Structural Bioinformatics of Membrane Proteins

✍ Scribed by D. Frishman

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 294
• Edition: 1st Edition.
• Category: Library

✦ Synopsis

This book is the first one specifically dedicated to the structural bioinformatics of membrane proteins. With a focus on membrane proteins from the perspective of bioinformatics, the present work covers a broad spectrum of topics in evolution, structure, function, and bioinformatics of membrane proteins focusing on the most recent experimental results. Leaders in the field who have recently reported breakthrough advances cover algorithms, databases and their applications to the subject. The increasing number of recently solved membrane protein structures makes the expert coverage presented here very timely. Structural bioinformatics of membrane proteins has been an active area of research over the last thee decades and proves to be a growing field of interest.

✦ Table of Contents

Cover......Page 1
Structural Bioinformatics
of Membrane Proteins......Page 3
ISBN 9783709100448......Page 4
Table of Contents
......Page 6
1 Introduction......Page 14
2 Comparative analysis of F/V-type ATPases: example of function cooption?......Page 16
3 Emergence of integral membrane proteins......Page 22
4 Emergence of lipid membranes......Page 23
5 Scenario for the origin and evolution of membranes and membrane proteins......Page 30
References......Page 34
1 Introduction......Page 42
3 Techniques to establish homology or the lack of homology......Page 43
4 Transport protein diversity......Page 44
5 The ABC superfamily
......Page 45
6 Independent origins for ABC porters......Page 46
7 The phosphoenolpyruvate-dependent sugar transporting phosphotransferase system (PTS)
......Page 48
8 Independent origins for PTS permeases......Page 50
9 Reverse (retro)-evolution......Page 51
10 Conclusions and perspectives......Page 53
References......Page 54
1 Introduction......Page 58
2.1 Protein Data Bank......Page 59
2.2 Manually curated structure resources of TMPs......Page 60
2.3 TMDET algorithm......Page 61
2.4 PDBTM database......Page 64
2.6 Modeling protein–lipid assembly......Page 65
3 2D structure resources......Page 66
3.1 TOPDB database......Page 67
3.2 TOPDOM database......Page 68
3.3 Prediction methods incorporating experimental results......Page 69
References......Page 70
1 Introduction......Page 74
2 From membrane protein sequence to topologic models......Page 75
2.1 Datasets of membrane proteins......Page 76
2.2 Scoring the accuracy of diff erent methods......Page 77
2.3 Propensity scales versus machine learning-based methods......Page 78
2.4 Methods for optimizing topologic models......Page 79
2.5 Single sequence versus multiple sequence profi le......Page 81
2.7 More methods are bett er than one: CINTHIA......Page 82
2.8 A large-scale annotator of the human proteome: the PONGO system......Page 84
3 From membrane protein sequence to function and structure......Page 86
3.1.1 All-alpha membrane proteins......Page 87
3.1.2 All-beta membrane proteins......Page 88
3.2.1 The cluster of glyceroporins......Page 89
3.2.2 The cluster of multidrug transporter proteins (EmrE proteins)......Page 91
References......Page 93
1 Introduction......Page 96
1.2 β-Contact and tertiary structure prediction......Page 97
2.1 Benchmark sets......Page 98
3.1.1 Neural network implementation......Page 100
3.1.2 Two-class prediction (β, –)......Page 101
3.1.3 Three-class prediction (M, C, –)......Page 102
3.3.1 Search energy......Page 103
3.3.2 Template usage......Page 104
3.3.3 Move types......Page 105
4.1.1 Secondary structure evaluation metrics......Page 106
4.1.2 Results using SetTransfold......Page 107
4.2.1 β-Contact evaluation metrics......Page 108
4.3 Tertiary structure prediction results......Page 109
4.3.1 Tertiary structure evaluation metrics......Page 110
4.3.3 Self-consistency results......Page 111
References......Page 112
1 Introduction......Page 116
2.1 Transmembrane substitution rates......Page 118
3 Overview of TM MSA methods......Page 120
3.1.1 Profile pre-processing......Page 121
3.1.2 Bipartite alignment scheme......Page 122
3.1.3 Tree-based consistency iteration......Page 123
3.2 Bipartite MSA compared to standard MSA......Page 124
3.3 Comparing PRA LINE-TM with non-TM MSA methods......Page 125
4 Benchmarking transmembrane alignments......Page 127
4.1 Defi ning TM regions......Page 128
