Editorial Cite This: J. Chem. Inf. Model. 2019, 59, 3091−3093
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Frontiers in CryoEM Modeling
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search.7 A major challenge for these methods is to obtain refined atomistic models that reliably represent density maps. Moreover, these methods have been designed to refine structures from medium-resolution maps (∼8 Å), while today’s cryoEM detectors provide higher resolution maps. Concomitant with the benefits of higher resolution maps, the resultant distinct structural features can trap the fitted structure into non-native conformations. This problem particularly arises when dealing with heterogeneous structures in which certain regions reach a near-Ångström resolution, whereas other areas of the map remain above ∼6−8 Å resolution. To deal with the increased resolution of cryoEM maps while refining lowresolution regions, enhanced sampling techniques have been proposed for the refinement process.8,9 In this respect, accelerated MD and metadynamics show promise during MD fitting, as these methodologies help avoid local minima of the potential energy and efficiently explore the conformational space. The absence of packing constraints in single-particle cryoEM facilitates the observation of an ensemble of coexisting conformational states. Typically, thousands of 2D images of picked particles are processed through clustering to obtain the most representative 3D structures. Such processing can result in artifacts from under-represented conformations or view directions. Moreover, while the 2D and 3D classifications are generally utilized to remove outliers exhibiting conformational heterogeneity, it is difficult to assess how well atomic models represent the density from which they were generated. Along the same lines, atomic models might lack the representation of the physiological architecture of the sample.10 To tackle these challenges, theoretical approaches are being considered, including novel clustering schemes11 in parallel with Bayesian analysis, which help deal with large amounts of heterogeneous data.12 Furthermore, modern image processing algorithms use graphics processing units to address image classification and high-resolution refinement,13 while MD is being used to explore the conformational space at the 3D level, defining the most probable states to be extracted from 2D representations. Alternative approaches for cryoEM reconstruction have recently emerged that employ network structural similarity metrics and harness graph theory for map reconstruction.14 Beyond determining the high-resolution structures of increasingly realistic biological systems, a closely related challenge is the determination of the energetic landscapes that govern how such systems interconvert between their functional states.15 Emerging methods such as manifold embedding offer the tantalizing possibility of determining energetic landscapes directly from the cryoEM data itself.16,17 In addition, MD simulations offer the promise of exploring such functional pathways. However, simulating such biophysical events at the molecular level and at longer (biologically relevant) time scales challenges the current capabilities of MD. 18,19 While the use of enhanced
n recent years, remarkable advances in single-particle cryoelectron microscopy (cryoEM) have enabled the determination of an increasing number of biomolecular assemblies with unprecedented detail and resolution.1 The emergence of direct detection device (DDD) detectors has driven a revolution in the field, performing digital image alignment to eliminate specimen drift in cryoEM images. This has led to a remarkable increase in resolution that enables the ability to solve increasingly realistic structures with nearÅngström resolution. Thanks to this technical revolution, it is now possible to observe large macromolecular complexes in their “native” aqueous solution. For example, the structures of protein/nucleic acid assemblies, viruses, and organelles have been obtained that reflect their in vivo situation. In addition, relatively smaller membrane protein structures can be determined using cryoEM experiments. The explosion of deposited cryoEM maps at high resolution is now challenging researchers in computational chemistry and biology, calling for dynamic and mechanistic interpretations of experimental data. Emerging theoretical approaches aim at processing, complementing, and interpreting cryoEM data, overcoming issues in map refinement, data processing, and the atomistic simulation of large biomolecules. It is to this end that JCIM invites authors to contribute their cryoEM work in an upcoming Special Issue, which will be published in Spring 2020.
David W. Taylor, University of Texas at Austin
From a historical perspective, computational chemists and biologists have approached cryoEM by developing and applying methods enabling the refinement of the first lowresolution cryoEM maps. The first fitting methods based on rigid-body docking are currently being replaced by Monte Carlo,2 Normal Mode Analysis,3 and molecular dynamics (MD) fitting schemes using the EM map as a constraint. Among them, the pioneering package Situs4 uses the minimization of density discrepancy, whereas the MD Flexible Fitting5 method uses the gradient of electronic density as a penalty function for potential, thereby allowing flexible fitting. Hybrid approaches have been developed that include a rigid fitting stage followed by MD-driven refinement6 or a coarsegrained force field to enable flexibility throughout a docking © 2019 American Chemical Society
Published: June 13, 2019 3091
DOI: 10.1021/acs.jcim.9b00412 J. Chem. Inf. Model. 2019, 59, 3091−3093
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Editorial
sampling20,21 is a straightforward way to tackle the problem, all-atom simulations, including millions of explicit atoms, will require next-generation algorithms exploiting future exascale computing power, which will likely change the perspective of what can be done with computational biophysics.22 Accordingly, this special issue focuses on the most exciting applications of molecular simulations in the field of cryoEM, as well as emerging theoretical and computational approaches aimed at processing, complementing, and interpreting singleparticle cryoEM experiments. This issue will give an overview of the methodological advances in the field and the forthcoming challenges, with particular attention to validation and reproducibility issues. We focus on three main objectives for the special issue, which concerns current challenges in modeling data from single-particle cryoEM. First, articles will be solicited that describe theoretical approaches enabling the refinement of cryoEM maps. Subsequently, manuscripts will be sought that discuss the current efforts of microscopists and computational biophysicists to process data obtained via cryoEM. Finally, the application of theoretical tools to refine cryoEM maps and allatom simulations of biological systems obtained via cryoEM will round out the special issue. We look forward to receiving your submissions for this Special Issue by November 1, 2019.
