Structure and Intrinsic Disorder in Enzymology

Front Cover
Structure and Intrinsic Disorder in Enzymology
Copyright Page
Contents
List of contributors
Preface
1 Enzymology: early insights
1.1 Introduction
1.2 Isolation and purification of proteins
1.3 The dawn of structural biology
1.4 The early work on enzyme kinetics
1.5 Assaying enzymes
1.6 Enzyme immobilization
1.7 Applied enzymology and white biotechnology
1.8 Enzymes in neat solvents
1.9 Some myths about applications of enzymes
1.10 Conclusions
References
2 Deep mutational scanning to probe specificity determinants in proteins
2.1 Proteins, enzymes, and disorder
2.2 Deep mutational scanning—a high-throughput method to explore protein sequence–function landscapes
2.3 Deep mutational scanning of globular proteins
2.3.1 Deep mutational scanning of enzymes
2.3.1.1 TEM-1 β-lactamase enzyme
2.3.1.2 E3 ligase in the ubiquitination pathway
2.3.1.3 APH(3′)II kinase and ERK2 kinase
2.3.1.4 Levoglucosan kinase and amide hydrolase
2.3.1.5 Influenza A neuraminidase and Streptococcus pyogenes Cas9
2.3.1.6 GTPase domain of RAS kinase, dihydrofolate reductase, and cystathionine-β-synthase
2.3.1.7 MurJ, a lipid II flippase
2.3.1.8 Thiopurine S-methyltransferase
2.3.1.9 Alkaline phosphatase PafA
2.3.2 Deep mutational scanning of nonenzymatic globular proteins
2.3.2.1 Deep mutational scanning to study mutational tolerance and classification of sites (buried versus active sites)
2.3.2.2 Deep mutational scanning to understand epistasis, protein–protein interactions, and allostery
2.3.2.3 Deep mutational scanning for epitope mapping and structure prediction
2.3.2.4 Deep mutational scanning to understand membrane energetics
2.4 Study of sequence–disorder–function relationships in Intrinsically Disordered Proteins (IDPs)
2.4.1 Deep mutational scanning of intrinsically disordered regions in transcription factors
2.4.2 Deep mutational scanning to probe aggregation of intrinsically disordered proteins
2.4.3 Nonproteinogenic deep mutational scanning in intrinsically disordered proteins
2.4.4 Deep mutational scanning to investigate residue-specific contributions to partner binding in intrinsically disordered...
2.5 Discussion
Acknowledgments
Author contributions
References
3 Protein flexibility and cryoenzymology: the trade-off between stability and catalytic rates
Abbreviations
3.1 Origin of cryoenzymology
3.2 Search for the solvents suitable for the low-temperature studies
3.3 Looking into the protein folding at subzero temperatures
3.4 Cryoenzymology: analysis of the enzymatic reactions at subzero temperatures
3.5 X-ray cryoenzymology
3.6 Cryo-electron microscopy enzymology
3.7 Cryoenzymology and psychrophiles
3.8 Concluding remarks
References
4 Thermodynamic perspective of protein disorder and phase separation: model systems
4.1 Introduction
4.1.1 Thermodynamic uses of protein disorder
4.1.2 Role of modeling and simulation
4.2 Conformational landscapes
4.2.1 Populations and free energy
4.2.2 Enthalpy of order
4.2.3 Entropy of disorder
4.3 Determinants of the disordered ensemble
4.3.1 Sequence composition: collapsed and extended disorder
4.3.2 Polymer physics and the quality of water as a disordered region solvent
4.4 Lessons from a protein backbone model
4.4.1 Polyglycine: a protein backbone and disordered region model
4.4.2 Protein backbone collapse: a solubility-driven event
4.4.2.1 Solvation of the protein backbone lacks signatures of classic hydrophobicity
4.4.2.2 What drives the collapse of the protein backbone?
4.4.2.3 Primary sequence: tuning the properties of the protein backbone
4.5 Aggregation of many peptides—liquid–liquid phase separation is a type of order
4.5.1 Solubility limits measure more than hydrophobicity
4.5.2 Backbone conformational entropy and liquid–liquid phase separation
4.5.3 Towards tuning the properties of liquid–liquid phase separation
4.6 Future perspectives
4.6.1 Simple systems and modeling approaches
4.6.2 Conformational entropy reservoir: thermodynamic coupling through order–disorder transitions
Acknowledgments
References
5 Structure and disorder: protein functions depend on this new binary transforming lock-and-key into structure-function con...
5.1 Introduction
5.2 Keys in locks and hands in gloves: classical representations of protein functionality
5.3 Structure-function paradigm cannot be stretched far enough to include protein “moonlighting,” multifunctionality, bindi...
