Structural Bioinformatics of Membrane Proteins (eBook)

D. Frishman (Herausgeber)

eBook Download: PDF

2011 | 2010
XI, 281 Seiten
Springer Wien (Verlag)
978-3-7091-0045-5 (ISBN)

Infos

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.

Inhalt

Title Page 2
Copyright Page 3
Table of Contents 4
Evolutionary origins of membrane proteins 11
1 Introduction 11
2 Comparative analysis of F/V-type ATPases: example of function cooption? 13
3 Emergence of integral membrane proteins 19
4 Emergence of lipid membranes 20
5 Scenario for the origin and evolution of membranes and membrane proteins 27
Acknowledgments 31
References 31
Molecular archeological studies of transmembrane transport systems 39
1 Introduction 39
2 Molecular transport 40
3 Techniques to establish homology or the lack of homology 40
4 Transport protein diversity 41
5 The ABC superfamily 42
6 Independent origins for ABC porters 43
7 The phosphoenolpyruvate-dependent sugar transporting phosphotransferase system (PTS) 45
8 Independent origins for PTS permeases 47
9 Reverse (retro)-evolution 48
10 Conclusions and perspectives 50
References 51
Resource for structure related information on transmembrane proteins 54
1 Introduction 54
2 3D structure resources 55
2.1 Protein Data Bank 55
2.2 Manually curated structure resources of TMPs 56
2.3 TMDET algorithm 57
2.4 PDBTM database 60
2.5 OPM database 61
2.6 Modeling protein–lipid assembly 61
3 2D structure resources 62
3.1 TOPDB database 63
3.2 TOPDOM database 64
3.3 Prediction methods incorporating experimental results 65
Acknowledgments 66
References 66
Topology prediction of membrane proteins: how distantly related homologs come into play 69
1 Introduction 69
2 From membrane protein sequence to topologic models 70
2.1 Datasets of membrane proteins 71
2.2 Scoring the accuracy of diff erent methods 72
2.3 Propensity scales versus machine learning-based methods 73
2.4 Methods for optimizing topologic models 74
2.5 Single sequence versus multiple sequence profi le 76
2.6 Prediction of signal peptides and GPI-anchors 77
2.7 More methods are bett er than one: CINTHIA 77
2.8 A large-scale annotator of the human proteome: the PONGO system 79
3 From membrane protein sequence to function and structure 81
3.1 Membrane proteins: how many with known functions and folds? 82
3.1.1 All-alpha membrane proteins 82
3.1.2 All-beta membrane proteins 83
3.2 What do BAR clusters contain? 84
3.2.1 The cluster of glyceroporins 84
3.2.2 The cluster of multidrug transporter proteins (EmrE proteins) 86
3.3.3 The cluster of P-glycoproteins 88
References 88
Transmembrane beta-barrel protein structure prediction 91
1 Introduction 91
1.1 1D feature prediction 92
1.2 ß-Contact and tertiary structure prediction 92
2 Data 93
2.1 Benchmark sets 93
2.2 Cross-validation 95
2.3 Template construction 95
3 Methods 95
3.1 Secondary structure prediction 95
3.1.1 Neural network implementation 95
3.1.2 Two-class prediction (ß, –) 96
3.1.3 Three-class prediction (M, C, –) 97
3.2 ß-Contact prediction 98
3.3 Tertiary structure prediction 98
3.3.1 Search energy 98
3.3.2 Template usage 99
3.3.3 Move types 100
3.3.4 Conformational search 101
4 Results 101
4.1 Secondary structure prediction results 101
4.1.1 Secondary structure evaluation metrics 101
4.1.2 Results using SetTransfold 102
4.1.3 Results using SetPRED-TMBB 103
4.2 ß-Contact prediction results 103
4.2.1 ß-Contact evaluation metrics 103
4.2.2 Results using SetTransfold 104
4.2.3 Results using SetPRED-TMBB 104
4.3 Tertiary structure prediction results 104
4.3.1 Tertiary structure evaluation metrics 105
4.3.2 Prediction results 106
