Vol. 21 Suppl. 1 2005, pages i431–i439
doi:10.1093/bioinformatics/bti1038
BIOINFORMATICS
Automated interpretation of MS/MS spectra of
oligosaccharides
Haixu Tang1,2,∗ , Yehia Mechref 3 and Milos V. Novotny 3
1 School
of Informatics, 2 Center for Genomics and Bioinformatics and
of Chemistry, Indiana University, Bloomington, IN, USA
3 Department
Received on January 15, 2005; accepted on March 27, 2005
ABSTRACT
Motivation: The emerging glycomics and glycoproteomics
projects aim to characterize all forms of glycoproteins in
different tissues and organisms. Tandem mass spectrometry (MS/MS) is the key experimental methodology for
high-throughput glycan identification and characterization.
Fragmentation of glycans from high energy collision-induced
dissociation generates ions from glycosidic as well as internal
cleavages. The cross-ring ions resulting from internal cleavages provide additional information that is important to reveal
the type of linkage between monosaccharides. This information, however, is not incorporated into the current programs
for analyzing glycan mass spectra. As a result, they can rarely
distinguish from the mass spectra isomeric oligosaccharides,
which have the same saccharide composition but different
types of sequences, branches or linkages.
Results: In this paper, we describe a novel algorithm for
glycan characterization using MS/MS. This algorithm consists of three steps. First, we develop a scoring scheme to
identify potential bond linkages between monosaccharides,
based on the appearance pattern of cross-ring ions. Next,
we use a dynamic programming algorithm to determine the
most probable oligosaccharide structures from the mass spectrum. Finally, we re-evaluate these oligosaccharide structures,
taking into account the double fragmentation ions. We also
show the preliminary results of testing our algorithm on several
MS/MS spectra of oligosaccharides.
Availability: The program GLYCH is available upon request
from the authors.
Contact: [email protected]
1 INTRODUCTION
Glycosylation is a common post-translational modification
that covalently attaches sugar chains (called oligosaccharides
or glycans) to the cell surface or secreted proteins. Glycans
(either attached to proteins or sometimes in free form) can
mediate cell–cell, cell–matrix and cell–environment interactions, which are crucial to many biological functions in
a complex multicellular organism (Varki et al., 1999). The
∗ To
whom correspondence should be addressed.
emerging glycomics and glycoproteomics projects aim to
identify all the carbohydrate molecules, the ‘glycome’, in
an organism (e.g. human) and to profile their changes under
different conditions, e.g. healthy versus disease.
Owing to the structural complexity of glycans, the technology for determining glycan structure has lagged far behind
those for the other classes of biological macromolecules, such
as nucleic acids and proteins. In recent years, mass spectrometry (MS) has become the key tool for glycan structural
analysis because of its high sensitivity. A number of glycan
analytical methods using MS have been published which
differ in various characteristics ranging from the ionization
method (e.g. MALDI/TOF-MS or ESI/MS) to the choice of
derivatives (or ions) (e.g. paralkyl or native structures, protonated or metal-cationized ions) (Mechref and Novotny, 2002;
Zaia, 2004). In this paper, we will focus on the experimental
results from MALDI/TOF/TOF-MS, but the same algorithm
can also be applied to the other MS platforms by adjusting the fragmentation model appropriately. Fragmentation
of glycans in MALDI/MS can result from the post-source
decay (PSD) or collision-induced dissociation (CID). Low
energy PSD spectra from neutral glycans are dominated by the
glycosidic cleavages with very weak cross-ring ions. On the
other hand, in high energy CID spectra, in addition to glycosidic cleavages, cross-ring ions are often observed, which
provide valuable information for oligosaccharide characterization (Mechref et al., 2003), as we show in the following
sections.
Despite the rapid progress in techniques in the field of
glycomics, the tools for automatic glycan mass spectrum interpretation are still some way off (von der Lieth et al., 2004).
There are several programs that are used to deduce glycan
structures from their tandem mass spectrometry (MS/MS)
spectra. Glycomod is one of the most commonly used
tools, which attempts to find all possible compositions of
a glycan molecule from its determined total mass (Cooper
et al., 2001). However, this program was not designed for
interpreting MS/MS spectra and hence cannot distinguish
between isomeric oligosaccharides, i.e. oligosaccharides with
the same monosaccharide composition but different structures. GlycosidIQ (Joshi et al., 2004) and GlycoSearchMS
© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
i431
H.Tang et al.
