1
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Dahmani ZL, Scott AL, Vénien-Bryan C, Perahia D, Costa MGS. MDFF_NM: Improved Molecular Dynamics Flexible Fitting into Cryo-EM Density Maps with a Multireplica Normal Mode-Based Search. J Chem Inf Model 2024. [PMID: 38907694 DOI: 10.1021/acs.jcim.3c02007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Molecular Dynamics Flexible Fitting (MDFF) is a widely used tool to refine high-resolution structures into cryo-EM density maps. Despite many successful applications, MDFF is still limited by its high computational cost, overfitting, accuracy, and performance issues due to entrapment within wrong local minima. Modern ensemble-based MDFF tools have generated promising results in the past decade. In line with these studies, we present MDFF_NM, a stochastic hybrid flexible fitting algorithm combining Normal Mode Analysis (NMA) and simulation-based flexible fitting. Initial tests reveal that, besides accelerating the fitting process, MDFF_NM increases the diversity of fitting routes leading to the target, uncovering ensembles of conformations in closer agreement with experimental data. The potential integration of MDFF_NM with other existing methods and integrative modeling pipelines is also discussed.
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Affiliation(s)
- Zakaria L Dahmani
- School of Medicine, Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch I Bldg, 3420 Forbes Avenue, Pittsburgh, Pennsylvania 15260, United States
- UMR 7590, CNRS, Museum National d'Histoire Naturelle, Institut de Minéralogie, Physique des Matériaux et Cosmochimie, IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
| | - Ana Ligia Scott
- CMCC, Computational Biophysics and Biology, Universidade Federal do ABC, Avenida dos Estados 5001, São Paulo, Santo André 09210-580, Brazil
- Université de Strasbourg─IGBMC─Departament de Biologie structurale integrative, 1 rue Laurent Fries BP, Illkirch 10142 67404, CEDEX, France
| | - Catherine Vénien-Bryan
- UMR 7590, CNRS, Museum National d'Histoire Naturelle, Institut de Minéralogie, Physique des Matériaux et Cosmochimie, IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
| | - David Perahia
- Laboratoire de Biologie et Pharmacologie Appliquée, UMR 8113, École Normale Supérieure Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Mauricio G S Costa
- UMR 7590, CNRS, Museum National d'Histoire Naturelle, Institut de Minéralogie, Physique des Matériaux et Cosmochimie, IMPMC, Sorbonne Université, 4 place Jussieu, Paris 75005, France
- Laboratoire de Biologie et Pharmacologie Appliquée, UMR 8113, École Normale Supérieure Paris-Saclay, Gif-sur-Yvette 91190, France
- Programa de Computação Científica, Vice-Presidência de Educação, Informação e Comunicação, Fundação Oswaldo Cruz, Av.Brasil 4365, Residência Oficial, Manguinhos, Rio de Janeiro 21040-900, Brazil
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2
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Bock LV, Igaev M, Grubmüller H. Single-particle Cryo-EM and molecular dynamics simulations: A perfect match. Curr Opin Struct Biol 2024; 86:102825. [PMID: 38723560 DOI: 10.1016/j.sbi.2024.102825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/19/2024]
Abstract
Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.
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Affiliation(s)
- Lars V Bock
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany. https://twitter.com/Pogoscience
| | - Maxim Igaev
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany. https://twitter.com/maxotubule
| | - Helmut Grubmüller
- Theoretical and Computational Biophysics Department, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, Göttingen, 37077, Germany.
