Structural Bioinformatics of Membrane Proteins

Title Page \r......Page 3
Copyright Page \r......Page 4
Table of Contents \r......Page 5
1 Introduction......Page 12
2 Comparative analysis of F/V-type ATPases: example of function cooption?......Page 14
3 Emergence of integral membrane proteins......Page 20
4 Emergence of lipid membranes......Page 21
5 Scenario for the origin and evolution of membranes and membrane proteins......Page 28
References......Page 32
1 Introduction......Page 40
3 Techniques to establish homology or the lack of homology......Page 41
4 Transport protein diversity......Page 42
5 The ABC superfamily\r......Page 43
6 Independent origins for ABC porters......Page 44
7 The phosphoenolpyruvate-dependent sugar transporting phosphotransferase system (PTS)\r......Page 46
8 Independent origins for PTS permeases......Page 48
9 Reverse (retro)-evolution......Page 49
10 Conclusions and perspectives......Page 51
References......Page 52
1 Introduction......Page 55
2.1 Protein Data Bank......Page 56
2.2 Manually curated structure resources of TMPs......Page 57
2.3 TMDET algorithm......Page 58
2.4 PDBTM database......Page 61
2.6 Modeling protein–lipid assembly......Page 62
3 2D structure resources......Page 63
3.1 TOPDB database......Page 64
3.2 TOPDOM database......Page 65
3.3 Prediction methods incorporating experimental results......Page 66
References......Page 67
1 Introduction......Page 70
2 From membrane protein sequence to topologic models......Page 71
2.1 Datasets of membrane proteins......Page 72
2.2 Scoring the accuracy of diff erent methods......Page 73
2.3 Propensity scales versus machine learning-based methods......Page 74
2.4 Methods for optimizing topologic models......Page 75
2.5 Single sequence versus multiple sequence profi le......Page 77
2.7 More methods are bett er than one: CINTHIA......Page 78
2.8 A large-scale annotator of the human proteome: the PONGO system......Page 80
3 From membrane protein sequence to function and structure......Page 82
3.1.1 All-alpha membrane proteins......Page 83
3.1.2 All-beta membrane proteins......Page 84
3.2.1 The cluster of glyceroporins......Page 85
3.2.2 The cluster of multidrug transporter proteins (EmrE proteins)......Page 87
References......Page 89
1 Introduction......Page 92
1.2 ß-Contact and tertiary structure prediction......Page 93
2.1 Benchmark sets......Page 94
3.1.1 Neural network implementation......Page 96
3.1.2 Two-class prediction (ß, –)......Page 97
3.1.3 Three-class prediction (M, C, –)......Page 98
3.3.1 Search energy......Page 99
3.3.2 Template usage......Page 100
3.3.3 Move types......Page 101
4.1.1 Secondary structure evaluation metrics......Page 102
4.1.2 Results using SetTransfold......Page 103
4.2.1 ß-Contact evaluation metrics......Page 104
4.3 Tertiary structure prediction results......Page 105
4.3.1 Tertiary structure evaluation metrics......Page 106
4.3.3 Self-consistency results......Page 107
References......Page 108
1 Introduction......Page 112
2.1 Transmembrane substitution rates......Page 114
3 Overview of TM MSA methods......Page 116
3.1.1 Profile pre-processing......Page 117
3.1.2 Bipartite alignment scheme......Page 118
3.1.3 Tree-based consistency iteration......Page 119
3.2 Bipartite MSA compared to standard MSA......Page 120
3.3 Comparing PRA LINE-TM with non-TM MSA methods......Page 121
4 Benchmarking transmembrane alignments......Page 123
4.1 Defi ning TM regions......Page 124
5 Applications for TM multiple alignments......Page 125
6 Current bottlenecks......Page 126
7 Avenues for improvement......Page 127
References......Page 128
1 Introduction......Page 132
3 Interface helices......Page 134
3.1 Prediction of interface helices......Page 136
4 Helical kinks in transmembrane helices......Page 137
5 Re-entrant regions......Page 138
5.1.1 TOP-MOD......Page 139
5.1.4 MEMSAT-SVM......Page 140
6 Prediction of the Z-coordinate......Page 141
7 Free energy of membrane insertion ?G......Page 142
