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Aldama LA, Dalton KM, Hekstra DR. Correcting systematic errors in diffraction data with modern scaling algorithms. Acta Crystallogr D Struct Biol 2023; 79:796-805. [PMID: 37584427 PMCID: PMC10478637 DOI: 10.1107/s2059798323005776] [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: 05/02/2023] [Accepted: 06/30/2023] [Indexed: 08/17/2023] Open
Abstract
X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.
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Affiliation(s)
- Luis A. Aldama
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
- Biophysics Graduate Program, Harvard University, Cambridge, Massachusetts, USA
| | - Kevin M. Dalton
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Doeke R. Hekstra
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
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Jensen M, Chandrasekaran V, García-Bonete MJ, Li S, Anindya AL, Andersson K, Erlandsson MC, Oparina NY, Burmann BM, Brath U, Panchenko AR, Bokarewa I. M, Katona G. Survivin prevents the polycomb repressor complex 2 from methylating histone 3 lysine 27. iScience 2023; 26:106976. [PMID: 37534134 PMCID: PMC10391610 DOI: 10.1016/j.isci.2023.106976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 08/04/2023] Open
Abstract
This study investigates the role of survivin in epigenetic control of gene transcription through interaction with the polycomb repressive complex 2 (PRC2). PRC2 is responsible for silencing gene expression by trimethylating lysine 27 on histone 3. We observed differential expression of PRC2 subunits in CD4+ T cells with varying levels of survivin expression, and ChIP-seq results indicated that survivin colocalizes with PRC2 along DNA. Inhibition of survivin resulted in a significant increase in H3K27 trimethylation, implying that survivin prevents PRC2 from functioning. Peptide microarray showed that survivin interacts with peptides from PRC2 subunits, and machine learning revealed that amino acid composition contains relevant information for predicting survivin interaction. NMR and BLI experiments supported the interaction of survivin with PRC2 subunit EZH2. Finally, protein-protein docking revealed that the survivin-EZH2 interaction interface overlaps with catalytic residues of EZH2, potentially inhibiting its H3K27 methylation activity. These findings suggest that survivin inhibits PRC2 function.
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Affiliation(s)
- Maja Jensen
- Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Box 462, 405 30 Gothenburg, Sweden
| | - Venkataragavan Chandrasekaran
- Department of Rheumatology and Inflammation Research, Institute of Medicine, University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
| | - María-José García-Bonete
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, Box 440, 405 30 Gothenburg, Sweden
| | - Shuxiang Li
- Department of Pathology and Molecular Medicine, School of Medicine, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Atsarina Larasati Anindya
- Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Box 462, 405 30 Gothenburg, Sweden
| | - Karin Andersson
- Department of Rheumatology and Inflammation Research, Institute of Medicine, University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
| | - Malin C. Erlandsson
- Department of Rheumatology and Inflammation Research, Institute of Medicine, University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
| | - Nina Y. Oparina
- Department of Rheumatology and Inflammation Research, Institute of Medicine, University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
| | - Björn M. Burmann
- Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Box 462, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 405 30 Gothenburg, Sweden
| | - Ulrika Brath
- Department of Chemistry and Molecular Biology and the Swedish NMR Centre, University of Gothenburg, 412 96 Gothenburg, Sweden
| | - Anna R. Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Maria Bokarewa I.
- Department of Rheumatology and Inflammation Research, Institute of Medicine, University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
- Rheumatology Clinic, Sahlgrenska University Hospital, Gröna stråket 16, 41346 Gothenburg, Sweden
| | - Gergely Katona
- Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Box 462, 405 30 Gothenburg, Sweden
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Greisman JB, Dalton KM, Hekstra DR. reciprocalspaceship: a Python library for crystallographic data analysis. J Appl Crystallogr 2021; 54:1521-1529. [PMID: 34671231 PMCID: PMC8493618 DOI: 10.1107/s160057672100755x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/23/2021] [Indexed: 11/10/2022] Open
Abstract
Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges - many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used pandas library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.
