Structural bioinformatics of membrane proteins

With a focus on membrane proteins from the perspective of bioinformatics, this book covers a broad spectrum of topics in evolution, structure, function and bioinformatics of membrane proteins focusing on the most recent experimental results.

Saved in:
Bibliographic Details
Main Author Frishman, Dmitrij
Format eBook Book
LanguageEnglish
Published Vienna Springer 2010
Springer Wien
Springer Vienna
Edition1
Subjects
Computational Biology > methods
Computational Biology/Bioinformatics
Computer Science
Membrane Proteins
Molecular structure
Systems Biology
Theoretical and Computational Chemistry
Online AccessGet full text
ISBN3709100445
9783709100448
9783709116807
3709116805
DOI10.1007/978-3-7091-0045-5

Cover

Table of Contents:
  • 3.1.1 Profile pre-processing -- 3.1.2 Bipartite alignment scheme -- 3.1.3 Tree-based consistency iteration -- 3.2 Bipartite MSA compared to standard MSA -- 3.3 Comparing PRA LINE-TM with non-TM MSA methods -- 4 Benchmarking transmembrane alignments -- 4.1 Defi ning TM regions -- 5 Applications for TM multiple alignments -- 5.1 Homology searches of TM proteins -- 6 Current bottlenecks -- 7 Avenues for improvement -- 8 Conclusions -- References -- Prediction of re-entrant regions and other structural features beyond traditional topology models -- 1 Introduction -- 2 Background -- 2.1 The Z-coordinate as a measure of distance to the membrane -- 3 Interface helices -- 3.1 Prediction of interface helices -- 3.2 Prediction of amphipathic membrane anchors -- 4 Helical kinks in transmembrane helices -- 4.1 Prediction of helix kinks -- 5 Re-entrant regions -- 5.1 Prediction of re-entrant regions -- 5.1.1 TOP-MOD -- 5.1.2 TMloop -- 5.1.3 OCTOPUS -- 5.1.4 MEMSAT-SVM -- 6 Prediction of the Z-coordinate -- 7 Free energy of membrane insertion ΔG -- 8 The frequency of re-entrant regions and interface helices -- 9 Summary -- References -- Dual-topology: one sequence, two topologies -- 1 Introduction -- 2 Background -- 2.1 A brief history of dual-topology research -- 2.2 The difference between dual- and multiple-topology -- 2.3 Topology mapping -- 2.4 Arginines and lysines are important for the topology -- 2.5 Internal structural repeats - evidence of former gene duplication events -- 3 Prediction of dual-topology -- 3.1 The small multidrug resistance family: one family, different topologies -- 3.2 The DUF606 family contains fused genes -- 4 Examples of membrane proteins with dual- or multiple-topology -- 4.1 MRAP -- 4.2 Ductin -- 4.3 Hepatitis B virus L protein -- 4.4 Hepatitis C virus protein NS4B -- 4.5 TatA -- 4.6 PrP -- 5 Using topology inversion for function
  • 5.1 SecG -- 6 Using dual-topology as a targeting system -- 6.1 Cytochrome p450-2E1 -- 6.2 Epoxide hydrolase -- References -- Predicting the burial/exposure status of transmembrane residues in helical membrane proteins -- 1 Introduction -- 2 Hydrophobicity analysis -- 3 Amino acid propensity scales -- 4 Methods using sequence conservation -- 5 Applications of burial prediction -- References -- Helix-helix interaction patt erns in membrane proteins -- 1 Introduction -- 2 Technical approaches to identify transmembrane helix-helix interfaces -- 3 Structure of transmembrane helix-helix interfaces -- 3.1 Amino acid side-chain packing -- 3.2 GxxxG motifs -- 3.3 Hydrogen bonding -- 3.4 Charge-charge interactions -- 3.5 Aromatic interactions -- 4 Dynamic TMD-TMD interactions -- Acknowledgments -- References -- Predicting residue and helix contacts in membrane proteins -- 1 Introduction -- 2 Biological background -- 2.1 Diversity of helix-helix contacts in membrane proteins -- 2.2 Frequency of residue contacts in membrane and soluble proteins -- 3 Prediction of lipid accessibility -- 3.1 Hydrophobicity-based predictions -- 3.2 Amino acid propensity scales derived from membrane protein sequences and structures -- 3.3 Sequence conservation of exposed and buried transmembrane residues -- 3.4 Best performing methods in the field of lipid accessibility -- 4 Prediction of helix-helix contacts -- 4.1 Co-evolving residues in membrane proteins -- 4.2 Prediction of helix-helix contacts with machine-learning techniques -- 5 Prediction of helix interactions -- 6 Modeling of membrane proteins with predicted contact information -- Acknowledgement -- References -- Natural constraints, folding, motion, and structural stability in transmembrane helical proteins -- 1 Folding background -- 1.1 Two-stage hypothesis -- 1.2 Translocon-aided folding
