1
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Wankowicz SA, Ravikumar A, Sharma S, Riley BT, Raju A, Flowers J, Hogan D, van den Bedem H, Keedy DA, Fraser JS. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM. bioRxiv 2024:2023.06.28.546963. [PMID: 37425870 PMCID: PMC10327213 DOI: 10.1101/2023.06.28.546963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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Affiliation(s)
- Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Ph.D. Program in Biology, The Graduate Center – City University of New York, New York, NY 10016
| | - Blake T. Riley
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Atomwise, Inc., San Francisco, CA, United States
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031
- Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center – City University of New York, New York, NY 10016
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
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2
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Perrett S, Chatrchyan V, Buckup T, van Thor JJ. Application of density matrix Wigner transforms for ultrafast macromolecular and chemical x-ray crystallography. J Chem Phys 2024; 160:100901. [PMID: 38456527 DOI: 10.1063/5.0188888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024] Open
Abstract
Time-Resolved Serial Femtosecond Crystallography (TR-SFX) conducted at X-ray Free Electron Lasers (XFELs) has become a powerful tool for capturing macromolecular structural movies of light-initiated processes. As the capabilities of XFELs advance, we anticipate that a new range of coherent control and structural Raman measurements will become achievable. Shorter optical and x-ray pulse durations and increasingly more exotic pulse regimes are becoming available at free electron lasers. Moreover, with high repetition enabled by the superconducting technology of European XFEL (EuXFEL) and Linac Coherent Light Source (LCLS-II) , it will be possible to improve the signal-to-noise ratio of the light-induced differences, allowing for the observation of vibronic motion on the sub-Angstrom level. To predict and assign this coherent motion, which is measurable with a structural technique, new theoretical approaches must be developed. In this paper, we present a theoretical density matrix approach to model the various population and coherent dynamics of a system, which considers molecular system parameters and excitation conditions. We emphasize the use of the Wigner transform of the time-dependent density matrix, which provides a phase space representation that can be directly compared to the experimental positional displacements measured in a TR-SFX experiment. Here, we extend the results from simple models to include more realistic schemes that include large relaxation terms. We explore a variety of pulse schemes using multiple model systems using realistic parameters. An open-source software package is provided to perform the density matrix simulation and Wigner transformations. The open-source software allows us to define any arbitrary level schemes as well as any arbitrary electric field in the interaction Hamiltonian.
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Affiliation(s)
- Samuel Perrett
- Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, United Kingdom
| | - Viktoria Chatrchyan
- Physikalisch Chemisches Institut, Ruprecht-Karls Universität, D-69120 Heidelberg, Germany
| | - Tiago Buckup
- Physikalisch Chemisches Institut, Ruprecht-Karls Universität, D-69120 Heidelberg, Germany
| | - Jasper J van Thor
- Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London, United Kingdom
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3
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Fraser JS, Murcko MA. Structure is beauty, but not always truth. Cell 2024; 187:517-520. [PMID: 38306978 PMCID: PMC10947451 DOI: 10.1016/j.cell.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/04/2024]
Abstract
Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and "anti-targets." Overcoming these challenges will amplify the impact of structural biology on drug discovery.
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Affiliation(s)
- James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
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4
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Mendez D, Holton JM, Lyubimov AY, Hollatz S, Mathews II, Cichosz A, Martirosyan V, Zeng T, Stofer R, Liu R, Song J, McPhillips S, Soltis M, Cohen AE. Deep residual networks for crystallography trained on synthetic data. Acta Crystallogr D Struct Biol 2024; 80:26-43. [PMID: 38164955 PMCID: PMC10833344 DOI: 10.1107/s2059798323010586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 12/12/2023] [Indexed: 01/03/2024] Open
Abstract
The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.