4.3.3 Self-consistency results 106
5 Discussion 107
References 107
Multiple alignment of transmembrane protein sequences 111
1 Introduction 111
2 Factors influencing the alignment of transmembrane proteins 113
2.1 Transmembrane substitution rates 113
2.2 Transmembrane alignment gaps 115
3 Overview of TM MSA methods 115
3.1 TM-aware multiple sequence alignment by the Praline method 116
3.1.1 Profile pre-processing 116
3.1.2 Bipartite alignment scheme 117
3.1.3 Tree-based consistency iteration 118
3.2 Bipartite MSA compared to standard MSA 119
3.3 Comparing PRA LINE-TM with non-TM MSA methods 120
4 Benchmarking transmembrane alignments 122
4.1 Defi ning TM regions 123
5 Applications for TM multiple alignments 124
5.1 Homology searches of TM proteins 125
6 Current bottlenecks 125
7 Avenues for improvement 126
8 Conclusions 127
References 127
Prediction of re-entrant regions and other structural features beyond traditional topology models 131
1 Introduction 131
2 Background 133
2.1 The Z-coordinate as a measure of distance to the membrane 133
3 Interface helices 133
3.1 Prediction of interface helices 135
3.2 Prediction of amphipathic membrane anchors 136
4 Helical kinks in transmembrane helices 136
4.1 Prediction of helix kinks 137
5 Re-entrant regions 137
5.1 Prediction of re-entrant regions 138
5.1.1 TOP-MOD 138
5.1.2 TMloop 139
5.1.3 OCTOPUS 139
5.1.4 MEMSAT-SVM 139
6 Prediction of the Z-coordinate 140
7 Free energy of membrane insertion .G 141
8 The frequency of re-entrant regions and interface helices 142
9 Summary 143
References 143
Dual-topology: one sequence, two topologies 145
1 Introduction 145
2 Background 147
2.1 A brief history of dual-topology research 147
2.2 The difference between dual- and multiple-topology 147
2.3 Topology mapping 147
2.4 Arginines and lysines are important for the topology 148
2.5 Internal structural repeats – evidence of former gene duplication events 148
3 Prediction of dual-topology 150
3.1 The small multidrug resistance family: one family, different topologies 150
3.2 The DUF606 family contains fused genes 151
4 Examples of membrane proteins with dual- or multiple-topology 152
4.1 MRAP 152
4.2 Ductin 152
4.3 Hepatitis B virus L protein 153
4.4 Hepatitis C virus protein NS4B 154
4.5 TatA 154
4.6 PrP 155
5 Using topology inversion for function 155
5.1 SecG 155
6 Using dual-topology as a targeting system 156
6.1 Cytochrome p450-2E1 156
6.2 Epoxide hydrolase 156
References 156
Predicting the burial/exposure status of transmembrane residues in helical membrane proteins 159
1 Introduction 159
2 Hydrophobicity analysis 162
3 Amino acid propensity scales 163
4 Methods using sequence conservation 166
5 Applications of burial prediction 170
References 171
Helix–helix interaction patt erns in membrane proteins 173
1 Introduction 173
2 Technical approaches to identify transmembrane helix–helix interfaces 175
3 Structure of transmembrane helix–helix interfaces 178
3.1 Amino acid side-chain packing 178
3.2 GxxxG motifs 179
3.3 Hydrogen bonding 181
3.4 Charge–charge interactions 182
3.5 Aromatic interactions 184
4 Dynamic TMD–TMD interactions 185
Acknowledgments 186
References 187
Predicting residue and helix contacts in membrane proteins 195
1 Introduction 195
2 Biological background 196
2.1 Diversity of helix–helix contacts in membrane proteins 197
2.2 Frequency of residue contacts in membrane and soluble proteins 198
3 Prediction of lipid accessibility 199
3.1 Hydrophobicity-based predictions 199
3.2 Amino acid propensity scales derived from membrane protein sequences and structures 200
3.3 Sequence conservation of exposed and buried transmembrane residues 201