8 The frequency of re-entrant regions and interface helices......Page 143
References......Page 144
1 Introduction......Page 146
2.3 Topology mapping......Page 148
2.5 Internal structural repeats – evidence of former gene duplication events......Page 149
3.1 The small multidrug resistance family: one family, different topologies......Page 151
3.2 The DUF606 family contains fused genes......Page 152
4.2 Ductin......Page 153
4.3 Hepatitis B virus L protein......Page 154
4.5 TatA......Page 155
5.1 SecG......Page 156
References......Page 157
1 Introduction......Page 160
2 Hydrophobicity analysis......Page 163
3 Amino acid propensity scales......Page 164
4 Methods using sequence conservation......Page 167
5 Applications of burial prediction......Page 171
References......Page 172
1 Introduction......Page 174
2 Technical approaches to identify transmembrane helix–helix interfaces......Page 176
3.1 Amino acid side-chain packing......Page 179
3.2 GxxxG motifs......Page 180
3.3 Hydrogen bonding......Page 182
3.4 Charge–charge interactions......Page 183
3.5 Aromatic interactions......Page 185
4 Dynamic TMD–TMD interactions......Page 186
Acknowledgments......Page 187
References......Page 188
1.1 Two-stage hypothesis......Page 196
2.1 Membrane constraints and interactions......Page 197
2.2 Loop constraints......Page 198
3.2 Motifs and stabilizing specific interactions......Page 199
3.3 The five types of specific stabilizing interhelical interactions considered......Page 200
3.4 Structural hot spots......Page 201
3.5 Experimental data on residue contributions to stabilization......Page 202
3.6 Particularly stabilizing interactions as geometric constraints......Page 203
3.8 Constraint perspective and underlying rigid-body geometry......Page 205
3.9 Iterative reassembly of full TM helix bundles using interactions of the five types......Page 207
3.10 The sets of the five types of particularly favorable interactions determine the packing of helices in the native structures of a diverse test set......Page 208
3.11 Distribution of particularly stabilizing residues, folding funnels, and the construction of low-energy minima......Page 209
4 Conservation and diversity of determining sets of stabilizing interactions......Page 210
5 Determining sets, multiple states, and motion......Page 212
5.1 Multiple states and motion in the ErbB family......Page 213
References......Page 216
1 Introduction......Page 221
2 Biological background......Page 222
2.1 Diversity of helix–helix contacts in membrane proteins......Page 223
2.2 Frequency of residue contacts in membrane and soluble proteins......Page 224
3.1 Hydrophobicity-based predictions......Page 225
3.2 Amino acid propensity scales derived from membrane protein sequences and structures......Page 226
3.4 Best performing methods in the field of lipid accessibility......Page 227
4.1 Co-evolving residues in membrane proteins......Page 228
4.2 Prediction of helix–helix contacts with machine-learning techniques......Page 229
5 Prediction of helix interactions......Page 231
6 Modeling of membrane proteins with predicted contact information......Page 233
References......Page 235
1 Introduction......Page 238
3.1 De novo membrane protein structure prediction......Page 239
3.1.2 The first MP structure prediction methods developed during the past decade......Page 241
3.1.3.1 Folding with predicted constraints......Page 244
3.1.3.2 Contact predictors......Page 246
3.1.4 MP-specifi c energy functions for decoy discrimination......Page 247
3.2 Sequence-based modeling with experimental constraints......Page 248
3.3 Comparative modeling of MP structures......Page 251
4 Conclusions and future directions......Page 252
References......Page 253
1 Introduction......Page 257
2 A short history......Page 258
3.1 Rhodopsin......Page 265
3.2 Ligand-mediated GPCRs......Page 267
4.2 Loop IV–V, cysteine bridges, and ligand binding......Page 272
5 The future......Page 276
References......Page 279
LIST OF CONTRIBUTORS......Page 285