Glycome Informatics : Methods and Applications

264 Pages·2009·3.181 MB·English

by  Aoki-Kinoshita, Kiyoko F

GLYCOME
INFORMATICS
METHODS AND APPLICATIONS
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GLYCOME
INFORMATICS
METHODS AND APPLICATIONS
KIYOKO F. AOKI-KINOSHITA
CRC Press
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Contents
List of Tables xi
List of Figures xiii
About the Author xvii
1 Introdu
tion to Gly
obiology 1
1.1 Roles of
arbohydrates . . . . . . . . . . . . . . . . . . . . . 1
1.2 Gly
an stru
tures . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Gly
an
lasses . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Gly
an biosynthesis . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.1 N-linked gly
ans . . . . . . . . . . . . . . . . . . . . . 13
1.4.2 O-linked gly
ans . . . . . . . . . . . . . . . . . . . . . 16
1.4.3 Gly
osaminogly
ans (GAGs) . . . . . . . . . . . . . . 16
1.4.4 Gly
osphingolipids (GSLs) . . . . . . . . . . . . . . . 17
1.4.5 GPI an
hors . . . . . . . . . . . . . . . . . . . . . . . 19
1.4.6 LPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.5 Gly
an motifs . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.6 Potential for drug dis
overy . . . . . . . . . . . . . . . . . . . 22
2 Ba
kground 25
2.1 Gly
an nomen
lature . . . . . . . . . . . . . . . . . . . . . . 25
TM
2.1.1 InChI . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.2 (Extended) IUPAC format . . . . . . . . . . . . . . . 27
2.1.3 CarbBank format . . . . . . . . . . . . . . . . . . . . . 30
2.1.4 KCF format . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1.5 LINUCS format . . . . . . . . . . . . . . . . . . . . . 32
2.1.6 BCSDB format . . . . . . . . . . . . . . . . . . . . . . 34
(cid:13)R
2.1.7 Linear Code . . . . . . . . . . . . . . . . . . . . . . 37
2.1.8 Gly
oCT format . . . . . . . . . . . . . . . . . . . . . 40
2.1.9 XML representations . . . . . . . . . . . . . . . . . . . 46
2.2 Le
tin-gly
an intera
tions . . . . . . . . . . . . . . . . . . . . 48
2.2.1 Families and types of le
tins. . . . . . . . . . . . . . . 50
2.2.2 Carbohydrate-binding me
hanism of le
tins . . . . . . 57
2.3 Carbohydrate-
arbohydrateintera
tions . . . . . . . . . . . . 58
vii
viii
3 Databases 61
3.1 Gly
an stru
ture databases . . . . . . . . . . . . . . . . . . . 61
3.1.1 KEGG GLYCAN . . . . . . . . . . . . . . . . . . . . . 62
3.1.2 GLYCOSCIENCES.de . . . . . . . . . . . . . . . . . . 68
3.1.3 CFG . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.1.4 BCSDB . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.1.5 GLYCO3D . . . . . . . . . . . . . . . . . . . . . . . . 85
3.1.6 MonoSa