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Affiliation(s)
- Derek Mendez
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - James M. Holton
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Biochemistry and Biophysics, UC San Francisco, San Francisco, CA 94158, USA
| | - Artem Y. Lyubimov
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Sabine Hollatz
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Irimpan I. Mathews
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Aleksander Cichosz
- Department of Statistics and Applied Probability, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Vardan Martirosyan
- Department of Mathematics, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Teo Zeng
- Department of Statistics and Applied Probability, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Ryan Stofer
- Department of Statistics and Applied Probability, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Ruobin Liu
- Department of Statistics and Applied Probability, UC Santa Barbara, Santa Barbara, CA 93106, USA
| | - Jinhu Song
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Scott McPhillips
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Mike Soltis
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Aina E. Cohen
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
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5
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Young ID, Mendez D, Poon BK, Blaschke JP, Wittwer F, Wall ME, Sauter NK. Interpreting macromolecular diffraction through simulation. Methods Enzymol 2023; 688:195-222. [PMID: 37748827 DOI: 10.1016/bs.mie.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
This chapter discusses the use of diffraction simulators to improve experimental outcomes in macromolecular crystallography, in particular for future experiments aimed at diffuse scattering. Consequential decisions for upcoming data collection include the selection of either a synchrotron or free electron laser X-ray source, rotation geometry or serial crystallography, and fiber-coupled area detector technology vs. pixel-array detectors. The hope is that simulators will provide insights to make these choices with greater confidence. Simulation software, especially those packages focused on physics-based calculation of the diffraction, can help to predict the location, size, shape, and profile of Bragg spots and diffuse patterns in terms of an underlying physical model, including assumptions about the crystal's mosaic structure, and therefore can point to potential issues with data analysis in the early planning stages. Also, once the data are collected, simulation may offer a pathway to improve the measurement of diffraction, especially with weak data, and might help to treat problematic cases such as overlapping patterns.
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Affiliation(s)
- Iris D Young
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Derek Mendez
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Billy K Poon
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Johannes P Blaschke
- National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Felix Wittwer
- National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Michael E Wall
- Computer, Computational and Statistical Sciences Division, Los Alamos, NM, United States
| | - Nicholas K Sauter
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
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6
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Case DA. MD simulations of macromolecular crystals: Implications for the analysis of Bragg and diffuse scattering. Methods Enzymol 2023; 688:145-168. [PMID: 37748825 DOI: 10.1016/bs.mie.2023.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Some of our most detailed information about structure and dynamics of macromolecules comes from X-ray-diffraction studies in crystalline environments. More than 170,000 atomic models have been deposited in the Protein Data Bank, and the number of observations (typically of intensities of Bragg diffraction peaks) is generally quite large, when compared to other experimental methods. Nevertheless, the general agreement between calculated and observed intensities is far outside the experimental precision, and the majority of scattered photons fall between the sharp Bragg peaks, and are rarely taken into account. This chapter considers how molecular dynamics simulations can be used to explore the connections between microscopic behavior in a crystalline lattice and observed scattering intensities, and point the way to new atomic models that could more faithfully recapitulate Bragg intensities and extract useful information from the diffuse scattering that lies between those peaks.
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Affiliation(s)
- David A Case
- Dept. of Chemistry & Chemical Biology, Rutgers University, Piscataway, NJ, United States.
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7
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Domagala S, Nourd P, Diederichs K, Henn J. Progress in detection of and correction for low-energy contamination. J Appl Crystallogr 2023; 56:1200-1220. [PMID: 37555226 PMCID: PMC10405589 DOI: 10.1107/s1600576723004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/30/2023] [Indexed: 08/10/2023] Open
Abstract
Contamination with low-energy radiation leads to an increased number of weighted residuals being larger in absolute terms than three standard uncertainties. For a Gaussian distribution, these rare events occur only in 0.27% of all cases, which is a small number for small- to medium-sized data sets. The correct detection of rare events - and an adequate correction procedure - thus relies crucially on correct standard uncertainties, which are often not available [Henn (2019), Crystallogr. Rev. 25, 83-156]. It is therefore advisable to use additional, more robust, metrics to complement the established ones. These metrics are developed here and applied to reference data sets from two different publications about low-energy contamination. Other systematic errors were found in the reference data sets. These errors compromise the correction procedures and may lead to under- or overcompensation. This can be demonstrated clearly with the new metrics. Empirical correction procedures generally may be compromised or bound to fail in the presence of other systematic errors. The following systematic errors, which were found in the reference data sets, need to be corrected for prior to application of the low-energy contamination correction procedure: signals of 2λ contamination, extinction, disorder, twinning, and too-large or too-low standard uncertainties (this list may not be complete). All five reference data sets of one publication show a common resolution-dependent systematic error of unknown origin. How this affects the correction procedure can be stated only after elimination of this error. The methodological improvements are verified with data published by other authors.