(2) Di Maio, F.; Song, Y.; Li, X.; Brunner, M. J.; Xu, C.; Conticello, V.; Egelman, E.; Marlovits, T.; Cheng, Y.; Baker, D. Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat. Methods 2015, 12, 361−365. (3) Tama, F.; Miyashita, O.; Brooks, C. L. Normal mode based flexible fitting of high-resolution structure into low-resolution experimental data from cryo-EM. J. Struct. Biol. 2004, 147, 315−326. (4) Kovacs, J. A.; Galkin, V. E.; Wriggers, W. Accurate flexible refinement of atomic models against medium-resolution cryo-EM maps using damped dynamics. BMC Struct. Biol. 2018, 18, 12. (5) Trabuco, L. G.; Villa, E.; Mitra, K.; Frank, J.; Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 2008, 16, 673−683. (6) Topf, M.; Lasker, K.; Webb, B.; Wolfson, H.; Chiu, W.; Sali, A. Protein structure fitting and refinement guided by cryo-EM density. Structure 2008, 16, 295−307. (7) de Vries, S. J.; Zacharias, M. ATTRACT-EM: A New Method for the Computational Assembly of Large Molecular Machines Using Cryo-EM Maps. PLoS One 2012, 7, e49733. (8) Goh, B. C.; Hadden, J. A.; Bernardi, R. C.; Singharoy, A.; McGreevy, R.; Rudack, T.; Cassidy, C. K.; Schulten, K. Computational Methodologies for Real-Space Structural Refinement of Large Macromolecular Complexes. Annu. Rev. Biophys 2016, 45, 253−278. (9) He, Y.; Yan, C.; Inouye, C.; Fang, J.; Tjian, R.; Ivanov, I.; Nogales, E. Near-atomic resolution visualization of human transcription promoter opening. Nature 2016, 533, 359−365. (10) Herzik, M. A., Jr.; Fraser, J. S.; Lander, G. C. A Multi-model Approach to Assessing Local and Global Cryo-EM Map Quality. Structure 2019, 27, 344−358. (11) Rodriguez, A.; Laio, A. Clustering by fast search and find of density peaks. Science 2014, 344, 1492−1496. (12) Cossio, P.; Rohr, D.; Baruffa, F.; Rampp, M.; Lindenstruth, V.; Hummer, G. BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images. Comput. Phys. Commun. 2017, 210, 163−171. (13) Kimanius, D.; Forsberg, B. O.; Scheres, S. J. W; Lindahl, S. Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife 2016, 5, e18722. (14) Yin, S.; Zhang, B.; Yang, Y.; Huang, Y.; Shen, H. B. Clustering Enhancement of Noisy Cryo-Electron Microscopy Single-Particle Images with a Network Structural Similarity Metric. J. Chem. Inf. Model. 2019, 59, 1658. (15) Frank, J. New Opportunities Created by Single-Particle CryoEM: The Mapping of Conformational Space. Biochemistry 2018, 57, 888. (16) Frank, J.; Ourmazd, A. Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM. Methods 2016, 100, 61−67. (17) Dashti, A.; Schwander, P.; Langlois, P.; Fung, R.; Li, W.; Hosseinizadeh, A.; Liao, H. Y.; Pallesen, L.; Sharma, G.; Stupina, V. A.; Simon, A. E.; Dinman, J. D.; Frank, J.; Ourmazd, A. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 17492−17497. (18) Palermo, G.; Miao, Y.; Walker, R. C.; Jinek, M.; McCammon, J. A. CRISPR-Cas9 conformational activation as elucidated from enhanced molecular simulations. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 7260−7265. (19) Casalino, L.; Palermo, G.; Spinello, A.; Rothlisberger, U.; Magistrato, A. All-Atom Simulations Disentangle the Functional Dynamics Underlying Gene Maturation in the Intron Lariat Spliceosome. Proc. Natl. Acad. Sci. U. S. A. 2018, 115, 6584−6589. (20) 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. (21) Jin, Q.; Sorzano, C. O. S.; de la Rosa-Trevín, J. M.; BilbaoCastro, J. R.; Núñez-Ramírez, R.; Llorca, O.; Tama, F.; Jonić, S. Iterative Elastic 3D-to-2D Alignment Method Using Normal Modes for Studying Structural Dynamics of Large Macromolecular Complexes. Structure 2014, 22, 496−506.
Giulia Palermo† Yuji Sugita‡,§,∥ Willy Wriggers⊥ Rommie E. Amaro# †
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Department of Bioengineering, University of California Riverside, Riverside, California 92521, United States ‡ Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan § Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan ∥ Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 1-6-5 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan ⊥ Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, Virginia 23529, United States # Department of Chemistry and Biochemistry, University of California San Diego, San Diego, California 92093-0340, United States
AUTHOR INFORMATION
ORCID
Giulia Palermo: 0000-0003-1404-8737 Yuji Sugita: 0000-0001-9738-9216 Willy Wriggers: 0000-0001-5326-3152 Rommie E. Amaro: 0000-0002-9275-9553 Notes
Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.
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REFERENCES
(1) Nogales, E. The development of cryo-EM into a mainstream structural biology technique. Nat. Methods 2016, 13, 24−27. 3092
DOI: 10.1021/acs.jcim.9b00412 J. Chem. Inf. Model. 2019, 59, 3091−3093
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(22) Lee, C. T.; Amaro, R. E. Exascale Computing: A New Dawn for Computational Biology. Comput. Sci. Eng. 2018, 20, 18−25.
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NOTE ADDED AFTER ASAP PUBLICATION The graphic featured in this article was added after initial publication. The paper was republished ASAP on July 10, 2019.
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DOI: 10.1021/acs.jcim.9b00412 J. Chem. Inf. Model. 2019, 59, 3091−3093
