Novel Developments in Cryo‐EM of Biological Molecules: Resolution in Time and State Space

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgments
Part I: Single-Particle Cryo-EM of Molecules in Thermal Equilibrium
	Chapter 1: Generalized Single-Particle Cryo-EM: A Historical Perspective
	Chapter 2: Advances in the Field of Single-Particle Cryo-Electron Microscopy Over the Last Decade
		2.1: Improving the Resolution of Asymmetric Structures
		2.2: The Development of Higher-Throughput Methodology
		2.3: Looking to the Future
	Chapter 3: Single-Particle Reconstruction of Biological Molecules: Story in a Sample (Nobel Lecture)
		3.1: The Background
		3.2: Graduate Studies and Harkness Fellowship
		3.3: Postdoctoral Work at the Cavendish Lab: The Concept of Single-Particle Averaging and Reconstruct
		3.4: Move to the Wadsworth Center: From Concept to Practice
		3.5: Determination of Angles and Three-Dimensional Reconstruction
		3.6: Move to Columbia University: Story in a Sample
		3.7: Conclusions
Part II: Machine Learning Applied to Ensembles of Molecules in Thermal Equilibrium: Resolution in State Space
	Chapter 4: Structural Characterization of mRNA-tRNA Translocation Intermediates
		4.1: Results
			4.1.1: Summary of Reconstructions
			4.1.2: Progression of Ribosomal Dynamics and tRNA Movements
			4.1.3: Quantification of Conformational Changes
			4.1.4: Coordination of the Dynamics Between the Ribosome and the tRNAs
			4.1.5: Translating Relative Occupancies of the PRE States into Free-Energy Differences
		4.2: Discussion
		4.3: Materials and Methods
			4.3.1: Image Processing
			4.3.2: Fitting of Crystallographic Structures into Electron Microscopy Densities
	Chapter 5: Trajectories of the Ribosome as a Brownian Nanomachine
		5.1: Conceptual Outline
		5.2: Analytical Procedure
		5.3: Results
		5.4: Discussion
		5.5: Conclusions
	Chapter 6: Continuous Changes in Structure Mapped by Manifold Embedding of Single-Particle Data in Cryo-EM
		6.1: Introduction
		6.2: Mapping of Heterogeneity by Manifold Embedding
		6.3: Results Obtained for the Ribosome
		6.4: Conclusions: Implications for Future Studies of Biological Macromolecules
	Chapter 7: New Opportunities Created by Single-Particle Cryo-EM: The Mapping of Conformational Space
	Chapter 8: POLARIS: Path of Least Action Analysis on Energy Landscapes
		8.1: Introduction
		8.2: Methods
			8.2.1: Image Segmentation
			8.2.2: Permutational Analysis
			8.2.3: Branching Recursion
			8.2.4: Pathway Pruning
		8.3: Results
			8.3.1: Comparison of Results from POLARIS and MEPSA
		8.4: Discussion
			8.4.1: Completeness
			8.4.2: Accuracy
			8.4.3: Complexity
	Chapter 9: Propagation of Conformational Coordinates Across Angular Space in Mapping the Continuum of States from Cryo-EM Data by Manifold Embedding
		9.1: Introduction
		9.2: Methods
			9.2.1: Overall Approach
			9.2.2: Formulating the Selection of Conformational Coordinate as an Optimization Problem
		9.3: Results and Discussion
		9.4: Conclusions
	Chapter 10: Retrieving Functional Pathways of Biomolecules from Single-Particle Snapshots
		10.1: Results
			10.1.1: Energy Landscapes
			10.1.2: Molecular Movies of Ligand Binding in RyR1
			10.1.3: Molecular Dynamics Simulations
			10.1.4: Functional Paths vs. Interpolation Between Discrete Clusters
		10.2: Discussion
		10.3: Methods
