Protein and Metabolite Identification - Proteomic and Metabolomic Approaches to Biomarker Discovery - page 241

y 9

PN QS A FT SSGLVSK

100

90

Δ m/z =147

80

Δ m/z =87

Phe

Ser

70

y 8

y 10

60

y 11

Δ m/z =101

50

Thr

Δ m/z =128

y 7

40

GIn

b 3

y 12

30 y 4

y 6 b 6

b 9

b 12

b 4

20

b 5

b 11

b 10

y 2 y 13

b 2

b 8 b 13

10

y 5

0

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

m/z

FIGURE 2 Tandem mass spectrometry (MS 2 ) spectrum of peptide PNQSAFTSSGLVSK. Some of the distances between
various y ions corresponding to speci

c amino acid residues (highlighted in bold) demonstrate the distance between these
ions is equal to the molecular mass of these residues.

sequence manually, this procedure is very time
consuming and generally fruitful for only the
highest quality MS 2 spectra. Considering that
many mass spectrometers can produce thou-
sands of MS 2 spectra/hour, the need for high-
throughput software analysis is clearly evident.
The original, and still one of the most popular,
software program for analyzing MS 2

ranked theoretical spectra are reported and as
the deltaX corr (

Cn).

D

is also widely used for analyzing
MS 2 data. 2 Mascot also owes much of its popu-
larity to the fact that it is bundled as part of the
software component of many types of mass
spectrometers in use today. Mascot provides
a probability-based assignment by conducting
a statistical evaluation of the matches between
the experimental and theoretical MS 2 data. Both
Sequest and Mascot can be used to identify
post-translational modi

Mascot

data

. 5 Invented in 1994 by Jimmy Eng
and John Yates, Sequest initially matches the
precursor ion mass to that of peptides within
a database with the same nominal mass within
a speci

is Sequest

cations (PTMs). The
user must indicate the possibility of the modi ed mass accuracy. The program uses

known fragmentation rules to generate theoret-
ical MS 2 spectra for the possible matches. These
theoretical spectra are compared to the experi-
mental MS 2 spectrum to

ca-

tion on speci c residue types. When searching for

a phosphopeptide, for example, the potential for
an additional mass of 80 Da (representing a phos-
phate group) is applied to each serine, threonine,
and tyrosine residue. The addition is applied
dynamically, allowing the software program to
consider the targeted residue as either modi

nd the sequence that
provides the best correlation. The cross correla-
tions between the experimental and theoretical
spectra are ranked and reported as a cross-
correlation (X corr ) score. In addition, the differ-
ences between the X corr of the

ed

or unmodi ed. Sequest can use data only in

rst and second aspeci c file format (.dta), and Mascot is capable