(Lohnmann et al., 2004) each implemented a SEQUEST-like
(Eng et al., 1994) database searching algorithm to compare an
experimental MS/MS spectrum against the theoretical spectra
of a collection of oligosaccharides from a curated database,
SweetDB (Loss et al., 2002) and GlycoSuiteDB (Cooper et al.,
2003), respectively. Despite being successful in some cases,
these approaches are not suitable for a large-scale glycomics
project, because unlike the protein database (which can be
derived from completely sequenced genomes), the glycan
database is still largely incomplete. One of the specific aims
of the glycomics project is to collect oligosaccharides from
model organisms. STAT (Gaucher et al., 2000), StrOligo
(Eithier et al., 2002) and a recently developed algorithm (Shan
et al., 2004) have attempted to reconstruct the glycan structure from MS/MS spectra de novo by exhaustively searching
all possible glycan structures that match the experimental total
mass. Owing to the limitation of computational complexity,
these algorithms can analyze only glycans consisting of no
more than 10 monosaccharides. Sophisticated algorithms are
required to analyze relatively large oligosaccharides efficiently. Finally, none of the above programs utilizes the
information provided by the cross-ring ions to identify the
correct oligosaccharides among many isomers.
In this paper, we propose a dynamic programming algorithm
in conjuction with a novel scoring scheme for de novo
oligosaccharide characterization from MS/MS spectra. This
algorithm is a variant of the de novo peptide sequencing
algorithm (Dancik et al., 1999; Chen et al., 2001; Bafna
and Edwards, 2003; Ma et al., 2003), except that it allows
branching in the polymer structure that the glycans may have.
2 OLIGOSACCHARIDES AND THEIR
FRAGMENTATION PATTERNS
There are two main types of glycosylation for glycoproteins
in vivo: N-linked glycosylation, in which N -linked oligosaccharides are linked to the amide N atom in the side-chain of
amino acid residue Asn, and O-linked glycosylation, in which
O-linked oligosaccharides are linked to the hydroxyl O atom
in the side-chain of amino acid residues Ser or Thr. The glycosylation consists of a series of reactions catalyzed by many
different glycan transferases. For each reaction, one monosaccharide is linked to the end of the present oligosaccharide by
forming a glycosidic bond. The common monosaccharides
found in higher animal oligosaccharides, which are considered in this study, are listed in Table 1. Several others can
be found in lower animals, bacteria or plants. Even in higher
animals, the monosaccharides found in different species may
be slightly different. For example, two predominant forms of
sialic acid in animals are N-acetyl-neuraminic acid (NeuAc)
and N-glycolyl-neuraminic acid (NeuGc). Because of a missing enzyme, humans express only NeuAc, which is different
from NeuGc only by one less oxygen atom (Chou et al., 1998).
Therefore, we should design a specific set of monosaccharides
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Table 1. The common monosaccharides in animal oligosaccharides
Name (abbreviation)
Epimersa
Mass
Symbol
Hexose (Hex)
Glucose (Glc)
Mannose (Man)
Galactose (Gal)
N-acetylglucosamine
(GlcNAc)
N-acetylgalactosamine
(GalNAc)
Glucuronic acid (GlcA)
Iduronic acid (IdcA)
—
—
—
162.05
203.08
176.03
♦
132.04
146.06
291.10
—
307.09
HexNAc
HexA
Xylose (Xyl)
Fucose (Fuc)
N -acetyl neuraminic
acid (NeuAc)
N-glycolyl neuraminic
acid (NeuGc)
a
Epimers, two monosaccharides that differ only in their configuration, cannot be
distinguished by mass spectrometry.
in the analysis of the organism from which the oligosaccharides are sampled. In this paper, we use the alphabet A,
consisting of six monosaccharides, in which either NeuAc or
NeuGc is considered depending on the organism of interest
(human or the other higher animals).