			10.3.1: The Upper Limit on the Energy of Accessible Conformational States
			10.3.2: Effect of Coarse-Graining on the Energy Landscapes
			10.3.3: Input Data, Preprocessing Steps, and Analytical Pipeline
			10.3.4: Rabbit Skeletal Muscle RyR1: Purification
			10.3.5: Residual Ca2+ Concentration in the No-Ligands Solution
			10.3.6: Cryo-EM
			10.3.7: Image Processing
			10.3.8: Orientation Recovery
			10.3.9: Geometric (Manifold-Based) Analytical Pipeline
			10.3.10: Limitations
			10.3.11: Estimating Transition Probabilities
			10.3.12: Molecular Dynamics Simulations
			10.3.13: Ligand Association and Binding
			10.3.14:  Distribution of Discrete Cluster Snapshots on Energy Landscapes
			10.3.15:  Estimating the Spatial Resolution of the Density Maps
			10.3.16: Fitting and Refinement of Atomic Coordinates
			10.3.17: Computational Resources
			10.3.18: Reporting summary
		10.4: Data Availability
		10.5: Code Availability
	Chapter 11: A Glycan Gate Controls Opening of the SARS-CoV-2: Spike Protein
		11.1: Results and Discussion
			11.1.1: WE Simulations of Spike Opening
			11.1.2: Comparison with Spike Conformations Detected by ManifoldEM
			11.1.3: The N343 Glycan Gates RBD Opening
			11.1.4: Atomic Details of the Opening Mechanism
		11.2: Conclusions
	Chapter 12: Recovery of Conformational Continuum from Single-Particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth
		12.1: Introduction
		12.2: Simulation of Cryo-EM Ensembles
		12.3: Analysis of Embeddings
			12.3.1: Analysis of Data-Type I
			12.3.2: Analysis of Data-Type II
			12.3.3: Analysis of Data-Type III
			12.3.4: Additional Considerations
		12.4: The ESPER Method
			12.4.1: Eigenfunction Realignment
			12.4.2: Subspace Partitioning
			12.4.3: Conformation Compilation
		12.5: Results with Synthetic Data
		12.6: Results with Experimental Data
			12.6.1: 80S Ribosome
			12.6.2: Ryanodine receptor (RyR1)
		12.7: Discussion
Part III: Non-Equilibrium Methods: Resolution in Time
	Chapter 13: Structural Dynamics of Ribosome Subunit Association Studied by  Mixing-Spraying Time-Resolved Cryogenic Electron Microscopy
		13.1: Introduction
		13.2: Results and Discussion
			13.2.1: Determination of the Reaction Time Window
			13.2.2: Optimization of Data Yield and Quality
			13.2.3: Strategy for Classification
			13.2.4: Time Course of the Subunit Association Reaction
			13.2.5: Conformational Differences of 70S Ribosomes
			13.2.6: Quantifying the Percentages of Ribosome in Different Conformations
			13.2.7: Model of the Structural Dynamics of Ribosome Subunit Association
		13.3: Experimental Procedures
			13.3.1: Mixing-Spraying Device
			13.3.2: Environmental Chamber
			13.3.3: Preparation of Time-Resolved Cryo-EM Grids
			13.3.4: Collection of Time-Resolved Cryo-EM Data Using the Leginon Program
			13.3.5: 3D Classification Using RELION
			13.3.6: Resolution Measurement
			13.3.7: Identification of Intersubunit Bridges in the Cryo-EM Maps of the 70S Ribosome
	Chapter 14: Two Promising Future Developments of Cryo-EM: Capturing Short-Lived States and Mapping a Continuum of States of a Macromolecule
		14.1: Introduction
		14.2: Time-Resolved Cryo-EM
			14.2.1: Case Study and Future Improvements of the Mixing-Spraying Method
		14.3: Mapping and Visualizing a Continuum of States
			14.3.1: The Challenge Posed by Conformational and Compositional Heterogeneity
