# Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Typical Workflow of Single-Particle Cryo-EM

## 3. Sources of the Cryo-EM Image Data Heterogeneity

## 4. Conventional Approaches to Study Cryo-EM Data Heterogeneity

#### 4.1. Conventional Multivariate Statistical Analysis (MSA)

#### 4.2. Regularized Likelihood Approach

## 5. Continuous Structural Heterogeneity Derived from Cryo-EM Data

#### 5.1. Covariance Matrix Estimation

#### 5.2. Hyper-Molecules

#### 5.3. 3DVA (3D Variability Analysis) Approach

#### 5.4. CryoDRGN

## 6. Mapping Energy Landscape from Cryo-EM Data

## 7. Hybrid Approaches with Molecular Dynamic Simulations

#### 7.1. Detecting Structural Variability Based on the Resolution Anisotropy

#### 7.2. Molecular Dynamics Flexible Fitting (MDFF)

## 8. Time-Resolved Cryo-EM Studies

## 9. Interpretation of the Extracted Information for Biomolecular Perturbation

## 10. Summary and Outlook

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Callaway, E. Revolutionary Cryo-EM Is Taking over Structural Biology. Nature
**2020**, 578, 201. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bai, X.-C.; McMullan, G.; Scheres, S.H.W. How Cryo-EM Is Revolutionizing Structural Biology. Trends Biochem. Sci.
**2015**, 40, 49–57. [Google Scholar] [CrossRef] [PubMed] - Guerrini, N.; Turchetta, R.; Van Hoften, G.; Henderson, R.; McMullan, G.; Faruqi, A.R. A High Frame Rate, 16 Million Pixels, Radiation Hard CMOS Sensor. J. Instrum.
**2011**, 6, C03003. [Google Scholar] [CrossRef] - Mooney, P.; Contarato, D.; Denes, P.; Gubbens, A.; Lee, B.; Lent, M.; Agard, D. A High-Speed Electron-Counting Direct Detection Camera for TEM. Microsc. Microanal.
**2011**, 17, 1004–1005. [Google Scholar] [CrossRef] [Green Version] - Milazzo, A.-C.; Moldovan, G.; Lanman, J.; Jin, L.; Bouwer, J.C.; Klienfelder, S.; Peltier, S.T.; Ellisman, M.H.; Kirkland, A.I.; Xuong, N.-H. Characterization of a Direct Detection Device Imaging Camera for Transmission Electron Microscopy. Ultramicroscopy
**2010**, 110, 744–747. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Frank, J. Three-Dimensional Electron. Microscopy of Macromolecular Assemblies: Visualization of Biological Molecules in Their Native State; Oxford University Press: New York, NY, USA, 2006; ISBN 9780198034384. [Google Scholar]
- Van Heel, M. Angular Reconstitution: A Posteriori Assignment of Projection Directions for 3D Reconstruction. Ultramicroscopy
**1987**, 21, 111–123. [Google Scholar] [CrossRef] - Glaeseral, R.M. Radiation Damage and High Resolution Biological Electron Microscopy. Proc. Annu. Meet. Electron. Microsc. Soc. Am.
**1973**, 31, 226–227. [Google Scholar] [CrossRef] - Isaacson, M.; Johnson, D.; Crewe, A.V. Electron Beam Excitation and Damage of Biological Molecules; Its Implications for Specimen Damage in Electron Microscopy. Radiat. Res.
**1973**, 55, 205–224. [Google Scholar] [CrossRef] - Glaeser, R.M.; Taylor, K.A. Radiation Damage Relative to Transmission Electron Microscopy of Biological Specimens at Low Temperature: A Review. J. Microsc.
**1978**, 112, 127–138. [Google Scholar] [CrossRef] - Hayward, S.B.; Glaeser, R.M. Radiation Damage of Purple Membrane at Low Temperature. Ultramicroscopy
**1979**, 4, 201–210. [Google Scholar] [CrossRef] - Cheng, A.; Eng, E.T.; Alink, L.; Rice, W.J.; Jordan, K.D.; Kim, L.Y.; Potter, C.S.; Carragher, B. High Resolution Single Particle Cryo-Electron Microscopy Using Beam-Image Shift. J. Struct. Biol.