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Affiliation(s)
- Slawomir Domagala
- DataQ Intelligence, Fichtelgebirgsstrasse 66, 95448 Bayreuth, Germany
- Codivert, Żubrowa 15, 01-978 Warsaw, Poland
| | - Petrick Nourd
- DataQ Intelligence, Fichtelgebirgsstrasse 66, 95448 Bayreuth, Germany
| | - Kay Diederichs
- Department of Biology, University of Konstanz, Universitätsstrasse 19, 78457 Konstanz, Germany
| | - Julian Henn
- DataQ Intelligence, Fichtelgebirgsstrasse 66, 95448 Bayreuth, Germany
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8
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Doyle M, Bhowmick A, Wych DC, Lassalle L, Simon PS, Holton J, Sauter NK, Yachandra VK, Kern JF, Yano J, Wall ME. Water Networks in Photosystem II Using Crystalline Molecular Dynamics Simulations and Room-Temperature XFEL Serial Crystallography. J Am Chem Soc 2023; 145:14621-14635. [PMID: 37369071 PMCID: PMC10347547 DOI: 10.1021/jacs.3c01412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Indexed: 06/29/2023]
Abstract
Structural dynamics of water and its hydrogen-bonding networks play an important role in enzyme function via the transport of protons, ions, and substrates. To gain insights into these mechanisms in the water oxidation reaction in Photosystem II (PS II), we have performed crystalline molecular dynamics (MD) simulations of the dark-stable S1 state. Our MD model consists of a full unit cell with 8 PS II monomers in explicit solvent (861 894 atoms), enabling us to compute the simulated crystalline electron density and to compare it directly with the experimental density from serial femtosecond X-ray crystallography under physiological temperature collected at X-ray free electron lasers (XFELs). The MD density reproduced the experimental density and water positions with high fidelity. The detailed dynamics in the simulations provided insights into the mobility of water molecules in the channels beyond what can be interpreted from experimental B-factors and electron densities alone. In particular, the simulations revealed fast, coordinated exchange of waters at sites where the density is strong, and water transport across the bottleneck region of the channels where the density is weak. By computing MD hydrogen and oxygen maps separately, we developed a novel Map-based Acceptor-Donor Identification (MADI) technique that yields information which helps to infer hydrogen-bond directionality and strength. The MADI analysis revealed a series of hydrogen-bond wires emanating from the Mn cluster through the Cl1 and O4 channels; such wires might provide pathways for proton transfer during the reaction cycle of PS II. Our simulations provide an atomistic picture of the dynamics of water and hydrogen-bonding networks in PS II, with implications for the specific role of each channel in the water oxidation reaction.
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Affiliation(s)
- Margaret
D. Doyle
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Asmit Bhowmick
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - David C. Wych
- Computer,
Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center
for Non-linear Studies, Los Alamos National
Laboratory, Los Alamos, New Mexico 87545, United States
| | - Louise Lassalle
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Philipp S. Simon
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - James Holton
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Biochemistry and Biophysics, University
of California, San Francisco, San
Francisco, California 94158, United States
- SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Nicholas K. Sauter
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Vittal K. Yachandra
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jan F. Kern
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Junko Yano
- Molecular
Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Michael E. Wall
- Computer,
Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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9
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Ganapati V, Tchoń D, Brewster AS, Sauter NK. Self-Supervised Deep Learning for Model Correction in the Computational Crystallography Toolbox. ArXiv 2023:arXiv:2307.01901v1. [PMID: 37461412 PMCID: PMC10350105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
The Computational Crystallography Toolbox (cctbx) is open-source software that allows for processing of crystallographic data, including from serial femtosecond crystallography (SFX), for macromolecular structure determination. We aim to use the modules in cctbx to determine the oxidation state of individual metal atoms in a macromolecule. Changes in oxidation state are reflected in small shifts of the atom's X-ray absorption edge. These energy shifts can be extracted from the diffraction images recorded in serial femtosecond crystallography, given knowledge of a forward physics model. However, as the diffraction changes only slightly due to the absorption edge shift, inaccuracies in the forward physics model make it extremely challenging to observe the oxidation state. In this work, we describe the potential impact of using self-supervised deep learning to correct the scientific model in cctbx and provide uncertainty quantification. We provide code for forward model simulation and data analysis, built from cctbx modules, at https://github.com/gigantocypris/SPREAD, which can be integrated with machine learning. We describe open questions in algorithm development to help spur advances through dialog between crystallographers and machine learning researchers. New methods could help elucidate charge transfer processes in many reactions, including key events in photosynthesis.
