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

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.

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).

309 more elements

Bioinformatics platform for high-throughput identification and quantification of N—glycopeptide

Life of this patent

Bioinformatics platform for high-throughput identification and quantification of N—glycopeptide

Life of this patent

Abstract

Claims14