**2018**, 204, 270–275. [Google Scholar] [CrossRef] [PubMed] - Nakane, T.; Kimanius, D.; Lindahl, E.; Scheres, S.H. Characterisation of Molecular Motions in Cryo-EM Single-Particle Data by Multi-Body Refinement in RELION. Elife
**2018**, 7, e36861. [Google Scholar] [CrossRef] [PubMed] - Mitra, K.; Frank, J. Ribosome Dynamics: Insights from Atomic Structure Modeling into Cryo-Electron Microscopy Maps. Annu. Rev. Biophys. Biomol. Struct.
**2006**, 35, 299–317. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tama, F.; Valle, M.; Frank, J.; Brooks, C.L., 3rd. Dynamic Reorganization of the Functionally Active Ribosome Explored by Normal Mode Analysis and Cryo-Electron Microscopy. Proc. Natl. Acad. Sci. USA
**2003**, 100, 9319–9323. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Zhong, E.D.; Bepler, T.; Berger, B.; Davis, J.H. CryoDRGN: Reconstruction of Heterogeneous Cryo-EM Structures Using Neural Networks. Nat. Methods
**2021**, 18, 176–185. [Google Scholar] [CrossRef] [PubMed] - Fica, S.M.; Nagai, K. Cryo-Electron Microscopy Snapshots of the Spliceosome: Structural Insights into a Dynamic Ribonucleoprotein Machine. Nat. Struct. Mol. Biol.
**2017**, 24, 791–799. [Google Scholar] [CrossRef] [PubMed] - Haselbach, D.; Komarov, I.; Agafonov, D.E.; Hartmuth, K.; Graf, B.; Dybkov, O.; Urlaub, H.; Kastner, B.; Lührmann, R.; Stark, H. Structure and Conformational Dynamics of the Human Spliceosomal Bact Complex. Cell
**2018**, 172, 454–464. [Google Scholar] [CrossRef] [Green Version] - Marino, J.; Schertler, G.F.X. A Set of Common Movements within GPCR-G-Protein Complexes from Variability Analysis of Cryo-EM Datasets. J. Struct. Biol.
**2021**, 213, 107699. [Google Scholar] [CrossRef] - Dong, M.; Deganutti, G.; Piper, S.J.; Liang, Y.-L.; Khoshouei, M.; Belousoff, M.J.; Harikumar, K.G.; Reynolds, C.A.; Glukhova, A.; Furness, S.G.B.; et al. Structure and Dynamics of the Active Gs-Coupled Human Secretin Receptor. Nat. Commun.
**2020**, 11, 4137. [Google Scholar] [CrossRef] - Josephs, T.M.; Belousoff, M.J.; Liang, Y.-L.; Piper, S.J.; Cao, J.; Garama, D.J.; Leach, K.; Gregory, K.J.; Christopoulos, A.; Hay, D.L.; et al. Structure and Dynamics of the CGRP Receptor in Apo and Peptide-Bound Forms. Science
**2021**, 372, eabf7258. [Google Scholar] [CrossRef] - Hilger, D. The Role of Structural Dynamics in GPCR-Mediated Signaling. FEBS J.
**2021**, 288, 2461–2489. [Google Scholar] [CrossRef] [PubMed] - Chiu, P.-L.; Kelly, D.F.; Walz, T. The Use of Trehalose in the Preparation of Specimens for Molecular Electron Microscopy. Micron
**2011**, 42, 762–772. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Dubochet, J.; McDowall, A.W. Vitrification of Pure Water for Electron Microscopy. J. Microsc.
**1981**, 124, 3–4. [Google Scholar] [CrossRef] - Adrian, M.; Dubochet, J.; Lepault, J.; McDowall, A.W. Cryo-Electron Microscopy of Viruses. Nature
**1984**, 308, 32–36. [Google Scholar] [CrossRef] - Cowley, J.M. Image Contrast in a Transmission Scanning Electron Microscope. Appl. Phys. Lett.
**1969**, 15, 58–59. [Google Scholar] [CrossRef] - Spence, J.C.H. High-Resolution Electron Microscopy; OUP Oxford University Press: Oxford, UK, 2013; ISBN 9780191508400. [Google Scholar]
- Erickson, H.; Klug, A. Measurement and Compensation of Defocusing and Aberrations by Fourier Processing of Electron Micrographs. Philos. Trans. R. Soc. Lond.