Free monosaccharides can exist in an open or cyclic form
(Fig. 1a, b). The numbering of the carbon atoms follows the
rules of organic chemistry nomenclature, such that the aldehyde carbon is referred to as C1 (Fig. 1a). The cyclic form
of monosaccharides has a ring structure that consists of six
covalent bonds, conventionally denoted from ‘0’ to ‘5’ in the
clockwise direction (Fig. 1b). Two monosaccharides can react
with each other, forming a glycosidic bond between the hemiacetal group (i.e. the C1 group) from one monosaccharide
and the alcohol group from the other while releasing a water
molecule. Depending on which alcohol group participates in
the reaction, the set of possible linkage types, denoted as B,
has four different types of glycosidic bonds, i.e. 1–2, 1–3,
1–4 and 1–6 bonds. The first number in the notation (e.g. 1
in 1–2 bond) represents the numbering of the carbon in the
hemiacetal group, and hence is always 1. The second number
in the notation (e.g. 2 in 1–2 bond) represents the numbering
of the carbon that the alcohol group is linked to, and hence
could be 2, 3, 4 or 6. Figure 1c shows a disaccharide consisting of two glucoses forming a 1–4 glycosidic bond. Since
a monosaccharide has more than one alcohol group, it may
react with more than one other monosaccharide. As a result,
an oligosaccharide can form branching structures. Figure 1d
shows a tetrasaccharide consisting of four glucoses, in which
one forms 1–4 and 1–6 glycosidic bonds with two others. This
branching structure can be drawn as a tree (Fig. 1e), in which
each monosaccharide is represented by a symbol (Table 1)
and each glycosidic bond is represented by an edge. In theory,
Automated interpretation of MS/MS spectra
Fig. 1. The structure of oligosaccharides. (a) The open (acyclic) form structure of glucose, an epimer of hexose, one of the monosaccharides,
i.e. the basic unit of an oligosaccharide. (b) The cyclic form structure of glucose. The carbon atoms in the ring are not shown. (c) A diglucose,
consisting of two glucoses forming a 1–4 glycosidic bond. (d) A tetraglucose, consisting of four glucoses with a branching. (e) The symbolic
representation of the tetraglucose shown in (d). The numbers show the linkage types.
there could be at most four branches in the tree; in reality, in
higher animals, the branchings are always binary, except in
one special case, called ‘bisecting GlcNAc’ (Stanley, 2002).
Above all, in addition to sequence variation, oligosaccharides can also exhibit variations of branching and bond
linkages. As a result, monosaccharides generate much
higher linkage complexity than amino acids and nucleotides.
For example, three amino acids can generate six different
peptide sequences; in comparison, three monosaccharides
can generate >100 trisaccharide configurations. Therefore,
oligosaccharide characterization is more complex than protein
identification.
The fragmentation patterns for oligosaccharides have been
studied in detail using different ionization techniques. According to the definitions of Domon and Costello (1988), the
corresponding ions are denoted Yn /Bl−n and Zn /Cl−n , where
l is the length of the oligosaccharides and n denotes the cleavage site (Fig. 2a). In high energy CID spectra, in addition
to the glycosidic cleavages, cross-ring cleavages, in which
two covalent bonds in the ring structure are broken, can often
be observed (Fig. 2b). The corresponding ions are denoted
p,q X and p,q A , where p and q are sites for cross-ring bond
n
n
(designated from 0 to 5, Fig.1b) cleavages. Similar to peptide MS/MS spectra, the ions corresponding to more than
one glycosidic bond cleavage are often weak. The X/A ions
in the CID spectra provide valuable information to distinguish between isomeric oligosaccharides and to determine
the branching and bond linkages. Table 2 shows mass differences of all nine types of potential X/A ions compared with
the Y /B ions cleaved at the same residue. Clearly, different monosaccharides tend to have different X/A ion patterns.
If we assume one peak in the MS/MS spectrum is from a
Y /B ion, based on its surrounding peaks, we may be able
to distinguish at which type of monosaccharide this cleavage
is. On the other hand, given an oligosaccharide, no matter
how complicated its structure is, we can construct its theoretical MS/MS spectrum by (1) computing the mass values of
the Y /B and Z/C ions and (2) computing the corresponding mass values of X/A ions corresponding to each Y /B
ion. Moreover, in a monosaccharide in the middle of an oligosaccharide (i.e. when it forms glycosidic bonds with two
other monosaccharides), some X/A ions will not appear in the
spectra, even though the corresponding fragmentation could
happen. Figure 2c illustrates this using the fragmentation of
triglucose as an example. We call the occurrences of X/A ions
in a particular type of linkage the occurrence pattern. Among
four types of linkages in B, any one has a unique occurrence
pattern different from the others. Table 3 summarizes the X/A
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H.Tang et al.