3.4 Best performing methods in the field of lipid accessibility 201
4 Prediction of helix–helix contacts 202
4.1 Co-evolving residues in membrane proteins 202
4.2 Prediction of helix–helix contacts with machine-learning techniques 203
5 Prediction of helix interactions 205
6 Modeling of membrane proteins with predicted contact information 207
Acknowledgement 209
References 209
Natural constraints, folding, motion, and structural stability in transmembrane helical proteins 212
1 Folding background 212
1.1 Two-stage hypothesis 212
1.2 Translocon-aided folding 213
2 Overview of non-interhelical stabilizing forces and natural constraints 213
2.1 Membrane constraints and interactions 213
2.1.1 Hydrophobic mismatch 214
2.1.2 Specifi c fl anking and anchoring interactions with polar headgroups 214
2.1.3 Positive-inside rule 214
2.2 Loop constraints 214
3 Interhelical interactions and constraints 215
3.1 Helix–helix packing 215
3.2 Motifs and stabilizing specific interactions 215
3.2.1 Packing motifs 216
3.2.2 Hydrogen bonds 216
3.2.3 Aromatic interactions 216
3.2.4 Salt bridges 216
3.3 The five types of specific stabilizing interhelical interactions considered 216
3.4 Structural hot spots 217
3.5 Experimental data on residue contributions to stabilization 218
3.6 Particularly stabilizing interactions as geometric constraints 219
3.7 Helix pairs revisited 221
3.8 Constraint perspective and underlying rigid-body geometry 221
3.9 Iterative reassembly of full TM helix bundles using interactions of the five types 223
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 224
3.11 Distribution of particularly stabilizing residues, folding funnels, and the construction of low-energy minima 225
3.12 Cooperativity with packing 226
3.13 Static structures versus ensembles 226
4 Conservation and diversity of determining sets of stabilizing interactions 226
4.1 Conservation and diversity of the determining sets of interactions of bR 228
5 Determining sets, multiple states, and motion 228
5.1 Multiple states and motion in the ErbB family 229
6 Conclusion 232
References 232
Prediction of three-dimensional transmembrane helical protein structures 237
1 Introduction 237
2 Goal of the chapter 238
3 Methods 238
3.1 De novo membrane protein structure prediction 238
3.1.1 MP topology predictions 240
3.1.2 The first MP structure prediction methods developed during the past decade 240
3.1.3 Solutions to the conformational search problem: folding with predicted constraints and contact predictors 243
3.1.3.1 Folding with predicted constraints 243
3.1.3.2 Contact predictors 245
3.1.4 MP-specifi c energy functions for decoy discrimination 246
3.2 Sequence-based modeling with experimental constraints 247
3.3 Comparative modeling of MP structures 250
4 Conclusions and future directions 251
References 252
GPCRs: past, present, and future 256
1 Introduction 256
2 A short history 257
3 GPCR structures 264
3.1 Rhodopsin 264
3.2 Ligand-mediated GPCRs 266
4 From sequence to structure 271
4.1 The conserved cysteine bridge in the extracellular domain 271
4.2 Loop IV–V, cysteine bridges, and ligand binding 271
5 The future 275
References 278
LIST OF CONTRIBUTORS 284

Produkt-Details

• Erscheint lt. Verlag: 22.1.2011
• Zusatzinfo: XI, 281 p.
• Verlagsort: Vienna
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Naturwissenschaften ► Chemie
• Themenwelt: Technik
• Schlagworte: algorithms • Bioinformatics • Biology • Databases • Evolution • Membrane Proteins • Protein • proteins • Structural
• ISBN-10: 3-7091-0045-3 / 3709100453
• ISBN-13: 978-3-7091-0045-5 / 9783709100455