**1971**, 261, 105–118. [Google Scholar] [CrossRef] - Wade, R.H. A Brief Look at Imaging and Contrast Transfer. Ultramicroscopy
**1992**, 46, 145–156. [Google Scholar] [CrossRef] - Wu, S.; Armache, J.-P.; Cheng, Y. Single-Particle Cryo-EM Data Acquisition by Using Direct Electron Detection Camera. Microscopy
**2016**, 65, 35–41. [Google Scholar] [CrossRef] [Green Version] - Chiu, P.-L.; Li, X.; Li, Z.; Beckett, B.; Brilot, A.F.; Grigorieff, N.; Agard, D.A.; Cheng, Y.; Walz, T. Evaluation of Super-Resolution Performance of the K2 Electron-Counting Camera Using 2D Crystals of Aquaporin-0. J. Struct. Biol.
**2015**, 192, 163–173. [Google Scholar] [CrossRef] [Green Version] - Li, X.; Mooney, P.; Zheng, S.; Booth, C.R.; Braunfeld, M.B.; Gubbens, S.; Agard, D.A.; Cheng, Y. Electron Counting and Beam-Induced Motion Correction Enable near-Atomic-Resolution Single-Particle Cryo-EM. Nat. Methods
**2013**, 10, 584–590. [Google Scholar] [CrossRef] [PubMed] [Green Version] - D’Imprima, E.; Kühlbrandt, W. Current Limitations to High-Resolution Structure Determination by Single-Particle CryoEM. Q. Rev. Biophys.
**2021**, 54, e4. [Google Scholar] [CrossRef] [PubMed] - Vinothkumar, K.R.; Henderson, R. Single Particle Electron Cryomicroscopy: Trends, Issues and Future Perspective. Q. Rev. Biophys.
**2016**, 49, e13. [Google Scholar] [CrossRef] [Green Version] - Russo, C.J.; Henderson, R. Charge Accumulation in Electron Cryomicroscopy. Ultramicroscopy
**2018**, 187, 43–49. [Google Scholar] [CrossRef] [PubMed] - Russo, C.J.; Henderson, R. Microscopic Charge Fluctuations Cause Minimal Contrast Loss in CryoEM. Ultramicroscopy
**2018**, 187, 56–63. [Google Scholar] [CrossRef] - Glaeser, R.M.; Han, B.-G.; Csencsits, R.; Killilea, A.; Pulk, A.; Cate, J.H.D. Factors That Influence the Formation and Stability of Thin, Cryo-EM Specimens. Biophys. J.
**2016**, 110, 749–755. [Google Scholar] [CrossRef] [Green Version] - Grant, T.; Grigorieff, N. Measuring the Optimal Exposure for Single Particle Cryo-EM Using a 2.6 Å Reconstruction of Rotavirus VP6. Elife
**2015**, 4, e06980. [Google Scholar] [CrossRef] - Zheng, S.Q.; Palovcak, E.; Armache, J.-P.; Verba, K.A.; Cheng, Y.; Agard, D.A. MotionCor2: Anisotropic Correction of Beam-Induced Motion for Improved Cryo-Electron Microscopy. Nat. Methods
**2017**, 14, 331–332. [Google Scholar] [CrossRef] [Green Version] - Sigworth, F.J.; Doerschuk, P.C.; Carazo, J.-M.; Scheres, S.H.W. An Introduction to Maximum-Likelihood Methods in Cryo-EM. Methods Enzymol.
**2010**, 482, 263–294. [Google Scholar] [CrossRef] [PubMed] - Sigworth, F.J. A Maximum-Likelihood Approach to Single-Particle Image Refinement. J. Struct. Biol.
**1998**, 122, 328–339. [Google Scholar] [CrossRef] [PubMed] - Scheres, S.H.W. Classification of Structural Heterogeneity by Maximum-Likelihood Methods. Methods Enzymol.
**2010**, 482, 295–320. [Google Scholar] [CrossRef] [PubMed] - Dempster, A.P.; Laird, N.M.; Rubin, D.B. Maximum Likelihood from Incomplete Data via the EM Algorithm. J. R. Stat. Soc.
**1977**, 39, 1–22. [Google Scholar] [CrossRef] - Radermacher, M. Weighted Back-Projection Methods. In Electron Tomography; Springer: New York, NY, USA, 2008; pp. 245–273. ISBN 9780387312347. [Google Scholar]
- Nogales, E.; Scheres, S.H.W. Cryo-EM: A Unique Tool for the Visualization of Macromolecular Complexity. Mol. Cell
**2015**, 58, 677–689. [Google Scholar] [CrossRef] [Green Version] - Henderson, R. Avoiding the Pitfalls of Single Particle Cryo-Electron Microscopy: Einstein from Noise. Proc. Natl. Acad. Sci. USA
**2013**, 110, 18037–18041. [Google Scholar] [CrossRef] [Green Version] - Scheres, S.H.W.; Chen, S. Prevention of Overfitting in Cryo-EM Structure Determination. Nat. Methods
**2012**, 9, 853–854. [Google Scholar] [CrossRef] [PubMed] - Penczek, P.A. Fundamentals of Three-Dimensional Reconstruction from Projections. In Methods in Enzymology; Elsevier: Amsterdam, The Netherlands, 2010; pp. 1–33. [Google Scholar]
- Allen, T.W.; Andersen, O.S.; Roux, B. On the Importance of Atomic Fluctuations, Protein Flexibility, and Solvent in Ion Permeation. J. Gen. Physiol.