Y2
B1
C1
Z2
B2
Y1
C2
Z1
Y0
Z0
B3 C3
the oligosaccharide, reconstruct the structure of the oligosaccharide such that its theoretical spectrum optimally matches
the given experimental spectrum.
Note that there are different levels of structures to be characterized for oligosaccharides: monosaccharide composition,
sequence, branching structure and linkage. In the context of
this paper, we aim to characterize the complete structure of the
oligosaccharides, i.e. all the above four should be determined
by the computational procedure.
3
Fig. 2. The fragmentation patterns of oligosaccharides. (a) The Y /B
and Z/C ions are from the glycosidic cleavages. (b) The cross-ring
cleavages create X/A ions. (c) Since the middle unit of the triglucose
is linked to two other glucoses, some cleavages create fragment ions
with small masses that are not close to the corresponding Y /B ions,
e.g. 0,4 X1 . These ions will be ignored in our program.
ALGORITHM
An oligosaccharide can be represented by a tree structure
of n monosaccharide residues (Fig. 3a), r1 , r2 , . . . , rn , in
which the root residue rn has an open hemiacetal group and
the leaf residues have open alcohol groups. Similar to the
approach employed for peptide sequencing (Dancik et al.,
1999; Bandeira et al., 2004), we define the prefix residue mass
(PRM) mi as the total mass of residues in the subtree rooted
by residue ri (Fig. 3b); in the special case, mn is referred
to as the parent mass Mparent , i.e. the total mass of the oligosaccharide. Generally, the series of PRMs corresponds to
the series of B ions in the MS/MS spectrum, with a constant
offset. However, unlike the peptide sequences, in addition to
the sequence variants, oligosaccharides have linkage variants.
To reflect this, we define the prefix residue feature (PRF), a
combination of three properties: (1) the PRM, (2) the monosaccaride type and (3) the linkage type of the residue. For
instance, (mi , ri , bi ) is the PRF at position i, where mi is the
PRM, ri is the monosaccharide type of residue i and bi is
the linkage type between residue i and the residue at the root
side to which it links.1 Therefore, any olicosaccharide with n
residues can be represented by a series of PRFs, (m1 , r1 , b1 ),
(m2 , r2 , b2 ), . . . , (mn , rn , bn ).
Our strategy to solve the oligosaccharide characterization
problem is first to find and score potential PRFs by evaluating the local fragmentation patterns and then to deduce the
oligosaccharide structure from these PRFs. Every peak in an
experimental MS/MS spectrum should correspond either to
one of the fragmentation ions (B/Y , C/Z, A/X) or to a noisy
ion.2 Assume that one peak p is the fragmentation ion type
i of residue r ∈ A with linkage b ∈ B, we can compute the
corresponding PRM Mir as
ion occurrence patterns of all linkages. Finally, some monosaccharides cannot form certain linkages, since they have no
alcohol group at that position. Table 4 summarizes the linkage
types that each monosaccharide can form.
Utilizing the rules given in Tables 2–4 for a given oligosaccharide, we can compute its theoretical MS/MS spectrum,
i.e. the mass values of all its potential ions under high
energy CID fragmentation, including Y /B, Z/C and X/A
ions. This procedure can be integrated into a glycan database searching program such as GlycosidIQ (Joshi et al.,
2004) or GlycoSearchMS (Lohnmann et al., 2004), comparing an experimental spectrum with the theoretical spectrum
of each known oligosaccharide in the database and determining an optimal match among them. However, given the
incompleteness of the current glycan database, we are more
interested in solving this problem assuming no preliminary knowledge. Therefore, we need to solve the following
problem.
note by this definition that the root residue n does not have the feature
of linkage type; thus we always (arbitarily) assign bn to 1 (1–2 linkage).
De novo oligosaccharide characterization problem.
Given an experimental MS/MS spectrum and the total mass of
noisy ions may be explained by the products of double cleavages, i.e.
two cleavages at different sites. At this step, we ignore the effect of these ions
and treat them as noisy peaks.