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3
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Ruedas R, Vuillemot R, Tubiana T, Winter JM, Pieri L, Arteni AA, Samson C, Jonic S, Mathieu M, Bressanelli S. Structure and conformational variability of the HER2-trastuzumab-pertuzumab complex. J Struct Biol 2024; 216:108095. [PMID: 38723875 DOI: 10.1016/j.jsb.2024.108095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 03/11/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
Single particle analysis from cryogenic transmission electron microscopy (cryo-EM) is particularly attractive for complexes for which structure prediction remains intractable, such as antibody-antigen complexes. Here we obtain the detailed structure of a particularly difficult complex between human epidermal growth factor receptor 2 (HER2) and the antigen-binding fragments from two distinct therapeutic antibodies binding to distant parts of the flexible HER2, pertuzumab and trastuzumab (HTP). We highlight the strengths and limitations of current data processing software in dealing with various kinds of heterogeneities, particularly continuous conformational heterogeneity, and in describing the motions that can be extracted from our dataset. Our HTP structure provides a more detailed view than the one previously available for this ternary complex. This allowed us to pinpoint a previously overlooked loop in domain IV that may be involved both in binding of trastuzumab and in HER2 dimerization. This finding may contribute to explain the synergistic anticancer effect of the two antibodies. We further propose that the flexibility of the HTP complex, beyond the difficulties it causes for cryo-EM analysis, actually reflects regulation of HER2 signaling and its inhibition by therapeutic antibodies. Notably we obtain our best data with ultra-thin continuous carbon grids, showing that with current cameras their use to alleviate particle misdistribution is compatible with a protein complex of only 162 kDa. Perhaps most importantly, we provide here a dataset for such a smallish protein complex for further development of software accounting for continuous conformational heterogeneity in cryo-EM images.
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Affiliation(s)
- Rémi Ruedas
- Université Paris-Saclay, CEA, CNRS - Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France; Sanofi, Integrated Drug Discovery, 13, quai Jules Guesde 94403, Vitry-sur-Seine, France
| | - Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, 75005, Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS - Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Jean-Marie Winter
- NanoImaging Core Facility, Centre de Ressources et Recherches Technologiques (C2RT), Institut Pasteur, 75015, Paris, France
| | - Laura Pieri
- Université Paris-Saclay, CEA, CNRS - Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Ana-Andreea Arteni
- Université Paris-Saclay, CEA, CNRS - Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Camille Samson
- Sanofi, Integrated Drug Discovery, 13, quai Jules Guesde 94403, Vitry-sur-Seine, France
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, 75005, Paris, France
| | - Magali Mathieu
- Sanofi, Integrated Drug Discovery, 13, quai Jules Guesde 94403, Vitry-sur-Seine, France
| | - Stéphane Bressanelli
- Université Paris-Saclay, CEA, CNRS - Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
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4
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Lam JH, Nakano A, Katritch V. Scalable computation of anisotropic vibrations for large macromolecular assemblies. Nat Commun 2024; 15:3479. [PMID: 38658556 PMCID: PMC11043083 DOI: 10.1038/s41467-024-47685-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.
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Affiliation(s)
- Jordy Homing Lam
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA
| | - Aiichiro Nakano
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA, USA.
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Bridge Institute and Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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5
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Riggi M, Torrez RM, Iwasa JH. 3D animation as a tool for integrative modeling of dynamic molecular mechanisms. Structure 2024; 32:122-130. [PMID: 38183978 PMCID: PMC10872329 DOI: 10.1016/j.str.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/01/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024]
Abstract
As the scientific community accumulates diverse data describing how molecular mechanisms occur, creating and sharing visual models that integrate the richness of this information has become increasingly important to help us explore, refine, and communicate our hypotheses. Three-dimensional (3D) animation is a powerful tool to capture dynamic hypotheses that are otherwise difficult or impossible to visualize using traditional 2D illustration techniques. This perspective discusses the current and future roles that 3D animation can play in the research sphere.
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Affiliation(s)
- Margot Riggi
- Department of Biochemistry, University of Utah, Salt Lake City, UT, USA
| | - Rachel M Torrez
- Department of Biochemistry, University of Utah, Salt Lake City, UT, USA
| | - Janet H Iwasa
- Department of Biochemistry, University of Utah, Salt Lake City, UT, USA.