**2004**, 124, 679–690. [Google Scholar] [CrossRef] [Green Version] - Skjaerven, L.; Reuter, N.; Martinez, A. Dynamics, Flexibility and Ligand-Induced Conformational Changes in Biological Macromolecules: A Computational Approach. Future Med. Chem.
**2011**, 3, 2079–2100. [Google Scholar] [CrossRef] [PubMed] - Bock, L.V.; Grubmüller, H. Effects of Cryo-EM Cooling on Structural Ensembles. Nat. Commun.
**2022**, 13, 1709. [Google Scholar] [CrossRef] - Swint-Kruse, L.; Brown, C.S. Resmap: Automated Representation of Macromolecular Interfaces as Two-Dimensional Networks. Bioinformatics
**2005**, 21, 3327–3328. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Aiyer, S.; Zhang, C.; Baldwin, P.R.; Lyumkis, D. Evaluating Local and Directional Resolution of Cryo-EM Density Maps. Methods Mol. Biol.
**2021**, 2215, 161–187. [Google Scholar] [CrossRef] - Local Resolution of Cryo-EM Maps with MonoRes. Nat. Methods
**2018**, 15, 246. [CrossRef] - Nandi, P.; Li, S.; Columbres, R.C.A.; Wang, F.; Williams, D.R.; Poh, Y.-P.; Chou, T.-F.; Chiu, P.-L. Structural and Functional Analysis of Disease-Linked P97 ATPase Mutant Complexes. Int. J. Mol. Sci.
**2021**, 22, 8079. [Google Scholar] [CrossRef] [PubMed] - Guo, H.; Rubinstein, J.L. Cryo-EM of ATP Synthases. Curr. Opin. Struct. Biol.
**2018**, 52, 71–79. [Google Scholar] [CrossRef] - Yang, J.-H.; Williams, D.; Kandiah, E.; Fromme, P.; Chiu, P.-L. Structural Basis of Redox Modulation on Chloroplast ATP Synthase. Commun. Biol.
**2020**, 3, 482. [Google Scholar] [CrossRef] [PubMed] - Hisabori, T.; Sunamura, E.-I.; Kim, Y.; Konno, H. The Chloroplast ATP Synthase Features the Characteristic Redox Regulation Machinery. Antioxid. Redox Signal.
**2013**, 19, 1846–1854. [Google Scholar] [CrossRef] [PubMed] [Green Version] - van Heel, M.; Frank, J. Use of Multivariate Statistics in Analysing the Images of Biological Macromolecules. Ultramicroscopy
**1981**, 6, 187–194. [Google Scholar] [CrossRef] - van Heel, M. Multivariate Statistical Classification of Noisy Images (Randomly Oriented Biological Macromolecules). Ultramicroscopy
**1984**, 13, 165–183. [Google Scholar] [CrossRef] - Van Der Maaten, L.; Postma, E. Dimensionality Reduction: A Comparative Review. J. Mach. Learn. Res.
**2009**, 10, 66–71. [Google Scholar] - Van Heel, M.F.J. Classification of Particles in Noisy Electron. Micrographs Using Correspondence Analysis. Pattern Recognition in Practice; North-Holland Publishing: Amsterdam, The Netherlands, 1980. [Google Scholar]
- Frank, J.; van Heel, M. Correspondence Analysis of Aligned Images of Biological Particles. J. Mol. Biol.
**1982**, 161, 134–137. [Google Scholar] [CrossRef] - Frank, J. Differential Averaging of Single Molecule Images Using Multivariate Statistical Classification. Proc. Annu. Meet. Electron. Microsc. Soc. Am.
**1982**, 40, 706–709. [Google Scholar] [CrossRef] - van Heel, M.; Portugal, R.V.; Schatz, M. Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy. Open J. Stat.
**2016**, 06, 701–739. [Google Scholar] [CrossRef] [Green Version] - Harauz, G.; Welsh, E.A. Multivariate Statistical Analysis of Electron Micrographs of a Mammalian Transcription Initiation Complex. J. Electron. Microsc.
**1992**, 41, 264–266. [Google Scholar] [CrossRef] - Likas, A.; Vlassis, N.J.; Verbeek, J. The Global K-Means Clustering Algorithm. Pattern Recognit.
