5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A flexible framework for multi-particle refinement in cryo-electron tomography

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are increasingly used for macromolecular structure determination in situ. Here, we introduce a set of computational tools and resources designed to enable flexible approaches to STA through increased automation and simplified metadata handling. We create a bidirectional interface between the Dynamo software package and the Warp-Relion-M pipeline, providing a framework for ab initio and geometrical approaches to multiparticle refinement in M. We illustrate the power of working within this framework by applying it to EMPIAR-10164, a publicly available dataset containing immature HIV-1 virus-like particles (VLPs), and a challenging in situ dataset containing chemosensory arrays in bacterial minicells. Additionally, we provide a comprehensive, step-by-step guide to obtaining a 3.4-Å reconstruction from EMPIAR-10164. The guide is hosted on https://teamtomo.org/, a collaborative online platform we establish for sharing knowledge about cryo-ET.

          Abstract

          Employing optimal computational methodology in cryo-electron tomography is not always easy; this article provides a set of tools and a complete guide to obtaining high-resolution structures from cryo-ET data.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              RELION: Implementation of a Bayesian approach to cryo-EM structure determination

              RELION, for REgularized LIkelihood OptimizatioN, is an open-source computer program for the refinement of macromolecular structures by single-particle analysis of electron cryo-microscopy (cryo-EM) data. Whereas alternative approaches often rely on user expertise for the tuning of parameters, RELION uses a Bayesian approach to infer parameters of a statistical model from the data. This paper describes developments that reduce the computational costs of the underlying maximum a posteriori (MAP) algorithm, as well as statistical considerations that yield new insights into the accuracy with which the relative orientations of individual particles may be determined. A so-called gold-standard Fourier shell correlation (FSC) procedure to prevent overfitting is also described. The resulting implementation yields high-quality reconstructions and reliable resolution estimates with minimal user intervention and at acceptable computational costs.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: ValidationRole: Writing – review & editing
                Role: InvestigationRole: Validation
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                26 August 2021
                August 2021
                26 August 2021
                : 19
                : 8
                : e3001319
                Affiliations
                [1 ] Institut de Biologie Structurale, Université Grenoble Alpes, CEA, CNRS, IBS, Grenoble, France
                [2 ] MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
                California Institute of Technology, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-9341-2295
                https://orcid.org/0000-0003-4875-9422
                https://orcid.org/0000-0002-1908-3921
                Article
                PBIOLOGY-D-21-00462
                10.1371/journal.pbio.3001319
                8389456
                34437530
                38827e3b-d1cd-429b-9b80-22505e95eb84
                © 2021 Burt et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 February 2021
                : 9 June 2021
                Page count
                Figures: 6, Tables: 0, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 647784
                Award Recipient :
                Funded by: Fondation pour la Recherche Medicale
                Award ID: FDT202001011069
                Award Recipient :
                Funded by: Grenoble Alliance for Integrated Structural and Cell Biology
                Award ID: GRAL, ANR-10-LABX-49-01, ANR-17-EURE-0003
                Award Recipient :
                This work has received funding from a European Union’s Horizon 2020 research and innovation programme under grant agreement No 647784 to IG. AB is supported by a University Grenoble Alpes PhD fellowship and by a Fondation pour la Recherche Medicale (FRM) fellowship FDT202001011069. LG is supported by a PhD fellowship from Grenoble Alliance for Integrated Structural and Cell Biology (GRAL, ANR-10-LABX-49-01) funded within the CBH Graduate School of the University Grenoble Alpes (ANR-17-EURE-0003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Methods and Resources
                Computer and Information Sciences
                Data Management
                Metadata
                Computer and Information Sciences
                Software Engineering
                Software Tools
                Engineering and Technology
                Software Engineering
                Software Tools
                Computer and Information Sciences
                Software Engineering
                Preprocessing
                Engineering and Technology
                Software Engineering
                Preprocessing
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Tomography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Tomography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Tomography
                Computer and Information Sciences
                Data Management
                Data Processing
                Computer and Information Sciences
                Software Engineering
                Computer Software
                Engineering and Technology
                Software Engineering
                Computer Software
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Physical Sciences
                Mathematics
                Optimization
                Custom metadata
                Data on immature HIV-1 dMACANC virus-like particles used for benchmarking of the pipeline was taken from EMPIAR-10164. Data on E. coli minicells will be available upon publication of the final reconstruction of the chemosensory array. Information underlying data displayed at Figs 5 and 6 is available for download at doi: 10.5281/zenodo.4783129 and doi: 10.5281/zenodo.4783151 respectively. Source code for all tools and resources described here is available at: • autoalign_dynamo - https://github.com/alisterburt/autoalign_dynamo • mdocspoofer - https://github.com/alisterburt/mdocspoofer • starfile - https://github.com/alisterburt/starfile • dynamotable - https://github.com/alisterburt/dynamotable • eulerangles - https://github.com/alisterburt/eulerangles All Python packages are made available on the Python package index (PyPI). Benchmarking code for eulerangles can be found at https://gist.github.com/alisterburt/4a32e9c122498ac0ab482ee5ba44ba10.

                Life sciences
                Life sciences

                Comments

                Comment on this article