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6
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Vuillemot R, Harastani M, Hamitouche I, Jonic S. MDSPACE and MDTOMO Software for Extracting Continuous Conformational Landscapes from Datasets of Single Particle Images and Subtomograms Based on Molecular Dynamics Simulations: Latest Developments in ContinuousFlex Software Package. Int J Mol Sci 2023; 25:20. [PMID: 38203192 PMCID: PMC10779004 DOI: 10.3390/ijms25010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/16/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
Cryo electron microscopy (cryo-EM) instrumentation allows obtaining 3D reconstruction of the structure of biomolecular complexes in vitro (purified complexes studied by single particle analysis) and in situ (complexes studied in cells by cryo electron tomography). Standard cryo-EM approaches allow high-resolution reconstruction of only a few conformational states of a molecular complex, as they rely on data classification into a given number of classes to increase the resolution of the reconstruction from the most populated classes while discarding all other classes. Such discrete classification approaches result in a partial picture of the full conformational variability of the complex, due to continuous conformational transitions with many, uncountable intermediate states. In this article, we present the software with a user-friendly graphical interface for running two recently introduced methods, namely, MDSPACE and MDTOMO, to obtain continuous conformational landscapes of biomolecules by analyzing in vitro and in situ cryo-EM data (single particle images and subtomograms) based on molecular dynamics simulations of an available atomic model of one of the conformations. The MDSPACE and MDTOMO software is part of the open-source ContinuousFlex software package (starting from version 3.4.2 of ContinuousFlex), which can be run as a plugin of the Scipion software package (version 3.1 and later), broadly used in the cryo-EM field.
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Affiliation(s)
| | | | | | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, 75005 Paris, France
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7
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de la Cruz MJ, Eng ET. Scaling up cryo-EM for biology and chemistry: The journey from niche technology to mainstream method. Structure 2023; 31:1487-1498. [PMID: 37820731 PMCID: PMC10841453 DOI: 10.1016/j.str.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/31/2023] [Accepted: 09/14/2023] [Indexed: 10/13/2023]
Abstract
Cryoelectron microscopy (cryo-EM) methods have made meaningful contributions in a wide variety of scientific research fields. In structural biology, cryo-EM routinely elucidates molecular structure from isolated biological macromolecular complexes or in a cellular context by harnessing the high-resolution power of the electron in order to image samples in a frozen, hydrated environment. For structural chemistry, the cryo-EM method popularly known as microcrystal electron diffraction (MicroED) has facilitated atomic structure generation of peptides and small molecules from their three-dimensional crystal forms. As cryo-EM has grown from an emerging technology, it has undergone modernization to enable multimodal transmission electron microscopy (TEM) techniques becoming more routine, reproducible, and accessible to accelerate research across multiple disciplines. We review recent advances in modern cryo-EM and assess how they are contributing to the future of the field with an eye to the past.
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Affiliation(s)
- M Jason de la Cruz
- Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - Edward T Eng
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA.
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8
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Gilles MA, Singer A. A Bayesian Framework for Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.28.564422. [PMID: 37961393 PMCID: PMC10634927 DOI: 10.1101/2023.10.28.564422] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Proteins and the complexes they form are central to nearly all cellular processes. Their flexibility, expressed through a continuum of states, provides a window into their biological functions. Cryogenic-electron microscopy (cryo-EM) is an ideal tool to study these dynamic states as it captures specimens in non-crystalline conditions and enables high-resolution reconstructions. However, analyzing the heterogeneous distribution of conformations from cryo-EM data is challenging. Current methods face issues such as a lack of explainability, overfitting caused by lack of regularization, and a large number of parameters to tune; problems exacerbated by the lack of proper metrics to evaluate or compare heterogeneous reconstructions. To address these challenges, we present RECOVAR, a white-box method based on principal component analysis (PCA) computed via regularized covariance estimation that can resolve intricate heterogeneity with similar expressive power to neural networks with significantly lower computational demands. We extend the ubiquitous Bayesian framework used in homogeneous reconstruction to automatically regularize principal components, overcoming overfitting concerns and removing the need for most parameters. We further exploit the conservation of density and distances endowed by the embedding in PCA space, opening the door to reliable free energy computation. We leverage the predictable uncertainty of image labels to generate high-resolution reconstructions and identify high-density trajectories in latent space. We make the code freely available at https://github.com/ma-gilles/recovar.