5.4 A new player in the block: functional proteins without unique structures
5.5 Structural heterogeneity of IDPs and IDRs
5.6 Proteoforms as a solution for the “one gene–many proteins” challenge
5.7 Intrinsic disorder, proteoforms, and protein-structure continuum
References
6 Methods for measuring structural disorder in proteins
6.1 Introduction
6.2 Obtaining hints of intrinsic disorder
6.2.1 Anticipating intrinsic disorder from the amino acid sequence
6.2.2 SDS-PAGE and limited proteolysis
6.2.3 Resistance to denaturing conditions
6.3 Assessing protein hydrodynamic properties
6.3.1 Size-exclusion chromatography
6.3.2 Dynamic light scattering
6.3.3 Analytical ultracentrifugation
6.3.4 Small-angle X-ray scattering
6.4 Assessing protein secondary structure content
6.4.1 Circular dichroism spectroscopy
6.4.2 Fourier transform infrared spectroscopy
6.4.3 Nuclear magnetic resonance spectroscopy
6.5 Assessing protein tertiary structure
6.5.1 Near-ultraviolet circular dichroism spectroscopy
6.5.2 Differential scanning calorimetry
6.5.3 Fluorescence spectroscopy
6.5.4 Electrospray ionization mass spectrometry
6.5.5 Ion mobility-mass spectrometry
6.5.6 Hydrogen–deuterium exchange approaches
6.6 High-speed atomic force microscopy
6.7 Approaches relying on protein labeling and/or site-directed mutagenesis
6.7.1 Fluorescence resonance energy transfer
6.7.2 Vibrational spectroscopy of cyanylated cysteines
6.7.3 Electron paramagnetic resonance spectroscopy
6.7.4 Tryptophan triplet cysteine quenching
6.8 In vivo assessment of disorder
6.9 Modeling intrinsically disordered proteins as conformational ensembles
6.10 Assessing binding events
Acknowledgments
References
7 Prediction of protein structure and intrinsic disorder in the era of deep learning
7.1 Introduction
7.2 A brief overview of protein structure prediction approaches
7.3 Critical assessment of structure prediction—structure prediction evaluation
7.4 Machine learning revolution through deep learning
7.5 Deep learning methods in structure prediction
7.6 Predicting protein disorder
7.7 Predicting the functions of disordered regions
7.8 Conclusions
References
8 Roles of intrinsically disordered regions in phosphoinositide 3-kinase biocatalysis
8.1 Biochemistry of PI3K enzymes
8.2 Class I PI3K enzymes
8.3 Class II PI3K enzymes
8.4 Class III PI3K enzyme
8.5 Normal and aberrant cellular functions of PI3K enzymes
8.6 Normal functions of class I PI3K in cellular signaling and physiology
8.7 Aberrant hyperactivation of PI3K as a major driver of diseases
8.8 Structural biology and biocatalysis of class I PI3K family
8.9 Role of intrinsically disordered regions in the PI3K functions
References
9 The various facets of protein promiscuity: not just broad specificity of proteins
9.1 Introduction
9.2 Protein specificity
9.3 Protein promiscuity as a driver of protein evolution
9.4 Types of promiscuity
9.5 Promiscuity of alkaline phosphatase superfamily
9.6 Quantifying enzyme promiscuity
9.7 Engineering enzyme promiscuity
9.8 Promiscuity in protein–protein interactions
9.9 Calmodulin promiscuity
9.10 α-Synuclein promiscuity, multifunctionality, and polypathogenicity
9.11 Promiscuity in drug design
9.12 Conclusions
References
10 Role of plasticity and disorder in protein moonlighting: blurring of lines between biocatalysts and other biologically a...
10.1 Introduction
10.2 Description of “moonlighting” as a phenomenon
10.3 What is a binding site?
10.4 Moonlighting proteins in health and diseases
10.5 Protein conformational plasticity
10.6 Disordered moonlighting regions
10.7 Intrinsic disorder roots of moonlighting: multifunctionality as a consequence of the disorder-based structural heterog...