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Affiliation(s)
- Jack B. Greisman
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Kevin M. Dalton
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Doeke R. Hekstra
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
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Hatti KS, McCoy AJ, Read RJ. Likelihood-based estimation of substructure content from single-wavelength anomalous diffraction (SAD) intensity data. Acta Crystallogr D Struct Biol 2021; 77:880-893. [PMID: 34196615 PMCID: PMC8251343 DOI: 10.1107/s2059798321004538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/28/2021] [Indexed: 11/14/2022] Open
Abstract
SAD phasing can be challenging when the signal-to-noise ratio is low. In such cases, having an accurate estimate of the substructure content can determine whether or not the substructure of anomalous scatterer positions can successfully be determined. Here, a likelihood-based target function is proposed to accurately estimate the strength of the anomalous scattering contribution directly from the measured intensities, determining a complex correlation parameter relating the Bijvoet mates as a function of resolution. This gives a novel measure of the intrinsic anomalous signal. The SAD likelihood target function also accounts for correlated errors in the measurement of intensities from Bijvoet mates, which can arise from the effects of radiation damage. When the anomalous signal is assumed to come primarily from a substructure comprising one anomalous scatterer with a known value of f'' and when the protein composition of the crystal is estimated correctly, the refined complex correlation parameters can be interpreted in terms of the atomic content of the primary anomalous scatterer before the substructure is known. The maximum-likelihood estimation of substructure content was tested on a curated database of 357 SAD cases with useful anomalous signal. The prior estimates of substructure content are highly correlated to the content determined by phasing calculations, with a correlation coefficient (on a log-log basis) of 0.72.
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Affiliation(s)
- Kaushik S. Hatti
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Airlie J. McCoy
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Cambridge Institute for Medical Research, Department of Haematology, University of Cambridge, The Keith Peters Building, Hills Road, Cambridge CB2 0XY, United Kingdom
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Nass K, Cheng R, Vera L, Mozzanica A, Redford S, Ozerov D, Basu S, James D, Knopp G, Cirelli C, Martiel I, Casadei C, Weinert T, Nogly P, Skopintsev P, Usov I, Leonarski F, Geng T, Rappas M, Doré AS, Cooke R, Nasrollahi Shirazi S, Dworkowski F, Sharpe M, Olieric N, Bacellar C, Bohinc R, Steinmetz MO, Schertler G, Abela R, Patthey L, Schmitt B, Hennig M, Standfuss J, Wang M, Milne CJ. Advances in long-wavelength native phasing at X-ray free-electron lasers. IUCRJ 2020; 7:965-975. [PMID: 33209311 PMCID: PMC7642782 DOI: 10.1107/s2052252520011379] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/19/2020] [Indexed: 05/31/2023]
Abstract
Long-wavelength pulses from the Swiss X-ray free-electron laser (XFEL) have been used for de novo protein structure determination by native single-wavelength anomalous diffraction (native-SAD) phasing of serial femtosecond crystallography (SFX) data. In this work, sensitive anomalous data-quality indicators and model proteins were used to quantify improvements in native-SAD at XFELs such as utilization of longer wavelengths, careful experimental geometry optimization, and better post-refinement and partiality correction. Compared with studies using shorter wavelengths at other XFELs and older software versions, up to one order of magnitude reduction in the required number of indexed images for native-SAD was achieved, hence lowering sample consumption and beam-time requirements significantly. Improved data quality and higher anomalous signal facilitate so-far underutilized de novo structure determination of challenging proteins at XFELs. Improvements presented in this work can be used in other types of SFX experiments that require accurate measurements of weak signals, for example time-resolved studies.
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Affiliation(s)
- Karol Nass
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Robert Cheng
- LeadXpro AG, Park InnovAARE, Villigen, 5234, Switzerland
| | - Laura Vera
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Aldo Mozzanica
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Sophie Redford
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Dmitry Ozerov
- Science IT, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Shibom Basu
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Daniel James
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Gregor Knopp
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Claudio Cirelli
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Isabelle Martiel
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Cecilia Casadei
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Tobias Weinert
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Przemyslaw Nogly
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zürich, Wolfgang-Pauli-Strasse 27, Zürich, 8093, Switzerland
| | - Petr Skopintsev
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
- Department of Biology, ETH Zürich, Wolfgang-Pauli-Strasse 27, Zürich, 8093, Switzerland
| | - Ivan Usov
- Science IT, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Filip Leonarski
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Tian Geng
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, United Kingdom
| | - Mathieu Rappas
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, United Kingdom
| | - Andrew S. Doré
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, United Kingdom
| | - Robert Cooke
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, United Kingdom
| | | | - Florian Dworkowski
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - May Sharpe
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Natacha Olieric
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Camila Bacellar
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Rok Bohinc
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Michel O. Steinmetz
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
- Biozentrum, University of Basel, Basel, 4056, Switzerland
| | - Gebhard Schertler
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
- Department of Biology, ETH Zürich, Wolfgang-Pauli-Strasse 27, Zürich, 8093, Switzerland
| | - Rafael Abela
- LeadXpro AG, Park InnovAARE, Villigen, 5234, Switzerland
| | - Luc Patthey
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Bernd Schmitt
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Michael Hennig
- LeadXpro AG, Park InnovAARE, Villigen, 5234, Switzerland
| | - Jörg Standfuss
- Laboratory of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Meitian Wang
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
| | - Christopher J. Milne
- Photon Science Division, Paul Scherrer Institut, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland
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