# US 9,436,797 B2 · Bioinformatics platform for high-throughput identification and quantification of N—glycopeptide

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- Number: US 9,436,797 B2
- Document ID: US9436797B2
- Status: Granted
- Kind: B2
- Application number: 13/884,233
- Filed: 2013-01-22
- Published: 2015-07-02
- Granted: 2016-09-06
- Earliest claimed: 2012-09-27
- Assignees: Korea Basic Science Institute
- Applicants: Korea Basic Science Institute
- Inventors: Gun Wook Park; Jong Shin Yoo; Jin Young Kim; Ju Yeon Lee; Hyun Kyoung Lee; Hyun Joo An; Jae-Han Kim
- Web page: https://patentgrep.com/patent/US9436797B2

## CPC

- G16B 15/00: ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
- C12Q 1/37: involving peptidase or proteinase
- G01N 30/86: Signal analysis
- G01N 33/6848: {Methods of protein analysis involving mass spectrometry}
- H01J 49/0027: {Methods for using particle spectrometers}
- H01J 49/004: {Combinations of spectrometers, tandem spectrometers, e.g. MS/MS, MSn}
- G01N 2030/8813: {biological materials}
- G01N 2030/8831: {involving peptides or proteins}
- G01N 2400/00: Assays, e.g. immunoassays or enzyme assays, involving carbohydrates
- G01N 2440/38: addition of carbohydrates, e.g. glycosylation, glycation
- G01N 2560/00: Chemical aspects of mass spectrometric analysis of biological material
- G01N 2570/00: Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
- H01J 49/0036: {Step by step routines describing the handling of the data generated during a measurement}
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- H01J: ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS (spark-gaps H01T; electric arc lamps H05B31/00; particle accelerators H05H)

## Abstract

The present invention relates to a more efficient and accurate method for the identification and quantification of comparatively low abundant glycopeptides, compared with general peptides, using mass spectrum obtained by using high resolution mass spectrometer. Therefore, the method of the present invention can be effectively used for the techniques for identification of biotherapeutics and diagnosis of cancer or disease by screening glycopeptide, the disease marker (Biomarker), from various samples.