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Affiliation(s)
- Vidya Ganapati
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Engineering, Swarthmore College, Swarthmore, PA 19081, USA
| | - Daniel Tchoń
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aaron S. Brewster
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nicholas K. Sauter
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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10
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Hekstra DR. Emerging Time-Resolved X-Ray Diffraction Approaches for Protein Dynamics. Annu Rev Biophys 2023; 52:255-274. [PMID: 37159292 PMCID: PMC10687665 DOI: 10.1146/annurev-biophys-111622-091155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Proteins guide the flows of information, energy, and matter that make life possible by accelerating transport and chemical reactions, by allosterically modulating these reactions, and by forming dynamic supramolecular assemblies. In these roles, conformational change underlies functional transitions. Time-resolved X-ray diffraction methods characterize these transitions either by directly triggering sequences of functionally important motions or, more broadly, by capturing the motions of which proteins are capable. To date, most successful have been experiments in which conformational change is triggered in light-dependent proteins. In this review, I emphasize emerging techniques that probe the dynamic basis of function in proteins lacking natively light-dependent transitions and speculate about extensions and further possibilities. In addition, I review how the weaker and more distributed signals in these data push the limits of the capabilities of analytical methods. Taken together, these new methods are beginning to establish a powerful paradigm for the study of the physics of protein function.
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Affiliation(s)
- Doeke R Hekstra
- Department of Molecular and Cellular Biology and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA;
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11
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Meisburger SP, Case DA, Ando N. Robust total X-ray scattering workflow to study correlated motion of proteins in crystals. Nat Commun 2023; 14:1228. [PMID: 36869043 DOI: 10.1038/s41467-023-36734-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/15/2023] [Indexed: 03/05/2023] Open
Abstract
The breathing motions of proteins are thought to play a critical role in function. However, current techniques to study key collective motions are limited to spectroscopy and computation. We present a high-resolution experimental approach based on the total scattering from protein crystals at room temperature (TS/RT-MX) that captures both structure and collective motions. To reveal the scattering signal from protein motions, we present a general workflow that enables robust subtraction of lattice disorder. The workflow introduces two methods: GOODVIBES, a detailed and refinable lattice disorder model based on the rigid-body vibrations of a crystalline elastic network; and DISCOBALL, an independent method of validation that estimates the displacement covariance between proteins in the lattice in real space. Here, we demonstrate the robustness of this workflow and further demonstrate how it can be interfaced with MD simulations towards obtaining high-resolution insight into functionally important protein motions.
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12
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Brehm W, White T, Chapman HN. Crystal diffraction prediction and partiality estimation using Gaussian basis functions. Acta Crystallogr A Found Adv 2023; 79:145-162. [PMID: 36862040 PMCID: PMC9979942 DOI: 10.1107/s2053273323000682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 01/25/2023] [Indexed: 02/19/2023] Open
Abstract
The recent diversification of macromolecular crystallographic experiments including the use of pink beams, convergent electron diffraction and serial snapshot crystallography has shown the limitations of using the Laue equations for diffraction prediction. This article gives a computationally efficient way of calculating approximate crystal diffraction patterns given varying distributions of the incoming beam, crystal shapes and other potentially hidden parameters. This approach models each pixel of a diffraction pattern and improves data processing of integrated peak intensities by enabling the correction of partially recorded reflections. The fundamental idea is to express the distributions as weighted sums of Gaussian functions. The approach is demonstrated on serial femtosecond crystallography data sets, showing a significant decrease in the required number of patterns to refine a structure to a given error.
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Affiliation(s)
- Wolfgang Brehm
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Germany,Department of Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany,Correspondence e-mail: ,
| | - Thomas White
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Germany
| | - Henry N. Chapman
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Germany,Department of Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany,The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany,Correspondence e-mail: ,
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13
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Wych DC, Aoto PC, Vu L, Wolff AM, Mobley DL, Fraser JS, Taylor SS, Wall ME. Molecular-dynamics simulation methods for macromolecular crystallography. Acta Crystallogr D Struct Biol 2023; 79:50-65. [PMID: 36601807 PMCID: PMC9815100 DOI: 10.1107/s2059798322011871] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022] Open
Abstract
It is investigated whether molecular-dynamics (MD) simulations can be used to enhance macromolecular crystallography (MX) studies. Historically, protein crystal structures have been described using a single set of atomic coordinates. Because conformational variation is important for protein function, researchers now often build models that contain multiple structures. Methods for building such models can fail, however, in regions where the crystallographic density is difficult to interpret, for example at the protein-solvent interface. To address this limitation, a set of MD-MX methods that combine MD simulations of protein crystals with conventional modeling and refinement tools have been developed. In an application to a cyclic adenosine monophosphate-dependent protein kinase at room temperature, the procedure improved the interpretation of ambiguous density, yielding an alternative water model and a revised protein model including multiple conformations. The revised model provides mechanistic insights into the catalytic and regulatory interactions of the enzyme. The same methods may be used in other MX studies to seek mechanistic insights.