5 Applications for TM multiple alignments......Page 129
6 Current bottlenecks......Page 130
7 Avenues for improvement......Page 131
References......Page 132
1 Introduction......Page 136
3 Interface helices......Page 138
3.1 Prediction of interface helices......Page 140
4 Helical kinks in transmembrane helices......Page 141
5 Re-entrant regions......Page 142
5.1.1 TOP-MOD......Page 143
5.1.4 MEMSAT-SVM......Page 144
6 Prediction of the Z-coordinate......Page 145
7 Free energy of membrane insertion ΔG......Page 146
8 The frequency of re-entrant regions and interface helices......Page 147
References......Page 148
1 Introduction......Page 150
2.3 Topology mapping......Page 152
2.5 Internal structural repeats – evidence of former gene duplication events......Page 153
3.1 The small multidrug resistance family: one family, different topologies......Page 155
3.2 The DUF606 family contains fused genes......Page 156
4.2 Ductin......Page 157
4.3 Hepatitis B virus L protein......Page 158
4.5 TatA......Page 159
5.1 SecG......Page 160
References......Page 161
1 Introduction......Page 164
2 Hydrophobicity analysis......Page 167
3 Amino acid propensity scales......Page 168
4 Methods using sequence conservation......Page 171
5 Applications of burial prediction......Page 175
References......Page 176
1 Introduction......Page 178
2 Technical approaches to identify transmembrane helix–helix interfaces......Page 180
3.1 Amino acid side-chain packing......Page 183
3.2 GxxxG motifs......Page 184
3.3 Hydrogen bonding......Page 186
3.4 Charge–charge interactions......Page 187
3.5 Aromatic interactions......Page 189
4 Dynamic TMD–TMD interactions......Page 190
Acknowledgments......Page 191
References......Page 192
1 Introduction......Page 200
2 Biological background......Page 201
2.1 Diversity of helix–helix contacts in membrane proteins......Page 202
2.2 Frequency of residue contacts in membrane and soluble proteins......Page 203
3.1 Hydrophobicity-based predictions......Page 204
3.2 Amino acid propensity scales derived from membrane protein sequences and structures......Page 205
3.4 Best performing methods in the field of lipid accessibility......Page 206
4.1 Co-evolving residues in membrane proteins......Page 207
4.2 Prediction of helix–helix contacts with machine-learning techniques......Page 208
5 Prediction of helix interactions......Page 210
6 Modeling of membrane proteins with predicted contact information......Page 212
References......Page 214
1.1 Two-stage hypothesis......Page 218
2.1 Membrane constraints and interactions......Page 219
2.2 Loop constraints......Page 220
3.2 Motifs and stabilizing specific interactions......Page 221
3.3 The five types of specific stabilizing interhelical interactions considered......Page 222
3.4 Structural hot spots......Page 223
3.5 Experimental data on residue contributions to stabilization......Page 224
3.6 Particularly stabilizing interactions as geometric constraints......Page 225
3.8 Constraint perspective and underlying rigid-body geometry......Page 227
3.9 Iterative reassembly of full TM helix bundles using interactions of the five types......Page 229
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 230
3.11 Distribution of particularly stabilizing residues, folding funnels, and the construction of low-energy minima......Page 231
4 Conservation and diversity of determining sets of stabilizing interactions......Page 232
5 Determining sets, multiple states, and motion......Page 234
5.1 Multiple states and motion in the ErbB family......Page 235
References......Page 238
1 Introduction......Page 244
3.1 De novo membrane protein structure prediction......Page 245
3.1.2 The first MP structure prediction methods developed during the past decade......Page 247
3.1.3.1 Folding with predicted constraints......Page 250
3.1.3.2 Contact predictors......Page 252
3.1.4 MP-specifi c energy functions for decoy discrimination......Page 253
3.2 Sequence-based modeling with experimental constraints......Page 254
3.3 Comparative modeling of MP structures......Page 257
4 Conclusions and future directions......Page 258
References......Page 259
1 Introduction......Page 264
2 A short history......Page 265
3.1 Rhodopsin......Page 272
3.2 Ligand-mediated GPCRs......Page 274
4.2 Loop IV–V, cysteine bridges, and ligand binding......Page 279
5 The future......Page 283
References......Page 286
LIST OF CONTRIBUTORS......Page 292