Review Article
Molecular structure of amyloid fibrils: insights from solid-state NMR
- Robert Tycko
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- Published online by Cambridge University Press:
- 13 June 2006, pp. 1-55
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1. Introduction 2
2. Sources of structural information in solid-state NMR data 5
2.1 General remarks 5
2.2 Chemical shifts, linewidths, and magic-angle spinning 6
2.3 Dipole–dipole couplings and dipolar recoupling 8
2.4 Tensor correlation techniques 12
2.5 Solid-state NMR of aligned samples 14
2.6 Indirect sources of structural information 15
2.7 Sample preparation for solid-state NMR 15
3. Levels of structure in amyloid fibrils 18
4. Molecular structure of β-amyloid fibrils 25
4.1 Self-propagating, molecular-level polymorphism in Aβ1–40 fibrils 25
4.2 Structural model for Aβ1-40 fibrils 28
4.3 Staggering of β-strands in Aβ1-40 fibrils 32
4.4 Structure of Aβ1-42 fibrils 34
4.5 Structure of fibrils formed by short β-amyloid fragments 34
4.6 Structures of non-fibrillar aggregates 35
5. Molecular structure of other amyloid fibrils 36
5.1 Ure2p10–39 and full-length Ure2p fibrils 36
5.2 TTR105–115 fibrils 38
5.3 HET-s fibrils 38
5.4 Amylin fibrils 39
5.5 PrP fibrils 39
5.6 ccβ fibrils 40
5.7 α-synuclein fibrils 40
5.8 Calcitonin fibrils 41
6. Data relevant to various proposals regarding amyloid structure 41
6.1 β-helical models for amyloid fibrils 41
6.2 Amyloid fibrils as water-filled nanotubes 42
6.3 Domain swapping in amyloid fibrils 42
6.4 The parallel superpleated β-structure model 43
6.5 α-sheet structures in amyloid fibrils 43
7. Conclusions 44
8. Acknowledgments 46
9. References 46
Solid-state nuclear magnetic resonance (NMR) measurements have made major contributions to our understanding of the molecular structures of amyloid fibrils, including fibrils formed by the β-amyloid peptide associated with Alzheimer's disease, by proteins associated with fungal prions, and by a variety of other polypeptides. Because solid-state NMR techniques can be used to determine interatomic distances (both intramolecular and intermolecular), place constraints on backbone and side-chain torsion angles, and identify tertiary and quaternary contacts, full molecular models for amyloid fibrils can be developed from solid-state NMR data, especially when supplemented by lower-resolution structural constraints from electron microscopy and other sources. In addition, solid-state NMR data can be used as experimental tests of various proposals and hypotheses regarding the mechanisms of amyloid formation, the nature of intermediate structures, and the common structural features within amyloid fibrils. This review introduces the basic experimental and conceptual principles behind solid-state NMR methods that are applicable to amyloid fibrils, reviews the information about amyloid structures that has been obtained to date with these methods, and discusses how solid-state NMR data provide insights into the molecular interactions that stabilize amyloid structures, the generic propensity of polypeptide chains to form amyloid fibrils, and a number of related issues that are of current interest in the amyloid field.
Computational biology in the study of cardiac ion channels and cell electrophysiology
- Yoram Rudy, Jonathan R. Silva
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- Published online by Cambridge University Press:
- 19 July 2006, pp. 57-116
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1. Prologue 58
2. The Hodgkin–Huxley formalism for computing the action potential 59
2.1 The axon action potential model 59
2.2 Cardiac action potential models 62
3. Ion-channel based formulation of the action potential 65
3.1 Ion-channel structure 65
3.2 Markov models of ion-channel kinetics 66
3.3 Role of selected ion channels in rate dependence of the cardiac action potential 71
3.4 Physiological implications of IKs subunit interaction 77
3.5 Mechanism of cardiac action potential rate-adaptation is species dependent 78
4. Simulating ion-channel mutations and their electrophysiological consequences 81
4.1 Mutations in SCN5A, the gene that encodes the cardiac sodium channel 82
4.1.1 The ΔKPQ mutation and LQT3 82
4.1.2 SCN5A mutation that underlies a dual phenotype 87
4.2 Mutations in HERG, the gene that encodes IKr: re-examination of the ‘gain of function/loss of function’ concept 94
4.3 Role of IKs as ‘repolarization reserve’ 100
5. Modeling cell signaling in electrophysiology 102
5.1 CaMKII regulation of the Ca2+ transient 102
5.2 The β-adrenergic signaling cascade 105
6. Epilogue 107
7. Acknowledgments 108
8. References 109
The cardiac cell is a complex biological system where various processes interact to generate electrical excitation (the action potential, AP) and contraction. During AP generation, membrane ion channels interact nonlinearly with dynamically changing ionic concentrations and varying transmembrane voltage, and are subject to regulatory processes. In recent years, a large body of knowledge has accumulated on the molecular structure of cardiac ion channels, their function, and their modification by genetic mutations that are associated with cardiac arrhythmias and sudden death. However, ion channels are typically studied in isolation (in expression systems or isolated membrane patches), away from the physiological environment of the cell where they interact to generate the AP. A major challenge remains the integration of ion-channel properties into the functioning, complex and highly interactive cell system, with the objective to relate molecular-level processes and their modification by disease to whole-cell function and clinical phenotype. In this article we describe how computational biology can be used to achieve such integration. We explain how mathematical (Markov) models of ion-channel kinetics are incorporated into integrated models of cardiac cells to compute the AP. We provide examples of mathematical (computer) simulations of physiological and pathological phenomena, including AP adaptation to changes in heart rate, genetic mutations in SCN5A and HERG genes that are associated with fatal cardiac arrhythmias, and effects of the CaMKII regulatory pathway and β-adrenergic cascade on the cell electrophysiological function.