum of glycoforms and then to individually identify the glycoform of interest by picking it out from the identification results.
In summary, the novel O-linked glycosylation peptide fragment analysis method provided by the invention realizes high-sensitivity analysis of O-linked glycopeptides of human serum samples, is multiplied in the amount of spectrogram identification compared with the traditional database retrieval method, and can simultaneously analyze more than 10 glycoforms at a time, thereby indicating that the method has the capability of large-scale data analysis on glycoproteomics and has important application potential in screening of glycoprotein-based disease markers.
Claims (9)
1. A method for analyzing an O-linked glycopeptide, comprising:
taking a protein sample, carrying out enzymolysis on the protein, then removing N-sugar chains by PNGase F enzyme, enriching O-linked glycopeptides by using a hydrophilic material (HILIC tip), then analyzing the glycopeptides by using LC-MS/MS, and finally carrying out high-sensitivity identification on O-linked glycopeptides on mass spectrum data.
2. The method of claim 1, wherein:
the analysis flow of the mass spectrum data is as follows:
(1) carrying out centroiding (centroiding) operation on the mass spectrogram;
(2) (ii) combining the selected O-linked saccharides to generate all combinations of glycosylation possible on the peptide fragments;
(3) filtering the mass spectrogram, and reserving the glycopeptide spectrogram;
(4) performing computer simulation deglycosylation treatment on the glycopeptide spectrogram obtained in the step (3), and generating a deglycosylation spectrogram according to the glycosylation combination form in the step (2);
(5) performing database retrieval on the processed spectrogram, and identifying the peptide fragment;
(6) and carrying out post-processing operation on the searched peptide fragment to obtain the final identified O-glycopeptide.
3. The method of claim 2, further comprising:
the combination method of the O-linked sugar in the step (2) comprises the following steps: all O-linked sugars that are desired to be identified are combined regardless of order according to the maximum number of glycosylation allowed on a given single peptide stretch, resulting in all forms of glycosylation combinations that may be present on the peptide stretch, and all sugars of each combination are summed up with the corresponding chemical composition.
4. The method of claim 2, further comprising:
and (3) filtering according to the intensity of the onium ions in the spectrogram, and if the intensity of the selected onium ions is greater than a specified threshold value, determining the spectrogram as the glycopeptide spectrogram.
5. The method of claim 2, further comprising:
the computer simulation deglycosylation process in the step (4) comprises the following steps: for each glycosylation combination, the valence state of the parent ion of the spectrogram is kept unchanged, the mass of the parent ion is subtracted by the mass of the sugar composition to generate new parent ions, each new parent ion corresponds to a new spectrogram, and the Y ions in the spectrogram are detected according to the new parent ions, and are removed to simplify the spectrogram.
6. The method of claim 2, further comprising:
downloading the protein sequence library searched by the database from the UniProt database; the variable glycosylation modification is not needed during retrieval, the conventional variable modification is set to comprise deamidation (Deamidated) and Oxidation (Oxidation), the modified ureidomethyl (Carbammidomethyl) is fixed, and the spectrogram retrieval result retains the 1-10 peptide segment spectrogram matches with the highest score.
7. The method of claim 2, further comprising:
in the step (6), considering the possibility of a composite spectrogram (a spectrogram generated by fragmentation of various peptide fragments), 1-5 deglycosylation spectrograms with the best matching effect are reserved for identification results of all deglycosylation spectrograms generated by an original spectrogram, and then the false positive rate of matching (PSM) of the peptide fragment spectrograms is controlled to be 0.1% -10%.
8. The method of claim 3, further comprising:
the maximum number of glycosylation allowed on a single peptide stretch is 0-50.
9. The method of claim 4, further comprising:
onium ions used for spectrum filtration include: 109.02,115.04,126.05,127.04,129.05,138.05,144.07,145.05,147.07,163.06,168.07,186.08,204.09,274.09,292.10,325.11,350.14,366.14,454.16,495.18,512.20,528.19,657.23,690.25, respectively; the accuracy of the onium ion matching is 0.001-0.5 Da; the intensity threshold of the onium ion adopts the intensity of a relative spectrogram base peak of 0.0-1.0; after detection of the onium ion, the onium ion may optionally be removed from the spectrum to simplify the spectrum.
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