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Affiliation(s)
- David C. Wych
- Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Phillip C. Aoto
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lily Vu
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Alexander M. Wolff
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David L. Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Susan S. Taylor
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Michael E. Wall
- Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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14
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Liu N, Mikhailovskii O, Skrynnikov NR, Xue Y. Simulating diffraction photographs based on molecular dynamics trajectories of a protein crystal: a new option to examine structure-solving strategies in protein crystallography. IUCrJ 2023; 10:16-26. [PMID: 36598499 PMCID: PMC9812212 DOI: 10.1107/s2052252522011198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A molecular dynamics (MD)-based pipeline has been designed and implemented to emulate the entire process of collecting diffraction photographs and calculating crystallographic structures of proteins from them. Using a structure of lysozyme solved in-house, a supercell comprising 125 (5 × 5 × 5) crystal unit cells containing a total of 1000 protein molecules and explicit interstitial solvent was constructed. For this system, two 300 ns MD trajectories at 298 and 250 K were recorded. A series of snapshots from these trajectories were then used to simulate a fully realistic set of diffraction photographs, which were further fed into the standard pipeline for structure determination. The resulting structures show very good agreement with the underlying MD model not only in terms of coordinates but also in terms of B factors; they are also consistent with the original experimental structure. The developed methodology should find a range of applications, such as optimizing refinement protocols to solve crystal structures and extracting dynamics information from diffraction data or diffuse scattering.
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Affiliation(s)
- Ning Liu
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | - Oleg Mikhailovskii
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Nikolai R. Skrynnikov
- Laboratory of Biomolecular NMR, St Petersburg State University, St Petersburg, Russian Federation
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | - Yi Xue
- School of Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
- Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, People’s Republic of China
- Tsinghua University–Peking University Joint Center for Life Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
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15
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Flanders PL, Contreras-Martel C, Brown NW, Shirley JD, Martins A, Nauta KN, Dessen A, Carlson EE, Ambrose EA. Combined Structural Analysis and Molecular Dynamics Reveal Penicillin-Binding Protein Inhibition Mode with β-Lactones. ACS Chem Biol 2022; 17:3110-3120. [PMID: 36173746 PMCID: PMC10057605 DOI: 10.1021/acschembio.2c00503] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
β-Lactam antibiotics comprise one of the most widely used therapeutic classes to combat bacterial infections. This general scaffold has long been known to inhibit bacterial cell wall biosynthesis by inactivating penicillin-binding proteins (PBPs); however, bacterial resistance to β-lactams is now widespread, and new strategies are urgently needed to target PBPs and other proteins involved in bacterial cell wall formation. A key requirement in the identification of strategies to overcome resistance is a deeper understanding of the roles of the PBPs and their associated proteins during cell growth and division, such as can be obtained with the use of selective chemical probes. Probe development has typically depended upon known PBP inhibitors, which have historically been thought to require a negatively charged moiety that mimics the C-terminus of the PBP natural peptidoglycan substrate, d-Ala-d-Ala. However, we have identified a new class of β-lactone-containing molecules that interact with PBPs, often in an isoform-specific manner, and do not incorporate this C-terminal mimetic. Here, we report a series of structural biology experiments and molecular dynamics simulations that we utilized to evaluate specific binding modes of this novel PBP inhibitor class. In this work, we obtained <2 Å resolution X-ray structures of four β-lactone probes bound to PBP1b from Streptococcus pneumoniae. Despite their diverging recognition modes beyond the site of covalent modification, these four probes all efficiently labeled PBP1b, as well as other PBPs from S. pneumoniae. From these structures, we analyzed protein-ligand interactions and characterized the β-lactone-bound active sites using in silico mutagenesis and molecular dynamics. Our approach has clarified the dynamic interaction profile in this series of ligands, expanding the understanding of PBP inhibitor binding.