**2003**, 36, 451–461. [Google Scholar] [CrossRef] [Green Version] - Yang, Z.; Fang, J.; Chittuluru, J.; Asturias, F.J.; Penczek, P.A. Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images. Structure
**2012**, 20, 237–247. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Sorzano, C.O.S.; Bilbao-Castro, J.R.; Shkolnisky, Y.; Alcorlo, M.; Melero, R.; Caffarena-Fernández, G.; Li, M.; Xu, G.; Marabini, R.; Carazo, J.M. A Clustering Approach to Multireference Alignment of Single-Particle Projections in Electron Microscopy. J. Struct. Biol.
**2010**, 171, 197–206. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Wu, J.; Ma, Y.-B.; Congdon, C.; Brett, B.; Chen, S.; Xu, Y.; Ouyang, Q.; Mao, Y. Massively Parallel Unsupervised Single-Particle Cryo-EM Data Clustering via Statistical Manifold Learning. PLoS ONE
**2017**, 12, e0182130. [Google Scholar] [CrossRef] [Green Version] - Chen, T.-L.; Hsieh, D.-N.; Hung, H.; Tu, I.-P.; Wu, P.-S.; Wu, Y.-M.; Chang, W.-H.; Huang, S.-Y. γ-SUP: A Clustering Algorithm for Cryo-Electron Microscopy Images of Asymmetric Particles. Ann. Appl. Stat.
**2014**, 8, 259–285. [Google Scholar] [CrossRef] - Rao, R.; Moscovich, A.; Singer, A. Wasserstein K-Means for Clustering Tomographic Projections. arXiv
**2020**, arXiv:2010.09989. [Google Scholar] [CrossRef] - Ludtke, S.J.; Baldwin, P.R.; Chiu, W. EMAN: Semiautomated Software for High-Resolution Single-Particle Reconstructions. J. Struct. Biol.
**1999**, 128, 82–97. [Google Scholar] [CrossRef] [Green Version] - Frank, J.; Radermacher, M.; Penczek, P.; Zhu, J.; Li, Y.; Ladjadj, M.; Leith, A. SPIDER and WEB: Processing and Visualization of Images in 3D Electron Microscopy and Related Fields. J. Struct. Biol.
**1996**, 116, 190–199. [Google Scholar] [CrossRef] [PubMed] - Rosenthal, P.B.; Henderson, R. Optimal Determination of Particle Orientation, Absolute Hand, and Contrast Loss in Single-Particle Electron Cryomicroscopy. J. Mol. Biol.
**2003**, 333, 721–745. [Google Scholar] [CrossRef] - Pannu, N.S.; Read, R.J. Improved Structure Refinement through Maximum Likelihood. Acta Crystallogr. A
**1996**, 52, 659–668. [Google Scholar] [CrossRef] - Provencher, S.W.; Vogel, R.H. Three-Dimensional Reconstruction from Electron Micrographs of Disordered Specimens. I. Method. Ultramicroscopy
**1988**, 25, 209–221. [Google Scholar] [CrossRef] - Scheres, S.H.W.; Gao, H.; Valle, M.; Herman, G.T.; Eggermont, P.P.B.; Frank, J.; Carazo, J.-M. Disentangling Conformational States of Macromolecules in 3D-EM through Likelihood Optimization. Nat. Methods
**2007**, 4, 27–29. [Google Scholar] [CrossRef] [PubMed] - Scheres, S.H.W. RELION: Implementation of a Bayesian Approach to Cryo-EM Structure Determination. J. Struct. Biol.
**2012**, 180, 519–530. [Google Scholar] [CrossRef] [Green Version] - Rawson, S.; Iadanza, M.G.; Ranson, N.A.; Muench, S.P. Methods to Account for Movement and Flexibility in Cryo-EM Data Processing. Methods
**2016**, 100, 35–41. [Google Scholar] [CrossRef] - Bai, X.-C.; Rajendra, E.; Yang, G.; Shi, Y.; Scheres, S.H.W. Sampling the Conformational Space of the Catalytic Subunit of Human γ-Secretase. eLife
**2015**, 4, e11182. [Google Scholar] [CrossRef] - Cossio, P.; Hummer, G. Likelihood-Based Structural Analysis of Electron Microscopy Images. Curr. Opin. Struct. Biol.
**2018**, 49, 162–168. [Google Scholar] [CrossRef] - Katsevich, G.; Katsevich, A.; Singer, A. Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem. arXiv
**2013**, arXiv:1309.1737. [Google Scholar] [CrossRef] - Lederman, R. Hyper-Molecules: High Dimensional Maps of Molecular Conformations. Acta Crystallogr. A Found. Adv.