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9
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Miyashita O, Tama F. Advancing cryo-electron microscopy data analysis through accelerated simulation-based flexible fitting approaches. Curr Opin Struct Biol 2023; 82:102653. [PMID: 37451233 DOI: 10.1016/j.sbi.2023.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023]
Abstract
Flexible fitting based on molecular dynamics simulation is a technique for structure modeling from cryo-EM data. It has been utilized for nearly two decades, and while cryo-EM resolution has improved significantly, it remains a powerful approach that can provide structural and dynamical insights that are not directly accessible from experimental data alone. Molecular dynamics simulations provide a means to extract atomistic details of conformational changes that are encoded in cryo-EM data and can also assist in improving the quality of structural models. Additionally, molecular dynamics simulations enable the characterization of conformational heterogeneity in cryo-EM data. We will summarize the advancements made in these techniques and highlight recent developments in this field.
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Affiliation(s)
- Osamu Miyashita
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
| | - Florence Tama
- RIKEN Center for Computational Science, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Department of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan; Institute of Transformative Bio-Molecules, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan.
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10
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Vénien-Bryan C, Fernandes CAH. Overview of Membrane Protein Sample Preparation for Single-Particle Cryo-Electron Microscopy Analysis. Int J Mol Sci 2023; 24:14785. [PMID: 37834233 PMCID: PMC10573263 DOI: 10.3390/ijms241914785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/21/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Single-particle cryo-electron microscopy (cryo-EM SPA) has recently emerged as an exceptionally well-suited technique for determining the structure of membrane proteins (MPs). Indeed, in recent years, huge increase in the number of MPs solved via cryo-EM SPA at a resolution better than 3.0 Å in the Protein Data Bank (PDB) has been observed. However, sample preparation remains a significant challenge in the field. Here, we evaluated the MPs solved using cryo-EM SPA deposited in the PDB in the last two years at a resolution below 3.0 Å. The most critical parameters for sample preparation are as follows: (i) the surfactant used for protein extraction from the membrane, (ii) the surfactant, amphiphiles, nanodiscs or other molecules present in the vitrification step, (iii) the vitrification method employed, and (iv) the type of grids used. The aim is not to provide a definitive answer on the optimal sample conditions for cryo-EM SPA of MPs but rather assess the current trends in the MP structural biology community towards obtaining high-resolution cryo-EM structures.
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Affiliation(s)
| | - Carlos A. H. Fernandes
- Unité Mixte de Recherche (UMR) 7590, Centre National de la Recherche Scientifique (CNRS), Muséum National d’Histoire Naturelle, Institut de Recherche pour le Développement (IRD), Institut de Minéralogie, Physique des Matériaux et de Cosmochimie (IMPMC), Sorbonne Université, 75005 Paris, France;
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11
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Krieger JM, Sorzano COS, Carazo JM. Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing. Int J Mol Sci 2023; 24:14245. [PMID: 37762547 PMCID: PMC10532346 DOI: 10.3390/ijms241814245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/10/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.
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Affiliation(s)
- James M. Krieger
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
| | | | - Jose Maria Carazo
- Biocomputing Unit, National Centre for Biotechnology (CNB CSIC), Campus Universidad Autónoma de Madrid, Darwin 3, Cantoblanco, 28049 Madrid, Spain
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12
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Tang WS, Zhong ED, Hanson SM, Thiede EH, Cossio P. Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy. Curr Opin Struct Biol 2023; 81:102626. [PMID: 37311334 DOI: 10.1016/j.sbi.2023.102626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 06/15/2023]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.