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Affiliation(s)
- Parker L Flanders
- Department of Medicinal Chemistry, University of Minnesota, 208 Harvard Street SE, Minneapolis, Minnesota 55454, United States
| | - Carlos Contreras-Martel
- Université Grenoble Alpes, CNRS, CEA, Institut de Biologie Structurale (IBS), F-38044 Grenoble, France
| | - Nathaniel W Brown
- Department of Chemistry, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States
| | - Joshua D Shirley
- Department of Medicinal Chemistry, University of Minnesota, 208 Harvard Street SE, Minneapolis, Minnesota 55454, United States
| | - Alexandre Martins
- Université Grenoble Alpes, CNRS, CEA, Institut de Biologie Structurale (IBS), F-38044 Grenoble, France
| | - Kelsie N Nauta
- Department of Chemistry, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States
| | - Andréa Dessen
- Université Grenoble Alpes, CNRS, CEA, Institut de Biologie Structurale (IBS), F-38044 Grenoble, France.,Brazilian Biosciences National Laboratory (LNBio), CNPEM, Campinas 13084-971, São Paulo, Brazil
| | - Erin E Carlson
- Department of Medicinal Chemistry, University of Minnesota, 208 Harvard Street SE, Minneapolis, Minnesota 55454, United States.,Department of Chemistry, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.,Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, 321 Church Street SE, Minneapolis, Minnesota 55454, United States.,Department of Pharmacology, University of Minnesota, 321 Church Street SE, Minneapolis, Minnesota 55454, United States
| | - Elizabeth A Ambrose
- Department of Medicinal Chemistry, University of Minnesota, 208 Harvard Street SE, Minneapolis, Minnesota 55454, United States.,Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, Minnesota 55455, United States
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16
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Gabel F, Engilberge S, Schmitt E, Thureau A, Mechulam Y, Pérez J, Girard E. Medical contrast agents as promising tools for biomacromolecular SAXS experiments. Acta Crystallogr D Struct Biol 2022; 78:1120-1130. [PMID: 36048152 PMCID: PMC9435597 DOI: 10.1107/s2059798322007392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Lanthanide-based complexes are presented as a promising class of molecules for efficient SAXS contrast-variation experiments. Their interactions and contrast properties are analyzed for an oligomeric protein and a protein–RNA complex. Small-angle X-ray scattering (SAXS) has become an indispensable tool in structural biology, complementing atomic-resolution techniques. It is sensitive to the electron-density difference between solubilized biomacromolecules and the buffer, and provides information on molecular masses, particle dimensions and interactions, low-resolution conformations and pair distance-distribution functions. When SAXS data are recorded at multiple contrasts, i.e. at different solvent electron densities, it is possible to probe, in addition to their overall shape, the internal electron-density profile of biomacromolecular assemblies. Unfortunately, contrast-variation SAXS has been limited by the range of solvent electron densities attainable using conventional co-solutes (for example sugars, glycerol and salt) and by the fact that some biological systems are destabilized in their presence. Here, SAXS contrast data from an oligomeric protein and a protein–RNA complex are presented in the presence of iohexol and Gd-HPDO3A, two electron-rich molecules that are used in biomedical imaging and that belong to the families of iodinated and lanthanide-based complexes, respectively. Moderate concentrations of both molecules allowed solvent electron densities matching those of proteins to be attained. While iohexol yielded higher solvent electron densities (per mole), it interacted specifically with the oligomeric protein and precipitated the protein–RNA complex. Gd-HPDO3A, while less efficient (per mole), did not disrupt the structural integrity of either system, and atomic models could be compared with the SAXS data. Due to their elevated solubility and electron density, their chemical inertness, as well as the possibility of altering their physico-chemical properties, lanthanide-based complexes represent a class of molecules with promising potential for contrast-variation SAXS experiments on diverse biomacromolecular systems.
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17
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Gray JG, Giambaşu GM, Case DA, Luchko T. Integral equation models for solvent in macromolecular crystals. J Chem Phys 2022; 156:014801. [PMID: 34998331 PMCID: PMC8889494 DOI: 10.1063/5.0070869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The solvent can occupy up to ∼70% of macromolecular crystals, and hence, having models that predict solvent distributions in periodic systems could improve the interpretation of crystallographic data. Yet, there are few implicit solvent models applicable to periodic solutes, and crystallographic structures are commonly solved assuming a flat solvent model. Here, we present a newly developed periodic version of the 3D-reference interaction site model (RISM) integral equation method that is able to solve efficiently and describe accurately water and ion distributions in periodic systems; the code can compute accurate gradients that can be used in minimizations or molecular dynamics simulations. The new method includes an extension of the Ornstein–Zernike equation needed to yield charge neutrality for charged solutes, which requires an additional contribution to the excess chemical potential that has not been previously identified; this is an important consideration for nucleic acids or any other charged system where most or all the counter- and co-ions are part of the “disordered” solvent. We present several calculations of proteins, RNAs, and small molecule crystals to show that x-ray scattering intensities and the solvent structure predicted by the periodic 3D-RISM solvent model are in closer agreement with the experiment than are intensities computed using the default flat solvent model in the refmac5 or phenix refinement programs, with the greatest improvement in the 2 to 4 Å range. Prospects for incorporating integral equation models into crystallographic refinement are discussed.