**2020**, 76, a61. [Google Scholar] [CrossRef] - Lederman, R.R.; Andén, J.; Singer, A. Hyper-Molecules: On the Representation and Recovery of Dynamical Structures for Applications in Flexible Macro-Molecules in Cryo-EM. Inverse Probl.
**2020**, 36, 044005. [Google Scholar] [CrossRef] - Tagare, H.D.; Kucukelbir, A.; Sigworth, F.J.; Wang, H.; Rao, M. Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images. J. Struct. Biol.
**2015**, 191, 245–262. [Google Scholar] [CrossRef] [Green Version] - Neal, R.M.; Hinton, G.E. A View of the Em Algorithm That Justifies Incremental, Sparse, and Other Variants. In Learning in Graphical Models; Springer: Dordrecht, The Netherlands, 1998; pp. 355–368. ISBN 9789401061049. [Google Scholar]
- Tipping, M.E.; Bishop, C.M. Probabilistic Principal Component Analysis. J. R. Stat. Soc. Ser. B Stat. Methodol.
**1999**, 61, 611–622. [Google Scholar] [CrossRef] - Roweis, S. EM Algorithms for PCA and SPCA. In Advances in Neural Information Processing Systems; Jordan, M., Kearns, M., Solla, S., Eds.; MIT Press: Cambridge, MA, USA, 1997; Volume 10. [Google Scholar]
- Punjani, A.; Fleet, D.J. 3D Variability Analysis: Resolving Continuous Flexibility and Discrete Heterogeneity from Single Particle Cryo-EM. J. Struct. Biol.
**2021**, 213, 107702. [Google Scholar] [CrossRef] - Zhong, E.D.; Lerer, A.; Davis, J.H.; Berger, B. CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures from Real Cryo-EM Images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), IEEE Montreal, Montreal, QC, Canada, 10–17 October 2021; pp. 4066–4075. [Google Scholar]
- Dashti, A.; Schwander, P.; Langlois, R.; Fung, R.; Li, W.; Hosseinizadeh, A.; Liao, H.Y.; Pallesen, J.; Sharma, G.; Stupina, V.A.; et al. Trajectories of the Ribosome as a Brownian Nanomachine. Proc. Natl. Acad. Sci. USA
**2014**, 111, 17492–17497. [Google Scholar] [CrossRef] [Green Version] - Dashti, A.; Mashayekhi, G.; Shekhar, M.; Ben Hail, D.; Salah, S.; Schwander, P.; des Georges, A.; Singharoy, A.; Frank, J.; Ourmazd, A. Retrieving Functional Pathways of Biomolecules from Single-Particle Snapshots. Nat. Commun.
**2020**, 11, 4734. [Google Scholar] [CrossRef] [PubMed] - Frank, J.; Ourmazd, A. Continuous Changes in Structure Mapped by Manifold Embedding of Single-Particle Data in Cryo-EM. Methods
**2016**, 100, 61–67. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Wu, Z.; Zhang, S.; Wang, W.L.; Ma, Y.; Dong, Y.; Mao, Y. Deep Manifold Learning Reveals Hidden Dynamics of Proteasome Autoregulation. arXiv
**2021**, arXiv:2012.12854. [Google Scholar] [CrossRef] - Giraldo-Barreto, J.; Ortiz, S.; Thiede, E.H.; Palacio-Rodriguez, K.; Carpenter, B.; Barnett, A.H.; Cossio, P. A Bayesian Approach to Extracting Free-Energy Profiles from Cryo-Electron Microscopy Experiments. Sci. Rep.
**2021**, 11, 13657. [Google Scholar] [CrossRef] [PubMed] - Karplus, M.; McCammon, J.A. Molecular Dynamics Simulations of Biomolecules. Nat. Struct. Biol.
**2002**, 9, 646–652. [Google Scholar] [CrossRef] [PubMed] - Vilas, J.L.; Gómez-Blanco, J.; Conesa, P.; Melero, R.; Miguel de la Rosa-Trevín, J.; Otón, J.; Cuenca, J.; Marabini, R.; Carazo, J.M.; Vargas, J.; et al. MonoRes: Automatic and Accurate Estimation of Local Resolution for Electron Microscopy Maps. Structure
**2018**, 26, 337–344. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kucukelbir, A.; Sigworth, F.J.; Tagare, H.D. Quantifying the Local Resolution of Cryo-EM Density Maps. Nat. Methods
**2014**, 11, 63–65. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Matsumoto, S.; Ishida, S.; Araki, M.; Kato, T.; Terayama, K.; Okuno, Y. Extraction of Protein Dynamics Information from Cryo-EM Maps Using Deep Learning. Nat. Mach. Intell.
**2021**, 3, 153–160. [Google Scholar] [CrossRef] - Wriggers, W.; Birmanns, S. Using Situs for Flexible and Rigid-Body Fitting of Multiresolution Single-Molecule Data. J. Struct. Biol.
**2001**, 133, 193–202. [Google Scholar] [CrossRef] [PubMed] [Green Version] - McGreevy, R.; Teo, I.; Singharoy, A.; Schulten, K. Advances in the Molecular Dynamics Flexible Fitting Method for Cryo-EM Modeling. Methods
**2016**, 100, 50–60. [Google Scholar] [CrossRef] [Green Version] - Miyashita, O.; Kobayashi, C.; Mori, T.; Sugita, Y.; Tama, F. Flexible Fitting to Cryo-EM Density Map Using Ensemble Molecular Dynamics Simulations. J. Comput. Chem.
**2017**, 38, 1447–1461. [Google Scholar] [CrossRef] [PubMed] - Kulik, M.; Mori, T.; Sugita, Y. Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution. Front. Mol. Biosci.
**2021**, 8, 631854. [Google Scholar] [CrossRef] [PubMed] - Trabuco, L.G.; Villa, E.; Schreiner, E.; Harrison, C.B.; Schulten, K. Molecular Dynamics Flexible Fitting: A Practical Guide to Combine Cryo-Electron Microscopy and X-ray Crystallography. Methods
**2009**, 49, 174–180. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Igaev, M.; Kutzner, C.; Bock, L.V.; Vaiana, A.C.; Grubmüller, H. Automated Cryo-EM Structure Refinement Using Correlation-Driven Molecular Dynamics. eLife
**2019**, 8, e43542. [Google Scholar] [CrossRef] - Orzechowski, M.; Tama, F. Flexible Fitting of High-Resolution X-ray Structures into Cryoelectron Microscopy Maps Using Biased Molecular Dynamics Simulations. Biophys. J.
**2008**, 95, 5692–5705. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mulder, A.M.; Yoshioka, C.; Beck, A.H.; Bunner, A.E.; Milligan, R.A.; Potter, C.S.; Carragher, B.; Williamson, J.R. Visualizing Ribosome Biogenesis: Parallel Assembly Pathways for the 30S Subunit. Science
**2010**, 330, 673–677. [Google Scholar] [CrossRef] - Fischer, N.; Konevega, A.L.; Wintermeyer, W.; Rodnina, M.V.; Stark, H. Ribosome Dynamics and TRNA Movement by Time-Resolved Electron Cryomicroscopy. Nature
**2010**, 466, 329–333. [Google Scholar] [CrossRef] [PubMed] - Frank, J. Time-Resolved Cryo-Electron Microscopy: Recent Progress. J. Struct. Biol.
**2017**, 200, 303–306. [Google Scholar] [CrossRef] [PubMed] - Mäeots, M.-E.; Lee, B.; Nans, A.; Jeong, S.-G.; Esfahani, M.M.N.; Ding, S.; Smith, D.J.; Lee, C.-S.; Lee, S.S.; Peter, M.; et al. Modular Microfluidics Enables Kinetic Insight from Time-Resolved Cryo-EM. Nat. Commun.
**2020**, 11, 3465. [Google Scholar] [CrossRef] [PubMed] - Dandey, V.P.; Budell, W.C.; Wei, H.; Bobe, D.; Maruthi, K.; Kopylov, M.; Eng, E.T.; Kahn, P.A.; Hinshaw, J.E.; Kundu, N.; et al. Time-Resolved Cryo-EM Using Spotiton. Nat. Methods
**2020**, 17, 897–900. [Google Scholar] [CrossRef] [PubMed] - Kontziampasis, D.; Klebl, D.P.; Iadanza, M.G.; Scarff, C.A.; Kopf, F.; Sobott, F.; Monteiro, D.C.F.; Trebbin, M.; Muench, S.P.; White, H.D. A Cryo-EM Grid Preparation Device for Time-Resolved Structural Studies. IUCrJ
**2019**, 6, 1024–1031. [Google Scholar] [CrossRef] [Green Version] - Klebl, D.P.; White, H.D.; Sobott, F.; Muench, S.P. On-Grid and in-Flow Mixing for Time-Resolved Cryo-EM. Acta Crystallogr. D Struct. Biol.
**2021**, 77, 1233–1240. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**Schematics of the single-particle cryo-EM processing workflow. (