## Claims

### Claim 1

A bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein, which comprises the following steps:
obtaining mass spectrum by analyzing polypeptides prepared by hydrolyzing glycoproteins with protease by using high resolution mass spectrometer (step 1);
converting the mass spectrum obtained in step 1 into MS1 (Mass spectrum 1) and tandem spectrum (MS/MS) (step 2);
calculating M-score at each tandem spectrum selected from the group consisting of converted tandem spectrums showing Oxonium ion peak molecular weights of 129.06, 138.06, 145.05, 147.07, 163.06, 168.07, 186.08, 204.08, 274.09, 292.10, 350.15, 366.14, 454.16, 528.19, and 657.24 (step 3), wherein the M-score is calculated by the following mathematical formula 1:
⁢
[
Mathematical
⁢
⁢
Formula
⁢
⁢
1
]
M
Score
=
n
N
*
∑
i
=
1
n
⁢
O
i
(
n
-
1
)
O
i
=
I
i
I
ma
⁢
⁢
x
⁡
(
<
700
⁢
Da
)
*
C
/
MassError
+
1.0
(N: Number of observable oxonium ions;
n: number of observed oxonium ions,
Ii: ith peak intensity in repetitive analysis; and
C: constant value);
selecting glycopeptide spectrum using the M-score obtained in step 3 (step 4);
obtaining isotopic distribution in MS1 of the glycopeptide spectrum selected in step 4, which is compared with a database to calculate S-score, and then identifying glycopeptide candidates by using the calculated S-score (step 5), where the S-score is a calculation for the comparison of similarity of the theoretically isotopic distribution obtained from the database with the isotopic distribution obtained by experiment with MS1, and wherein the S-score is calculated by the following mathematical formula 2:
⁢
[
Mathematical
⁢
⁢
Formula
⁢
⁢
2
]
S
Score
=
(
1.0
(
1.0
+
∑
i
=
1
n
⁢
(
X
⁢
⁢
1
-
Y
⁢
⁢
1
)
i
2
)
*
C
⁢
⁢
1
)
+
(
n
⁡
(
∑
X
⁢
⁢
2
⁢
Y
⁢
⁢
2
)
-
(
∑
X
⁢
⁢
2
)
⁢
(
∑
Y
⁢
⁢
2
)
(
n
⁢
∑
X
⁢
⁢
2
2
-
(
∑
X
⁢
⁢
2
)
2
)
*
(
n
⁢
∑
Y
⁢
⁢
2
2
-
(
∑
Y
⁢
⁢
2
)
2
)
*
C
⁢
⁢
2
)
(X1: mass of nth peak among theoretical isotope peaks;
Y1: mass of nth peak among experimentally obtained isotope peaks;
X2: relative intensity of nth peak among theoretical isotope peaks;
Y2: relative intensity of nth peak among experimentally obtained isotope peaks;
C1: constant value; and
C2: constant value);
evaluating exact glycopeptide from the glycopeptide candidates identified in step 5 by using E-score, Y-score, and Y′-score in tandem spectrum (step 6),
wherein the E-score is calculated by the following mathematical formula 5:
⁢
[
Mathematical
⁢
⁢
Formula
⁢
⁢
5
]
E
Score
=
n
N
*
∑
i
=
1
n
⁢
log
2
⁡
(
I
[
i
]
I
ma
⁢
⁢
x
⁡
(
ProcursorIonExclusion
)
)
/
(
MassError
+
1.0
)
(N: the number of observable glycopeptide fragmentation (c,z ions);
n: the number of observed glycopeptide fragmentation; and
Ii: ith intensity in repetitive analysis) spectrum;
wherein the Y-score is calculated by the following mathematical formula 3:
⁢
[
Mathematical
⁢
⁢
Formula
⁢
⁢
3
]
Y
Score
=
n
N
*
∑
i
=
1
n
⁢
log
2
⁡
(
I
[
i
]
I
ma
⁢
⁢
x
)
/
(
MassError
+
1.0
)
(N: the number of observable glycopeptide fragmentation;
n: the number of observed glycopeptide fragmentation; and
Ii: ith in repetitive analysis); and
wherein the Y′-score is calculated by the following mathematical formula 4:
Y′-score=Y-score+y-score; Mathematical Formula 4
performing quantitative analysis with the glycopeptides evaluated in step 6 (step 7); and
performing additional identification and quantification of family N-linked glycopeptide via correlation analysis with those glycopeptides quantified in step 6 (step 8).

### Claim 2 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the tandem spectrum is CID or HCD spectrum.

### Claim 3 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the database is established in step 4 from glycoproteins by using theoretical isotopic distribution of glycopeptides.

### Claim 4 (depends on 2)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 2, wherein the similarity of the mass distribution of isotopes is measured by using Euclidean distance and the similarity of the strength distribution is measured by using Pearson correlation analysis in order to produce S-score in step 5.

### Claim 5 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the tandem spectrum in step 6 is CID or HCD spectrum.

### Claim 6 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the tandem spectrum in step 6 is HCD spectrum.

### Claim 7 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the tandem spectrum in step 6 is ETD spectrum.

### Claim 8 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the glycopeptide is identified and quantified without tandem spectrum (MS/MS) but using S-score and MS accuracy in MS1 after the family N-glycopeptide is additionally predicted based on the glycopeptide accurately identified by using tandem spectrum (MS/MS) in step 6.

### Claim 9 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the correlation of all the glycopeptides is presented as a map representing the similarity among the samples for comparison after the identification and quantification in step 8.

### Claim 10 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the hydrolysis is performed by using the enzyme selected from the group consisting of trypsin, Arg-C, Asp-N, Glu-C, Lys-C, and chymotrypsin.

### Claim 11 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the mass spectrometer is the one selected from the group consisting of LTQ-FT, Orbitrap, Triple-Tof, Q-Tof, and QExactive.

### Claim 12 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the identification and quantification is performed by using the high resolution mass spectrometer with mass resolution of at least 10,000 and mass accuracy of up to 50 ppm.

### Claim 13 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the glycopeptide for the quantification is selected by using S-score in MS1 spectra.

### Claim 14 (depends on 1)

The bioinformatics process for the identification and quantification of N-linked glycopeptide from glycoprotein according to claim 1, wherein the strength of the glycopeptide selected from MS1 spectra for the quantification is presented as the sum of the strength of three strongest peaks theoretically expected.