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Affiliation(s)
- Wai Shing Tang
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/WaiShingTang
| | - Ellen D Zhong
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ, 08544, United States. https://twitter.com/ZhongingAlong
| | - Sonya M Hanson
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/sonyahans
| | - Erik H Thiede
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States. https://twitter.com/erik_der_elch
| | - Pilar Cossio
- Center for Computational Mathematics, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States; Center for Computational Biology, Flatiron Institute, 162 5th Ave, New York, NY, 10010, United States.
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13
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Vuillemot R, Rouiller I, Jonić S. MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations. Sci Rep 2023; 13:10596. [PMID: 37391578 PMCID: PMC10313669 DOI: 10.1038/s41598-023-37037-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/14/2023] [Indexed: 07/02/2023] Open
Abstract
Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Isabelle Rouiller
- Department of Biochemistry and Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Australian Research Council Centre for Cryo-Electron Microscopy of Membrane Proteins, Parkville, VIC, 3052, Australia
| | - Slavica Jonić
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, CC 115, 4 Place Jussieu, 75005, Paris, France.
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14
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Chen M, Toader B, Lederman R. Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models. J Mol Biol 2023; 435:168014. [PMID: 36806476 PMCID: PMC10164680 DOI: 10.1016/j.jmb.2023.168014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/17/2023]
Abstract
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å-1, and present the results in a more interpretable form.
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Affiliation(s)
- Muyuan Chen
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
| | - Bogdan Toader
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Roy Lederman
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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15
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Toader B, Sigworth FJ, Lederman RR. Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity. J Mol Biol 2023; 435:168020. [PMID: 36863660 PMCID: PMC10164696 DOI: 10.1016/j.jmb.2023.168020] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/02/2023]
Abstract
Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.
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Affiliation(s)
- Bogdan Toader
- Department of Statistics and Data Science, Yale University, United States.
| | - Fred J Sigworth
- Department of Cellular and Molecular Physiology, Yale University, United States
| | - Roy R Lederman
- Department of Statistics and Data Science, Yale University, United States. https://twitter.com/roylederman
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16
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Chang L, Mondal A, MacCallum JL, Perez A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J Phys Chem A 2023; 127:3906-3913. [PMID: 37084537 DOI: 10.1021/acs.jpca.3c01731] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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17
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Vuillemot R, Mirzaei A, Harastani M, Hamitouche I, Fréchin L, Klaholz BP, Miyashita O, Tama F, Rouiller I, Jonic S. MDSPACE: Extracting Continuous Conformational Landscapes from Cryo-EM Single Particle Datasets Using 3D-to-2D Flexible Fitting based on Molecular Dynamics Simulation. J Mol Biol 2023; 435:167951. [PMID: 36638910 DOI: 10.1016/j.jmb.2023.167951] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/08/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
This article presents an original approach for extracting atomic-resolution landscapes of continuous conformational variability of biomolecular complexes from cryo electron microscopy (cryo-EM) single particle images. This approach is based on a new 3D-to-2D flexible fitting method, which uses molecular dynamics (MD) simulation and is embedded in an iterative conformational-landscape refinement scheme. This new approach is referred to as MDSPACE, which stands for Molecular Dynamics simulation for Single Particle Analysis of Continuous Conformational hEterogeneity. The article describes the MDSPACE approach and shows its performance using synthetic and experimental datasets.
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Affiliation(s)
- Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France; Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Alex Mirzaei
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Ilyes Hamitouche
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Léo Fréchin
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | - Bruno P Klaholz
- Centre for Integrative Biology, Department of Integrated Structural Biology, IGBMC-UMR 7104 CNRS, U964 Inserm, Université de Strasbourg, Strasbourg, France
| | | | - Florence Tama
- RIKEN Center for Computational Science, Kobe, Japan; Institute of Transformative Biomolecules, Graduate School of Science, Nagoya University, Nagoya, Japan; Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Japan
| | - Isabelle Rouiller
- Department of Biochemistry & Pharmacology and Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Victoria, Australia
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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18
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Dasgupta B, Tiwari SP. Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics. Biophys Rev 2022; 14:1379-1392. [PMID: 36659985 PMCID: PMC9842844 DOI: 10.1007/s12551-022-01026-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/27/2022] [Indexed: 12/23/2022] Open
Abstract
The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.