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Mir = Mp + δMir ,
i = B, C,0,2 A, . . . ,3,5 A,
Mir = Mparent − (Mp + δMir ),
i = Y , Z,
0,2
(1)
3,5
X, . . . ,
X,
(2)
1 We
2 Some
Automated interpretation of MS/MS spectra
Table 2. The mass difference of X/A ions and the corresponding Y /B ion
Monosaccharide
0,2 A
0,3 A
0,4 A
1,3 A
1,4 A
1,5 A
2,4 A
2,5 A
3,5 A
Hex
HexA
HexNAc
Fuc
Xyl
NeuAc
NeuGc
43.02
43.02
84.04
43.02
43.02
71.01
87.01
73.03
73.03
114.05
73.03
73.03
101.03
101.03
103.04
103.04
144.06
103.04
103.04
172.06
172.06
103.04
116.01
103.04
87.04
103.04
232.08
232.08
73.03
86.00
73.02
57.03
73.03
161.04
161.04
29.00
29.00
29.00
29.00
29.00
72.99
72.99
103.04
116.01
144.06
87.04
103.04
175.06
190.05
59.01
59.01
100.04
59.01
59.01
87.01
103.00
89.03
89.03
130.04
89.03
89.03
117.03
133.03
Table 3. The list of occurring X/A ions for monosaccharide residues that are linked to two or more other monosaccharidesa
X/A ions
(2, n)b
(3, n)
(4, n)
(6, n)
(2, 3)3
(2, 4)
(2, 6)
(3, 4)
(3, 6)
(4, 6)
0,2 X/A
−
−
−
+
+
+
+
+
+
+
−
−
+
+
+
+
+
+
+
+
−
−
−
−
−
−
−
+
+
+
−
−
−
−
−
−
+
−
−
−
−
−
+
+
−
+
+
−
+
−
−
+
+
+
+
+
+
+
+
−
+
+
+
−
+
−
+
−
−
−
−
+
−
−
+
−
+
+
−
+
+
−
−
+
−
+
−
+
−
−
0,3 X/A
0,4 X/A
1,3 X/A
1,4 X/A
1,5 X/A
2,4 X/A
2,5 X/A
3,5 X/A
a
‘+’ represents presence; ‘−’ represents absence. For those monosaccharides that are linked to more than two other ones, only the A/X ions that occurs in all the bi-linkage cases
should occur.
b
(2,n) represents that one of two linkages is 1–2; the other is any of four linked to the monosaccharide’s first carbon; (2,3) shows that the two linkages are to the monosaccharide’s
second and third carbons, i.e. a branching with 1–2 and 1–3 linkages.
Table 4. The list of occurring X/A ions for monosaccharide residues in
difference linkagesa
X/A ions
1–2
1–3
1–4
1–6
Hex
HexA
HexNAc
Fuc
Xyl
NeuAc
NeuGc
+
+
−
+
+
−
−
+
+
+
+
+
+
+
+
+
+
+
+
−
−
+
−
+
−
−
−
−
a
‘+’ represents presence; ‘−’ represents absence.
In practice, since MS has mass accuracy of measurement, we
allow a certain tolerance for this support peak computation.
For MALDI/TOF/TOF-MS, = 0.2 Da.
We score PRFs depending on how their fragmentation patterns are supported by the experimental ion peaks. Currently,
we use a simple scoring scheme. We define the supporting
score of a PRF as the number of its support peaks Nf . After
deducing a list of PRFs, each associated with a supporting score, we attempt to find a series of PRFs (M1 , r1 , b1 ),
(M2 , r2 , b2 ), . . . , (Mn = Mparent , rn , bn ) (M1 ≤ M2 ≤ · · · ≤
Mn ) that correspond to one oligosaccharide’s tree structure,
and have the maximal total score
Vall =
S(Mi , ri , bi ),
(3)
1≤i≤n
where Mp is the mass of p and δMir is the mass difference
between ion type i and the B ion for the residue r (Table 2).
We call p an i-support peak of PRF (Mir , r, b) for any b ∈ B.