10.8 Moonlighting in virulence activity of pathogens
10.9 Conclusions and future perspectives
References
11 Molten globular enzymes
Abbreviations
11.1 Enzymes as a cornerstone of the “lock-and-key” model of protein functionality
11.2 Molten globular enzymes: machines at the edge of stability
11.2.1 Dihydrofolate reductase mutants with amino acid replacements in the active center
11.2.2 Circularly permuted dihydrofolate reductase
11.2.3 Truncated Δ131Δ mutant of staphylococcal nuclease
11.2.4 Double-point mutant F34W/W140F of staphylococcal nuclease
11.2.5 C-terminally truncated mutants of the capsid protease from Semliki Forest virus
11.2.6 Monomeric chorismate mutase from Methanococcus jannaschii
11.2.7 Simplified chorismate mutase with 9-amino acid alphabet
11.2.8 Molten globular active sites of the detoxication enzyme glutathione transferase A1–1
11.2.9 Urzyme: an ancestral tryptophanyl-tRNA synthetase precursor
11.2.10 De-evolved dephospho-CoA kinase
11.2.11 Catalytically active alkaline molten globular enzyme: 5-aminolevulinate synthase
11.2.12 Nucleolytic activity of folding intermediates of RNase T1
11.2.13 Nonnative intermediate transiently populated during folding of the acylphosphatase from Sulfolobus solfataricus
11.2.14 Oxaldies: artificial, rationally designed catalytic polypeptides
11.2.15 Biologically active de novo proteins
11.3 Concluding remarks
Acknowledgments
References
12 Intrinsic disorder and allosteric regulation
12.1 The ensemble view of allostery and its applications
12.2 The role of intrinsic disorder in protein allosteric regulation: representative cases
12.3 Small allosteric molecules targeting intrinsically disordered proteins
12.4 Allostery of multidomain proteins with disordered linkers
12.5 Phase separation of intrinsically disordered proteins
12.6 Computational methods to study allostery in disordered proteins and mechanism of phase separation
12.7 Outlook
References
13 Macromolecular crowding: how it affects protein structure, disorder, and catalysis
13.1 Introduction
13.2 What do we know about crowding inside cells?
13.3 Crowding agents employed to simulate intracellular environments
13.4 How crowding affects catalytic activity of enzymes
13.5 Effect of crowding on proteins with intrinsic disorder
13.6 Effect of crowding on protein assembly, aggregation, and amyloid formation
13.7 Miscellaneous recent observations
13.8 Conclusions and future perspectives
References
14 Intrinsic disorder and posttranslational modification: an evolutionary perspective
14.1 Introduction
14.2 PTMs prevail in intrinsically disordered regions and intrinsically disordered regions are enriched in PTMs
14.3 Links between posttranslational modification and disorder-to-order transitions of IDRs
14.4 An evolutionary perspective
14.4.1 Intrinsically disordered regions and the evolution of phosphorylation sites
14.4.2 Phosphorylation sites in intrinsically disordered regions and pathogenic mutations in human proteins
14.4.3 Intrinsically disordered regions and modifications on lysine and arginine residues
14.5 Intrinsically disordered regions as fertile substrates for the evolution of posttranslational modification cross talk
14.6 Conclusions
References
15 The roles of prion-like domains in amyloid formation, phase separation, and solubility
15.1 Discovery of yeast prion proteins
15.2 Prion domains
15.3 Amyloid fibrils
15.4 Kinetics and thermodynamics of amyloid formation
15.5 Prions as protein-based genetic elements
15.6 Stable, nontransmissible assemblies as a form of cellular memory
15.7 PrLDs in the formation of biomolecular condensates
15.8 FUS as a model for LLPS by PrLDs
15.9 Misregulation of phase behavior
15.10 PrLDs as regulators of phase behavior
15.11 Conclusion
Acknowledgments
References
16 Disordered protein networks as mechanistic drivers of membrane remodeling and endocytosis
16.1 Introduction
16.2 Disordered proteins as sensors of membrane curvature
16.3 Disordered protein networks as catalysts of trafficking vesicle assembly
16.4 Disordered proteins as drivers of membrane curvature
16.4.1 Repulsive interactions drive convex curvature
16.4.2 Attractive interactions drive concave curvature
16.5 Disordered protein networks as drivers of vesicle coating
16.6 Disordered proteins as drivers of vesicle uncoating
16.7 Conclusion and outlook
Acknowledgments
References
17 How binding to surfaces affects disorder?
17.1 Introduction
17.2 Lipid bilayers
17.3 Membrane fusion
17.4 Membrane curvature
17.5 Hemifusion stalk
17.6 The fusion pore
17.7 Membrane proteins
17.8 Binding proteins to surfaces
17.9 Synaptotagmin-1 C2A and C2B domains
17.10 The Bin/Amphiphysin/Rvs domain with an N-terminal amphipathic helix
17.11 The dynamin family
17.12 Intrinsic disorder
17.13 The acrosome reaction
17.14 Proteins within the acrosome reaction
17.15 α-Synuclein
17.16 Computational methods
17.17 Collective variables
17.18 Coordination and radial distribution function
17.19 Radius of gyration
17.20 Root mean square fluctuations
17.21 Lindemann disorder index
17.22 Conclusions
References
Index
Back Cover