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Affiliation(s)
- Bhaskar Dasgupta
- Research Center for Advanced Science and Technology, University of Tokyo, 4-6-1 Komaba, Meguro-Ku, Tokyo, 153-8904 Japan
| | - Sandhya P. Tiwari
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima City, 1-3-1 Kagamiyama, Hiroshima, 739-8526 Japan
- Present Address: Institute of Protein Research, Osaka University, 3-2 Yamadaoka, Suita-Shi, Osaka, 565-0871 Japan
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19
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Harastani M, Vuillemot R, Hamitouche I, Moghadam NB, Jonic S. ContinuousFlex: Software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy and tomography data. J Struct Biol 2022; 214:107906. [PMID: 36244611 DOI: 10.1016/j.jsb.2022.107906] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/02/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
ContinuousFlex is a user-friendly open-source software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET) data. In 2019, ContinuousFlex became available as a plugin for Scipion, an image processing software package extensively used in the cryo-EM field. Currently, ContinuousFlex contains software for running (1) recently published methods HEMNMA-3D, TomoFlow, and NMMD; (2) earlier published methods HEMNMA and StructMap; and (3) methods for simulating cryo-EM and cryo-ET data with conformational variability and methods for data preprocessing. It also includes external software for molecular dynamics simulation (GENESIS) and normal mode analysis (ElNemo), used in some of the mentioned methods. The HEMNMA software has been presented in the past, but not the software of other methods. Besides, ContinuousFlex currently also offers a deep learning extension of HEMNMA, named DeepHEMNMA. In this article, we review these methods in the context of the ContinuousFlex package, developed to facilitate their use by the community.
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Affiliation(s)
- Mohamad Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Rémi Vuillemot
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Ilyes Hamitouche
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Nima Barati Moghadam
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France
| | - Slavica Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, Muséum National d'Histoire Naturelle, Paris, France.
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20
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Beton JG, Cragnolini T, Kaleel M, Mulvaney T, Sweeney A, Topf M. Integrating model simulation tools and
cryo‐electron
microscopy. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Joseph George Beton
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck and University College London London UK
| | - Manaz Kaleel
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
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21
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Farafonov VS, Stich M, Nerukh D. Reconstruction and validation of entire virus model with complete genome from mixed resolution cryo-EM density. Faraday Discuss 2022; 240:152-167. [PMID: 35916040 DOI: 10.1039/d2fd00053a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
It is very difficult to reconstruct computationally a large biomolecular complex in its biological entirety from experimental data. The resulting atomistic model should not contain gaps structurally and it should yield stable dynamics. We, for the first time, reconstruct from the published incomplete cryo-EM density a complete MS2 virus at atomistic resolution, that is, the capsid with the genome, and validate the result by all-atom molecular dynamics with explicit water. The available experimental data includes a high resolution protein capsid and an inhomogeneously resolved genome map. For the genomic RNA, apart from 16 hairpins with atomistic resolution, the strands near the capsid's inner surface were resolved up to the nucleic backbone level, and the innermost density was completely unresolved. As a result, only 242 nucleotides (out of 3569) were positioned, while only a fragmented backbone was outlined for the rest of the genome, making a detailed model reconstruction necessary. For model reconstruction, in addition to the available atomistic structure information, we extensively used the predicted secondary structure of the genome (base pairing). The technique was based on semi-automatic building of relatively large strands of RNA with subsequent manual positioning over the traced backbone. The entire virus structure (capsid + genome) was validated by a molecular dynamics run in physiological solution with ions at standard conditions confirming the stability of the model.