Therefore, every peak in the experimental spectrum can be
the support peak of at most 22 × 6 × 4 = 528 different PRFs.3
3 Twenty-two is the total number of ion types, including 9 A and 9 Z ions,
and B, Y , C, Z ions; 6 is the size of the monosaccharide alphabet A; 4 is the
size of the linkage alphabet B.
where S(Mi , ri , bi ) is the score of PRF (Mi , ri , bi ).
In order to solve this problem, we first sort all PRFs
according to their PRMs. For each PRF (m, r, b), we define
the score V (m, r, b) as the maximal total score of a series
of (M1 , r1 , b1 ), (M2 , r2 , b2 ), . . . , (Ml =, m, rj , bj ) (M1 ≤
M2 ≤ · · · ≤ Mj ) that corresponds to one oligosaccharide’s
tree structure with a root residue rj and a parent mass m.
Clearly, the global optimal solution for the problem described
above is maxr∈A V (Mparent , r, 1).
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H.Tang et al.
(a)
1
4
42
4
3
6
4
9
4
5
4
6
6
4
12
11
4
6
7
10
4
8
4
(b)
1
4
1
4
2
3
4
2
4
3
6
4
9
6
4
4
5
6
7
10
4
8
1
4
4
2
4
3
4
6
9
5
6
4
4
6
11
7
6
4
10
8
4
Fig. 3. The PRMs of an oligosacchrides. (a) An oligosaccharide
consisting of 12 glucoses. The glucose residues are indexed according to the partial order of nodes of the tree, from leafs to the root. For
any two residues ri and rj , if ri is within the subtree rooted by rj , i
must be indexed smaller than j . (b) The subtrees rooted by residues
r3 , r9 , r10 , r11 .
The optimal score V (m, r, b) can be computed by dynamic
programming over all PRFs with the ascending order of their
PRMs:
V (m, r, b) = S(m, r, b)

0



V (m1 , r1 , b1 )
+ min
m1 ≤m2 <m 
V (m1 , r1 , b1 ) + V (m2 , r2 , b2 )


if m = mass(r)
if m = mass(r) + m1
if m = mass(r)
+m1 + m2 , b1 = b2
(4)
where mass(r) is the mass of the monosaccharide residue r
(Table 1). The computational complexity of this algorithm
is O(N 3 ), where N is the number of PRFs, which is proportional to the number of peaks in the MS/MS spectrum.
Note that the above iteration formula takes into consideration
only binary branching in the tree structure of the oligosaccharide. This is a realistic assumption since there are very few
exceptions in a real situation. On the other hand, it is easy to
modify this algorithm to consider more complex branching.
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At each step in the process of the forwarding iteration, we need
to record the optimal trace. After we get the optimal score,
we can retrieve the entire series of PRFs using a traceback
procedure.
The algorithm described above maximizes a simple scoring function: the number of support peaks, i.e. the number
of theoretical ions of an oligosaccharide that match the mass
of peaks in the experimental spectrum. It does not consider
the relative intensities of the matched peaks. Nor does it consider the potential redundantly matched peaks (i.e. one peak
can support several different theoretical ions) if their masses
are the same. We note that this is different from what was
considered in peptide de novo sequencing algorithms (Dancik
et al., 1999; Chen et al., 2001; Bafna and Edwards, 2003), in
which it is forbidden to use one peak to explain more than
one fragment ion. To consider this constraint, de novo peptide sequencing methods employed a dynamic programming
algorithm to solve the anti-symmetric longest path problem.
However, a similar problem formulation and solution allowing
for branching stuctures, e.g. oligosaccharides, remains open.
What is more, in oligosaccharide characterization, there are
often actual redundant ions (i.e. ions with the same theoretical mass values) because oligosaccharides have a smaller
alphabet than proteins and hexose is the most frequent monosaccharide. Therefore, in our scoring framework we allow
the redundant usage of the peaks. A more sophisticated scoring scheme, e.g. intensity-based scores, may alleviate this
problem and will be considered in the future.
Because of the complexity of oligosaccharide structures,
there are often multiple solutions which match the experimental spectrum equally well in terms of the matching score,
especially those oligosaccharides that differ only in linkages.
Our approach can report all the optimal solutions from the
dynamic programming algorithm by recording the k best solutions at each iteration step (by default k = 200). We adopt a
post-processing step to re-evaluate each of them and finally
rank these oligosaccharides based on their re-evaluation score.