harideDB . . . . . . . . . . . . . . . . . . . 86
3.1.7 Gly
omeDB . . . . . . . . . . . . . . . . . . . . . . . . 89
3.2 Gly
o-gene databases . . . . . . . . . . . . . . . . . . . . . . 90
3.2.1 KEGG BRITE . . . . . . . . . . . . . . . . . . . . . . 91
3.2.2 CFG . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.2.3 GGDB . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.2.4 CAZy . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.3 Lipid databases . . . . . . . . . . . . . . . . . . . . . . . . . 96
(cid:13)
3.3.1 SphingoMAP . . . . . . . . . . . . . . . . . . . . . . 96
3.3.2 LipidBank . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.3.3 LMSD . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.4 Le
tin databases . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.4.1 Le
tines . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.4.2 Animal Le
tin DB . . . . . . . . . . . . . . . . . . . . 101
3.5 Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.5.1 Gly
oEpitopeDB . . . . . . . . . . . . . . . . . . . . . 101
3.5.2 ECODAB . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.5.3 SugarBindDB . . . . . . . . . . . . . . . . . . . . . . . 106
4 Gly
ome Informati
s 107
4.1 Terminology and notations . . . . . . . . . . . . . . . . . . . 107
4.2 Algorithmi
te
hniques . . . . . . . . . . . . . . . . . . . . . 108
4.2.1 Tree stru
ture alignment. . . . . . . . . . . . . . . . . 108
4.2.2 Linkage analysis using s
ore matri
es. . . . . . . . . . 110
4.2.3 Gly
an variation map . . . . . . . . . . . . . . . . . . 112
4.3 Bioinformati
methods . . . . . . . . . . . . . . . . . . . . . 114
4.3.1 Gly
an stru
ture predi
tion from gly
ogene mi
roarrays 114
4.3.2 Gly
o-gene sequen
e and stru
ture analysis . . . . . . 116
4.3.3 Gly
o-related pathway analysis . . . . . . . . . . . . . 119
4.3.4 Mass spe
tral data annotation . . . . . . . . . . . . . 124
4.4 Data mining te
hniques . . . . . . . . . . . . . . . . . . . . . 130
4.4.1 Kernel methods . . . . . . . . . . . . . . . . . . . . . . 131
4.4.2 Frequent subtree mining . . . . . . . . . . . . . . . . . 138
4.4.3 Probabilisti
models . . . . . . . . . . . . . . . . . . . 142
4.5 Gly
omi
s tools . . . . . . . . . . . . . . . . . . . . . . . . . 173
4.5.1 Visualization tools . . . . . . . . . . . . . . . . . . . . 173
4.5.2 Pathway analysis tools . . . . . . . . . . . . . . . . . . 177
4.5.3 PDB data analysis . . . . . . . . . . . . . . . . . . . . 178
ix
4.5.4 3D analysis tools . . . . . . . . . . . . . . . . . . . . . 179
4.5.5 Mole
ular dynami
s . . . . . . . . . . . . . . . . . . . 182
4.5.6 Spe
tros
opi
tools . . . . . . . . . . . . . . . . . . . . 186
4.5.7 NMR tools . . . . . . . . . . . . . . . . . . . . . . . . 189
5 Potential Resear
h Proje
ts 193
5.1 Sequen
e and stru
tural analyses . . . . . . . . . . . . . . . . 193
5.1.1 Gly
an s
ore matrix . . . . . . . . . . . . . . . . . . . 194
5.1.2 Visualization . . . . . . . . . . . . . . . . . . . . . . . 194
5.2 Databases and te
hniques to integrate heterogeneous data sets 195
5.3 Automated
hara
terizationof gly
ans from MS data . . . . 196
5.4 Predi
tion of gly
ans from data other than MS . . . . . . . . 196
5.5 Biomarkerpredi
tion . . . . . . . . . . . . . . . . . . . . . . 197
5.6 Systems analyses . . . . . . . . . . . . . . . . . . . . . . . . . 197
5.7 Drug dis
overy . . . . . . . . . . . . . . . . . . . . . . . . . . 198
A Sequen
e Analysis Methods 199
A.1 Pairwisesequen
e alignment (dynami
programming) . . . . 199
A.1.1 Dynami
programming. . . . . . . . . . . . . . . . . . 199
A.1.2 Sequen
e alignment . . . . . . . . . . . . . . . . . . . 202
A.2 BLOSUM (BLO
ks Substitution Matrix) . . . . . . . . . . . 205
B Ma
hine Learning Methods 207
B.1 Kernel methods and SVMs . . . . . . . . . . . . . . . . . . . 207
B.2 Hidden Markovmodels . . . . . . . . . . . . . . . . . . . . . 211
B.2.1 The three problems of interest for HMMs . . . . . . . 213
B.2.2 Expe
tation-Maximization (EM) algorithm . . . . . . 215
B.2.3 Hidden tree Markovmodels . . . . . . . . . . . . . . . 216
B.2.4 Pro(cid:12)le Hidden Markovmodels (pro(cid:12)le HMMs) . . . . 218
C Gly
omi
s Te
hnologies 221
C.1 Mass spe
trometry (MS) . . . . . . . . . . . . . . . . . . . . 221
C.1.1 MALDI-MS . . . . . . . . . . . . . . . . . . . . . . . . 222
C.1.2 FT-ICR . . . . . . . . . . . . . . . . . . . . . . . . . . 223
C.1.3 LC-MS (HPLC) . . . . . . . . . . . . . . . . . . . . . 224
C.1.4 Tandem MS . . . . . . . . . . . . . . . . . . . . . . . . 224
C.2 Nu
lear magneti
resonan
e (NMR) . . . . . . . . . . . . . . 225
Referen
es 227
Index 241