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Affiliation(s)
- Vladimir S Farafonov
- Department of Physical Chemistry, Kharkiv National University, Ukraine.,Department of Mathematics, Aston University, Birmingham, UK.
| | - Michael Stich
- Area of Applied Mathematics, Universidad Rey Juan Carlos, Madrid, Spain.,Department of Mathematics, Aston University, Birmingham, UK.
| | - Dmitry Nerukh
- Department of Mathematics, Aston University, Birmingham, UK.
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22
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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23
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Jiménez de la Morena J, Conesa P, Fonseca YC, de Isidro-Gómez FP, Herreros D, Fernández-Giménez E, Strelak D, Moebel E, Buchholz TO, Jug F, Martinez-Sanchez A, Harastani M, Jonic S, Conesa JJ, Cuervo A, Losana P, Sánchez I, Iceta M, Del Cano L, Gragera M, Melero R, Sharov G, Castaño-Díez D, Koster A, Piccirillo JG, Vilas JL, Otón J, Marabini R, Sorzano COS, Carazo JM. ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation. J Struct Biol 2022; 214:107872. [PMID: 35660516 PMCID: PMC7613607 DOI: 10.1016/j.jsb.2022.107872] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022]
Abstract
Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years ago. Its data processing workflows are far from being well defined and the user experience is still not smooth. Moreover, file formats of different software packages and their associated metadata are not standardized, mainly since different packages are developed by different groups, focusing on different steps of the data processing pipeline. The Scipion framework, originally developed for SPA (de la Rosa-Trevín et al., 2016), has a generic python workflow engine that gives it the versatility to be extended to other fields, as demonstrated for model building (Martínez et al., 2020). In this article, we provide an extension of Scipion based on a set of tomography plugins (referred to as ScipionTomo hereafter), with a similar purpose: to allow users to be focused on the data processing and analysis instead of having to deal with multiple software installation issues and the inconvenience of switching from one to another, converting metadata files, managing possible incompatibilities, scripting (writing a simple program in a language that the computer must convert to machine language each time the program is run), etcetera. Additionally, having all the software available in an integrated platform allows comparing the results of different algorithms trying to solve the same problem. In this way, the commonalities and differences between estimated parameters shed light on which results can be more trusted than others. ScipionTomo is developed by a collaborative multidisciplinary team composed of Scipion team engineers, structural biologists, and in some cases, the developers whose software packages have been integrated. It is open to anyone in the field willing to contribute to this project. The result is a framework extension that combines the acquired knowledge of Scipion developers in close collaboration with third-party developers, and the on-demand design of functionalities requested by beta testers applying this solution to actual biological problems.
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Affiliation(s)
| | - P Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Y C Fonseca
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Herreros
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Strelak
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Masaryk University, Brno, Czech Republic
| | - E Moebel
- Inria Rennes - Bretagne Atlantique, Rennes
| | - T O Buchholz
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Center for Systems Biology Dresden (CSBD), Germany
| | - F Jug
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Fondazione Human Technopole, Milan, Italy
| | - A Martinez-Sanchez
- University of Oviedo, Department of Computer Sciences, Oviedo, Spain; Health Research Institute of Asturias (ISPA), Oviedo, Spain
| | - M Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - S Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - J J Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - A Cuervo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - P Losana
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - I Sánchez
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Iceta
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - L Del Cano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Gragera
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - R Melero
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - G Sharov
- Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - D Castaño-Díez
- BioEM Lab, Biozentrum, University of Basel, Basel, Switzerland
| | - A Koster
- University of Leiden, Ultrastructural and molecular imaging, Leiden, The Netherlands
| | - J G Piccirillo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J L Vilas
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J Otón
- Alba Synchrotron - CELLS (ICTS), Barcelona, Spain
| | - R Marabini
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Superior Polytechnic School. Univ. Autónoma of Madrid. Madrid, Spain
| | - C O S Sorzano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J M Carazo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
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