  • 2 Overview of non-interhelical stabilizing forces and natural constraints -- 2.1 Membrane constraints and interactions -- 2.1.1 Hydrophobic mismatch -- 2.1.2 Specifi c fl anking and anchoring interactions with polar headgroups -- 2.1.3 Positive-inside rule -- 2.2 Loop constraints -- 3 Interhelical interactions and constraints -- 3.1 Helix-helix packing -- 3.2 Motifs and stabilizing specific interactions -- 3.2.1 Packing motifs -- 3.2.2 Hydrogen bonds -- 3.2.3 Aromatic interactions -- 3.2.4 Salt bridges -- 3.3 The five types of specific stabilizing interhelical interactions considered -- 3.4 Structural hot spots -- 3.5 Experimental data on residue contributions to stabilization -- 3.6 Particularly stabilizing interactions as geometric constraints -- 3.7 Helix pairs revisited -- 3.8 Constraint perspective and underlying rigid-body geometry -- 3.9 Iterative reassembly of full TM helix bundles using interactions of the five types -- 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 -- 3.11 Distribution of particularly stabilizing residues, folding funnels, and the construction of low-energy minima -- 3.12 Cooperativity with packing -- 3.13 Static structures versus ensembles -- 4 Conservation and diversity of determining sets of stabilizing interactions -- 4.1 Conservation and diversity of the determining sets of interactions of bR -- 5 Determining sets, multiple states, and motion -- 5.1 Multiple states and motion in the ErbB family -- 6 Conclusion -- References -- Prediction of three-dimensional transmembrane helical protein structures -- 1 Introduction -- 2 Goal of the chapter -- 3 Methods -- 3.1 De novo membrane protein structure prediction -- 3.1.1 MP topology predictions
  • Intro -- Title Page -- Copyright Page -- Table of Contents -- Evolutionary origins of membrane proteins -- 1 Introduction -- 2 Comparative analysis of F/V-type ATPases: example of function cooption? -- 3 Emergence of integral membrane proteins -- 4 Emergence of lipid membranes -- 5 Scenario for the origin and evolution of membranes and membrane proteins -- Acknowledgments -- References -- Molecular archeological studies of transmembrane transport systems -- 1 Introduction -- 2 Molecular transport -- 3 Techniques to establish homology or the lack of homology -- 4 Transport protein diversity -- 5 The ABC superfamily -- 6 Independent origins for ABC porters -- 7 The phosphoenolpyruvate-dependent sugar transporting phosphotransferase system (PTS) -- 8 Independent origins for PTS permeases -- 9 Reverse (retro)-evolution -- 10 Conclusions and perspectives -- References -- Resource for structure related information on transmembrane proteins -- 1 Introduction -- 2 3D structure resources -- 2.1 Protein Data Bank -- 2.2 Manually curated structure resources of TMPs -- 2.3 TMDET algorithm -- 2.4 PDBTM database -- 2.5 OPM database -- 2.6 Modeling protein-lipid assembly -- 3 2D structure resources -- 3.1 TOPDB database -- 3.2 TOPDOM database -- 3.3 Prediction methods incorporating experimental results -- Acknowledgments -- References -- Topology prediction of membrane proteins: how distantly related homologs come into play -- 1 Introduction -- 2 From membrane protein sequence to topologic models -- 2.1 Datasets of membrane proteins -- 2.2 Scoring the accuracy of diff erent methods -- 2.3 Propensity scales versus machine learning-based methods -- 2.4 Methods for optimizing topologic models -- 2.5 Single sequence versus multiple sequence profi le -- 2.6 Prediction of signal peptides and GPI-anchors -- 2.7 More methods are bett er than one: CINTHIA
  • 2.8 A large-scale annotator of the human proteome: the PONGO system -- 3 From membrane protein sequence to function and structure -- 3.1 Membrane proteins: how many with known functions and folds? -- 3.1.1 All-alpha membrane proteins -- 3.1.2 All-beta membrane proteins -- 3.2 What do BAR clusters contain? -- 3.2.1 The cluster of glyceroporins -- 3.2.2 The cluster of multidrug transporter proteins (EmrE proteins) -- 3.3.3 The cluster of P-glycoproteins -- References -- Transmembrane beta-barrel protein structure prediction -- 1 Introduction -- 1.1 1D feature prediction -- 1.2 β-Contact and tertiary structure prediction -- 2 Data -- 2.1 Benchmark sets -- 2.2 Cross-validation -- 2.3 Template construction -- 3 Methods -- 3.1 Secondary structure prediction -- 3.1.1 Neural network implementation -- 3.1.2 Two-class prediction (β, -) -- 3.1.3 Three-class prediction (M, C, -) -- 3.2 β-Contact prediction -- 3.3 Tertiary structure prediction -- 3.3.1 Search energy -- 3.3.2 Template usage -- 3.3.3 Move types -- 3.3.4 Conformational search -- 4 Results -- 4.1 Secondary structure prediction results -- 4.1.1 Secondary structure evaluation metrics -- 4.1.2 Results using SetTransfold -- 4.1.3 Results using SetPRED-TMBB -- 4.2 β-Contact prediction results -- 4.2.1 β-Contact evaluation metrics -- 4.2.2 Results using SetTransfold -- 4.2.3 Results using SetPRED-TMBB -- 4.3 Tertiary structure prediction results -- 4.3.1 Tertiary structure evaluation metrics -- 4.3.2 Prediction results -- 4.3.3 Self-consistency results -- 5 Discussion -- References -- Multiple alignment of transmembrane protein sequences -- 1 Introduction -- 2 Factors influencing the alignment of transmembrane proteins -- 2.1 Transmembrane substitution rates -- 2.2 Transmembrane alignment gaps -- 3 Overview of TM MSA methods -- 3.1 TM-aware multiple sequence alignment by the Praline method
  • 3.1.2 The first MP structure prediction methods developed during the past decade