**A**) General processing workflow for single-particle reconstruction workflow. (

**B**) One-dimensional plot of the contrast transfer function (CTF) versus spatial frequency. Curves are calculated using an accelerated voltage of 300 keV and a spherical aberration coefficient (Cs) of 2.7 mm. Blue and green curves are for the defocus at −800 and −87.5 nm (Scherzer defocus), respectively. (

**C**) Schematics of 2D projections from an imaged object and a 3D back-projection from the 2D projections. Light blue represents thin ice that embeds the particles in different orientations.

**Figure 2.**Two-dimensional (2D) class averages of cryo-EM particle images show the compositional heterogeneity. p97 R155H mutant complex [55]. Box side length is 374 Å. Yellow arrows indicate one single protein complex.

**Figure 3.**Redox states of chloroplast ATP synthase [57]. Blue, white, and salmon are different rotary states of the ATP synthase. Percentages labeled are the particle proportions within those in individual redox states.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

DeVore, K.; Chiu, P.-L.
Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity. *Biomolecules* **2022**, *12*, 628.
https://doi.org/10.3390/biom12050628

**AMA Style**

DeVore K, Chiu P-L.
Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity. *Biomolecules*. 2022; 12(5):628.
https://doi.org/10.3390/biom12050628

**Chicago/Turabian Style**

DeVore, Kira, and Po-Lin Chiu.
2022. "Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity" *Biomolecules* 12, no. 5: 628.
https://doi.org/10.3390/biom12050628