In the re-evaluation scheme, we generate a theoretical spectrum from the oligosaccharides, taking into consideration both
the single cleavage (the glycosidic and the cross-ring) and
the double cleavage ions. Then we compare this spectrum
with the experimental spectrum, count the number of common
peaks between them and use this score to rank the candidate
oligosaccharides.
4
RESULTS
The algorithm described above is implemented in a program
entitled GLYCH (GLYcan CHaracterization), written in the
C language on a UNIX/LINUX system. We have tested this
program for several experimental MS/MS spectra of oligosaccharides (shown in Fig. 4). In these experiments, N -glycans
were released enzymatically by ribonuclease B (Mechref and
Novotny, 1998). All oligosaccharides were permethylated
Automated interpretation of MS/MS spectra
4
4
4
4
3
4
6
4
6-sialyllactose
3-sialyllactose
oligomannose
4
3
3
3
4
6
tetraose-a
4
3
4
4
4
4
tetraose-c
6
4
3
Hexaose
Fig. 4. The structures of the oligosaccharides used for testing in this paper.
Table 5. Performance of GLYCH on oligosaccharide characterization
Category
Linear
Branching
Oligosaccharides
Hexaose
3-Sialyllactose
6-Sialyllactose
Tetraose-a
Tetraose-c
Oligomannose
DP solutions
Rank
OS
Score
Re-evaluation
Rank
OS
Score
1
1
1
1
1
13
369
31
35
61
58
177
19
11
12
13
14
20
1
1
1
1
1
1
26
2
2
3
2
17
26
19
21
20
20
25
DP solutions: solutions identified by the dynamic programming approach; re-evaluation: solutions ranked by the re-evalution score; OS: number of optimal solutions; score: score of
the optimal solution. The structures ranked at the first two positions are selected for the re-evaluation.
before the MS/MS analysis. MS/MS spectra were obtained
by MALDI/TOF/TOF-MS, using the procedure described by
Mechref et al. (2003). All MS/MS spectra were pre-processed
before being analyzed using GLYCH. A sliding window
including 20 peaks was moved along the experimental spectrum. Inside every window, all peaks with an intensity below
the average intensity minus three times the standard deviation
of all peak intensities were ignored.
The results are summarized in Table 5. In all cases, the
real structure is among the best solutions GLYCH identified. The dynamic programming approach can identify the
real oligosaccharide among many other which have very
similar structures, often differing by only one linkage. The
re-evaluation procedure can dramatically reduce the number
of possible optimal solutions, while keeping the real structure among the optimal solutions. For more complicated
structures, e.g. the oligomannose with branching structure,
the real oligosaccharide is not ranked best by the dynamic
programming approach; after the re-evaluation, however, it is
among the optimal solutions.
4.1
Linear oligosaccharides
We first tested GLYCH on the MS/MS spectrum of oligomannose, consisting of six mannoses linked by 1–4 glycasilic
bonds. Our dynamic programming algorithm successfully
determined the linear sequence of this oligosaccharide.
Among the 107 optimally ranked solutions, which all have the
same score (19), 37 of them give the correct linear sequence
of the oligosaccharide and the real structure is among them.
The other 70 solutions have the correct monosaccharide composition but wrong (branching) structures. Our re-evaluation
procedure significantly reduced the redundancy of the optimal
solutions. After re-evalution, only six were ranked with the
highest score (26), including the real structure. All of these
six optimal solutions have a linear structure and they differ
only by the linkage types (1–4 or 1–6) in the middle of the
chain.
Next we tested GLYCH on the MS/MS spectra of several
oligosaccharides with hybrid linkage type. 3-Sialyllactose and
6-sialyllactose are two trisaccharides, both consisting of two
hexoses and one sialic acid (NeuAc) at the end. The difference between them is that the linkages between the sialic acid
and the hexose are 1–3 and 1–6, respectively. GLYCH correctly identified the real structure and ranked it in first place
even before re-evalution. For two oligosaccharides that have
more complicated structures, tetraose-a and tetraose-c, both
consisting of one sialic acid, three hexoses and one acetylglucosamine, GLYCH again performed well. It identified the real
structure of the oligosaccharides and ranked it in first place
(among several others) after the re-evaluation step.