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Affiliation(s)
- Jonathon G Gray
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - George M Giambaşu
- Institute for Quantitative Biomedicine, Rutgers University, Piscataway, New Jersey 08854, USA
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | - Tyler Luchko
- Department of Physics and Astronomy, California State University, Northridge, California 91330, USA
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18
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Chinnam NB, Syed A, Burnett KH, Hura GL, Tainer JA, Tsutakawa SE. Universally Accessible Structural Data on Macromolecular Conformation, Assembly, and Dynamics by Small Angle X-Ray Scattering for DNA Repair Insights. Methods Mol Biol 2022; 2444:43-68. [PMID: 35290631 PMCID: PMC9020468 DOI: 10.1007/978-1-0716-2063-2_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Structures provide a critical breakthrough step for biological analyses, and small angle X-ray scattering (SAXS) is a powerful structural technique to study dynamic DNA repair proteins. As toxic and mutagenic repair intermediates need to be prevented from inadvertently harming the cell, DNA repair proteins often chaperone these intermediates through dynamic conformations, coordinated assemblies, and allosteric regulation. By measuring structural conformations in solution for both proteins, DNA, RNA, and their complexes, SAXS provides insight into initial DNA damage recognition, mechanisms for validation of their substrate, and pathway regulation. Here, we describe exemplary SAXS analyses of a DNA damage response protein spanning from what can be derived directly from the data to obtaining super resolution through the use of SAXS selection of atomic models. We outline strategies and tactics for practical SAXS data collection and analysis. Making these structural experiments in reach of any basic and clinical researchers who have protein, SAXS data can readily be collected at government-funded synchrotrons, typically at no cost for academic researchers. In addition to discussing how SAXS complements and enhances cryo-electron microscopy, X-ray crystallography, NMR, and computational modeling, we furthermore discuss taking advantage of recent advances in protein structure prediction in combination with SAXS analysis.
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Affiliation(s)
- Naga Babu Chinnam
- Department of Molecular and Cellular Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Aleem Syed
- Department of Molecular and Cellular Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Kathryn H Burnett
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Greg L Hura
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Chemistry and Biochemistry Department, University of California Santa Cruz, Santa Cruz, CA, USA
| | - John A Tainer
- Department of Molecular and Cellular Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Susan E Tsutakawa
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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19
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Alexander LT, Lepore R, Kryshtafovych A, Adamopoulos A, Alahuhta M, Arvin AM, Bomble YJ, Böttcher B, Breyton C, Chiarini V, Chinnam NB, Chiu W, Fidelis K, Grinter R, Gupta GD, Hartmann MD, Hayes CS, Heidebrecht T, Ilari A, Joachimiak A, Kim Y, Linares R, Lovering AL, Lunin VV, Lupas AN, Makbul C, Michalska K, Moult J, Mukherjee PK, Nutt W(S, Oliver SL, Perrakis A, Stols L, Tainer JA, Topf M, Tsutakawa SE, Valdivia‐Delgado M, Schwede T. Target highlights in CASP14: Analysis of models by structure providers. Proteins 2021; 89:1647-1672. [PMID: 34561912 PMCID: PMC8616854 DOI: 10.1002/prot.26247] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 12/11/2022]
Abstract
The biological and functional significance of selected Critical Assessment of Techniques for Protein Structure Prediction 14 (CASP14) targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modeled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins.
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Affiliation(s)
- Leila T. Alexander
- Biozentrum, University of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
| | | | | | - Athanassios Adamopoulos
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Markus Alahuhta
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Ann M. Arvin
- Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Microbiology and ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Yannick J. Bomble
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Bettina Böttcher
- Biocenter and Rudolf Virchow Center, Julius‐Maximilians Universität WürzburgWürzburgGermany
| | - Cécile Breyton
- Univ. Grenoble Alpes, CNRS, CEA, Institute for Structural BiologyGrenobleFrance
| | - Valerio Chiarini
- Program in Structural Biology and BiophysicsInstitute of Biotechnology, University of HelsinkiHelsinkiFinland
| | - Naga babu Chinnam
- Department of Molecular and Cellular OncologyThe University of Texas M.D. Anderson Cancer CenterHoustonTexasUSA
| | - Wah Chiu
- Microbiology and ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
- BioengineeringStanford University School of MedicineStanfordCaliforniaUSA
- Division of Cryo‐EM and Bioimaging SSRLSLAC National Accelerator LaboratoryMenlo ParkCaliforniaUSA
| | | | - Rhys Grinter
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of MicrobiologyMonash UniversityClaytonAustralia
| | - Gagan D. Gupta
- Radiation Biology & Health Sciences DivisionBhabha Atomic Research CentreMumbaiIndia
| | - Marcus D. Hartmann