			14.3.2: Classification of a Continuum of States
		14.4: Outlook
	Chapter 15: Key Intermediates in Ribosome Recycling Visualized by Time-Resolved Cryo-Electron Microscopy
		15.1: Introduction
		15.2: Results
			15.2.1: Biochemical Characterization and Computer Simulations
			15.2.2: Control Experiment Confirming the Stability of the RRF-Bound Post-Termination Complex
			15.2.3: Observed Structures of the 70S Recycling Complex at 140 ms
			15.2.4: 50S Subunit Complexes Observed at the 140 ms Time Point
			15.2.5: 30S Subunit Observed at the 140 ms Time Point
			15.2.6: Observed Structures of the 30S and 50S Subunits after a Long Reaction Time
		15.3: Discussion
		15.4: Experimental Procedures
			15.4.1: Preparation of Time-Resolved Cryo-EM Grids
			15.4.2: Materials
			15.4.3: Control Experiment
			15.4.4: Time-Resolved Cryo-EM Experiment
			15.4.5: Data Acquisition
			15.4.6: Image Processing
	Chapter 16: A Fast and Effective Microfluidic Spraying-Plunging Method for High-Resolution Single-Particle Cryo-EM
		16.1: Introduction
		16.2: Results
			16.2.1: Microsprayer Chip Design and Experimental Setup
			16.2.2: Characterization of the Performance of the Sprayer
			16.2.3: A High-Resolution Apoferritin Structure Determined to Test the Spraying-Plunging Method
		16.3: Discussion
		16.4: STAR*METHODS
	Chapter 17: Time-Resolved Cryo-Electron Microscopy: Recent Progress
		17.1: Quasi-Stability of States in Biology
		17.2: Time-Resolved Cryo-EM
		17.3: Conclusions
	Chapter 18: Time-Resolved Cryo-Electron Microscopy Using a Microfluidic Chip
		18.1: Introduction
		18.2: Materials
			18.2.1: Sample
			18.2.2: Accessories to Fix Port on the Microfluidic Chip
			18.2.3: Customized Apparatus for Grid Preparation
		18.3: Methods
			18.3.1: Experimental Design
			18.3.2: Cryo-EM Grid Preparation
				18.3.2.1: Setting up time-resolved apparatus
				18.3.2.2: Alignment
				18.3.2.3: Time-resolved grid preparation
				18.3.2.4: Cleaning the microfluidic chip
				18.3.2.5: Maintenance: Connecting ports to microfluidic chip
			18.3.3: Data Collection and Processing
		18.4: Notes
	Chapter 19: Late Steps in Bacterial Translation Initiation Visualized Using Time-Resolved Cryo-EM
	Chapter 20: The Structural Basis for Release-Factor Activation During Translation Termination Revealed by Time-Resolved Cryogenic Electron Microscopy
		20.1: Results
		20.2: Discussion
		20.3: Methods
	Chapter 21: A Time-Resolved Cryo-EM Study of Saccharomyces cerevisiae 80S  Ribosome Protein Composition in Response to a Change in Carbon Source
		21.1: Introduction
		21.2: Experimental Section
			21.2.1: Ribosome Isolation and Purification
			21.2.2: Cryo-Electron Microscopy Sample Preparation and Data Collection
			21.2.3: Image Processing
		21.3: Results
			21.3.1: Cryo-EM Studies of S. cerevisiae Ribosomes at Multiple Time Points
			21.3.2: Global Conformational Changes of “Incomplete” 80S Ribosomes
			21.3.3: Locations and Interacting Partners of Proteins eS1 and uL16
			21.3.4: Depletion of eS1 and uL16 in 80S Ribosomes after the Switch of Carbon Source
		21.4: Discussion
			21.4.1: Depletion of Ribosomal Proteins eS1 or uL16 Renders Ribosomes Inactive
			21.4.2: Rapid Response to Carbon Source Switch Effected by Release of Two r-Proteins
			21.4.3: Methodological Limitations and Future Ways to Overcome These
Index