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H.Tang et al.
4.2
Oligosaccharides with branching structure
Finally, we tested GLYCH on the MS/MS spectra of a branching hexaose, an N-glycan. This oligosaccharide consists of
seven monosaccharides, four mannoses, two acetylglucosamines and one sialic acid (Fig. 4), of which three mannoses
and the two acetylglucosamines form the core ‘pentamer’
structure (see Discussion section). The dynamic programming
approach identified the real structure, but did not rank it in
first place; its score is smaller than those of 12 other oligosaccharides. However, after the re-evalution, the real structure
was re-ranked to first place among 16 other oligosaccharides.
It is worth pointing out that among the 16 oligosaccharides, 13 have the linear structure with no branching, which
implies that the current GLYCH algorithm prefers linear structure to branching structure. This issue has to be addressed
by the future improvement of the scoring function used
in GLYCH.
5 DISCUSSION
We proposed a novel algorithm to interpret the MS/MS spectra of oligosaccharides de novo. The preliminary performance
tests show that our scoring function, which simply counts the
number of support peaks, gives encouraging results. However,
in most cases, the real structure is scored similarly to several
other oligosaccharides. The more complicated the oligosaccharide structure was, the more optimal solutions we got.
Furthermore, this simple scoring function prefers linear structure to branching structure, maybe because linear structures
often have more hypothetical cross-ring cleavages, and hence
have more (random) chances of matching experimental peaks.
In addition, we found that our post-processing procedure often
improved the rank of the real structure. This is because the real
structure can match more peaks from double cleavages. However, it is not easy to incorporate double cleavage ions into the
scoring function used in the dynamic programming algorithm.
In order to improve scoring function, a probabilistic model is
required to describe how likely it is that one fragmentation
could happen and one ion could be captured by the MS. In
this way, we will be able to model not only the appearance
but also the intensity of the peaks. Such a model has recently
been described for peptide fragmentation in low energy MS
(Zhang, 2004). But similar models have not been well studied for glycans, except that the case study showed that the
relative intensities of different cross-ring peaks were useful
to distinguish 1–4 and 1–6 linkages of isomeric oligosaccharides (Mechref et al., 2003). We are going to generate plenty of
experimental MS/MS spectra of glycans in order to perform
a systematic study of this topic. The long-term objective will
be to develop an intensity-based scoring function that helps
distinguish different linkages and branchings in high resolution. We expect that this new scoring scheme will significantly
reduce the redundancy in the current solutions and therefore
can be used in a large-scale benchmarking test.
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The examples used in this paper are for testing purposes
and not from a real glycomics study. We must address
some practical issues when applying this algorithm to highthroughput glycomics studies, in which we may encounter
large glycans with complex branching structures. One of them
is how to incorporate the knowledge from other glycobiology experiments. For instance, in higher animals, N-linked
glycosylation starts from the additions of five oligosaccharides (two acetylglucosamines and three mannoses) forming
a unique branching core structure, often called a ‘pentamer’,
which is covalently linked to the amino acid residue. Additional variant oligosaccharides can be added to this core
structure and form different branching configurations (e.g.
the hexaose in Fig. 4). In other words, the structures of oligosaccharides from in vivo studies are often variants of some
regular structures, which are largely determined by the glycan
transferases present in the organism. By incorporating this
knowledge, on the one hand, we may reduce the search space
of the optimal solutions; on the other hand, the scoring function needs to be modified according to those glycan structures
that are favored in cells.
The ultimate goal of glycomics and glycoproteomics is to
annotate the structure of glycoproteins, including
(1) identification of the glycoproteins;
(2) characterization of the modification of oligosaccharides;
(3) determination of the modification site, i.e. the residues
to which the oligosaccharides are attached.
In this paper, we partially addressed the second problem.
In fact, the other two problems are equally important, but
more difficult computationally. For example, so far no method
has been proposed to interpret the MS/MS spectra of glycopeptides. We are going to study these computational
problems in the future.
ACKNOWLEDGEMENTS
This work was partially funded by Grant No. (GM24349)
from the National Institute of General Medical Sciences, US
Department of Health and Human Services, and the Indiana
Genomics Initiative, which is funded in part by the Lilly
Endowment, Inc.
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