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Lugmayr W, Kotov V, Goessweiner-Mohr N, Wald J, DiMaio F, Marlovits TC. StarMap: a user-friendly workflow for Rosetta-driven molecular structure refinement. Nat Protoc 2023; 18:239-264. [PMID: 36323866 DOI: 10.1038/s41596-022-00757-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/08/2022] [Indexed: 01/13/2023]
Abstract
Cryogenic electron microscopy (cryo-EM) data represent density maps of macromolecular systems at atomic or near-atomic resolution. However, building and refining 3D atomic models by using data from cryo-EM maps is not straightforward and requires significant hands-on experience and manual intervention. We recently developed StarMap, an easy-to-use interface between the popular structural display program ChimeraX and Rosetta, a powerful molecular modeling engine. StarMap offers a general approach for refining structural models of biological macromolecules into cryo-EM density maps by combining Monte Carlo sampling with local density-guided optimization, Rosetta-based all-atom refinement and real-space B-factor calculations in a straightforward workflow. StarMap includes options for structural symmetry, local refinements and independent model validation. The overall quality of the refinement and the structure resolution is then assessed via analytical outputs, such as magnification calibration (pixel size calibration) and Fourier shell correlations. Z-scores reported by StarMap provide an easily interpretable indicator of the goodness of fit for each residue and can be plotted to evaluate structural models and improve local residue refinements, as well as to identify flexible regions and potentially functional sites in large macromolecular complexes. The protocol requires general computer skills, without the need for coding expertise, because most parts of the workflow can be operated by clicking tabs within the ChimeraX graphical user interface. Time requirements for the model refinement depend on the size and quality of the input data; however, this step can typically be completed within 1 d. The analytical parts of the workflow are completed within minutes.
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Affiliation(s)
- Wolfgang Lugmayr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Vadim Kotov
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Evotec SE, Hamburg, Germany
| | - Nikolaus Goessweiner-Mohr
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.,Johannes Kepler University, Institute of Biophysics, Linz, Austria
| | - Jiri Wald
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany.,CSSB Centre for Structural Systems Biology, Hamburg, Germany.,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany.,Research Institute of Molecular Pathology (IMP), Vienna, Austria.,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria
| | - Frank DiMaio
- University of Washington, Department of Biochemistry, Seattle, WA, USA
| | - Thomas C Marlovits
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Structural and Systems Biology, Hamburg, Germany. .,CSSB Centre for Structural Systems Biology, Hamburg, Germany. .,Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany. .,Research Institute of Molecular Pathology (IMP), Vienna, Austria. .,Institute for Molecular Biotechnology (IMBA), Austrian Academy of Sciences, Vienna, Austria.
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Warshamanage R, Yamashita K, Murshudov GN. EMDA: A Python package for Electron Microscopy Data Analysis. J Struct Biol 2021; 214:107826. [PMID: 34915128 PMCID: PMC8935390 DOI: 10.1016/j.jsb.2021.107826] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 12/01/2021] [Accepted: 12/08/2021] [Indexed: 12/01/2022]
Abstract
An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying map-model differences and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA’s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out first to bring both maps into the same coordinate frame and then to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of adjusting the map magnification in structural comparison studies.
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Affiliation(s)
- Rangana Warshamanage
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom.
| | - Keitaro Yamashita
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Garib N Murshudov
- Structural Studies, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom.
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Gaber A, Pavšič M. Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches. Int J Mol Sci 2021; 22:9081. [PMID: 34445785 PMCID: PMC8396596 DOI: 10.3390/ijms22169081] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 12/12/2022] Open
Abstract
Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.
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Caesar J, Reboul CF, Machello C, Kiesewetter S, Tang ML, Deme JC, Johnson S, Elmlund D, Lea SM, Elmlund H. SIMPLE 3.0. Stream single-particle cryo-EM analysis in real time. JOURNAL OF STRUCTURAL BIOLOGY-X 2020; 4:100040. [PMID: 33294840 PMCID: PMC7695977 DOI: 10.1016/j.yjsbx.2020.100040] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
We here introduce the third major release of the SIMPLE (Single-particle IMage Processing Linux Engine) open-source software package for analysis of cryogenic transmission electron microscopy (cryo-EM) movies of single-particles (Single-Particle Analysis, SPA). Development of SIMPLE 3.0 has been focused on real-time data processing using minimal CPU computing resources to allow easy and cost-efficient scaling of processing as data rates escalate. Our stream SPA tool implements the steps of anisotropic motion correction and CTF estimation, rapid template-based particle identification and 2D clustering with automatic class rejection. SIMPLE 3.0 additionally features an easy-to-use web-based graphical user interface (GUI) that can be run on any device (workstation, laptop, tablet or phone) and supports a remote multi-user environment over the network. The new project-based execution model automatically records the executed workflow and represents it as a flow diagram in the GUI. This facilitates meta-data handling and greatly simplifies usage. Using SIMPLE 3.0, it is possible to automatically obtain a clean SP data set amenable to high-resolution 3D reconstruction directly upon completion of the data acquisition, without the need for extensive image processing post collection. Only minimal standard CPU computing resources are required to keep up with a rate of ∼300 Gatan K3 direct electron detector movies per hour. SIMPLE 3.0 is available for download from simplecryoem.com.
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Affiliation(s)
- Joseph Caesar
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Cyril F Reboul
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Chiara Machello
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Simon Kiesewetter
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Molly L Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Justin C Deme
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Steven Johnson
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Dominika Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
| | - Susan M Lea
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK.,Central Oxford Structural Microscopy and Imaging Centre, University of Oxford, Oxford UK
| | - Hans Elmlund
- Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.,Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Victoria, Australia
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