- Department of Protein EvolutionMax Planck Institute for Developmental BiologyTübingenGermany
| | - Christopher S. Hayes
- Department of Molecular, Cellular and Developmental BiologyUniversity of California, Santa BarbaraSanta BarbaraCaliforniaUSA
- Biomolecular Science and Engineering ProgramUniversity of California, Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Tatjana Heidebrecht
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Andrea Ilari
- Institute of Molecular Biology and Pathology of the National Research Council of Italy (CNR)RomeItaly
| | - Andrzej Joachimiak
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
- Department of Biochemistry and Molecular BiologyUniversity of ChicagoChicagoIllinoisUSA
| | - Youngchang Kim
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - Romain Linares
- Univ. Grenoble Alpes, CNRS, CEA, Institute for Structural BiologyGrenobleFrance
| | | | - Vladimir V. Lunin
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Andrei N. Lupas
- Department of Protein EvolutionMax Planck Institute for Developmental BiologyTübingenGermany
| | - Cihan Makbul
- Biocenter and Rudolf Virchow Center, Julius‐Maximilians Universität WürzburgWürzburgGermany
| | - Karolina Michalska
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - John Moult
- Department of Cell Biology and Molecular GeneticsInstitute for Bioscience and Biotechnology Research, University of MarylandRockvilleMarylandUSA
| | - Prasun K. Mukherjee
- Nuclear Agriculture & Biotechnology DivisionBhabha Atomic Research CentreMumbaiIndia
| | - William (Sam) Nutt
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - Stefan L. Oliver
- Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
| | - Anastassis Perrakis
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Lucy Stols
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - John A. Tainer
- Department of Molecular and Cellular OncologyThe University of Texas M.D. Anderson Cancer CenterHoustonTexasUSA
- Department of Cancer BiologyUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Maya Topf
- Institute of Structural and Molecular Biology, Birkbeck, University College LondonLondonUK
- Centre for Structural Systems Biology, Leibniz‐Institut für Experimentelle VirologieHamburgGermany
| | - Susan E. Tsutakawa
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | | | - Torsten Schwede
- Biozentrum, University of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
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20
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Czyzewski A, Krawiec F, Brzezinski D, Porebski PJ, Minor W. Detecting anomalies in X-ray diffraction images using convolutional neural networks. Expert Syst Appl 2021; 174:114740. [PMID: 34366575 PMCID: PMC8341115 DOI: 10.1016/j.eswa.2021.114740] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Our understanding of life is based upon the interpretation of macromolecular structures and their dynamics. Almost 90% of currently known macromolecular models originated from electron density maps constructed using X-ray diffraction images. Even though diffraction images are critical for structure determination, due to their vast amounts and noisy, non-intuitive nature, their quality is rarely inspected. In this paper, we use recent advances in machine learning to automatically detect seven types of anomalies in X-ray diffraction images. For this purpose, we utilize a novel X-ray beam center detection algorithm, propose three different image representations, and compare the predictive performance of general-purpose classifiers and deep convolutional neural networks (CNNs). In benchmark tests on a set of 6,311 X-ray diffraction images, the proposed CNN achieved between 87% and 99% accuracy depending on the type of anomaly. Experimental results show that the proposed anomaly detection system can be considered suitable for early detection of sub-optimal data collection conditions and malfunctions at X-ray experimental stations.
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Affiliation(s)
- Adam Czyzewski
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
| | - Faustyna Krawiec
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
| | - Dariusz Brzezinski
- Institute of Computing Science, Poznan University of
Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
- Center for Biocrystallographic Research, Institute of
Bioorganic Chemistry, Polish Academy of Sciences, Poznan, 61-714, Poland
- Center for Artificial Intelligence and Machine Learning,
Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznan, Poland
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
| | - Przemyslaw Jerzy Porebski
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics,
University of Virginia, Charlottesville, VA 22901, USA
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21
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Zouhir S, Contreras-Martel C, Maragno Trindade D, Attrée I, Dessen A, Macheboeuf P. MagC is a NplC/P60-like member of the α-2-macroglobulin Mag complex of Pseudomonas aeruginosa that interacts with peptidoglycan. FEBS Lett 2021; 595:2034-2046. [PMID: 34115884 DOI: 10.1002/1873-3468.14148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/17/2021] [Accepted: 06/08/2021] [Indexed: 11/07/2022]
Abstract
Bacterial α-2 macroglobulins (A2Ms) structurally resemble the large spectrum protease inhibitors of the eukaryotic immune system. In Pseudomonas aeruginosa, MagD acts as an A2M and is expressed within a six-gene operon encoding the MagA-F proteins. In this work, we employ isothermal calorimetry (ITC), analytical ultracentrifugation (AUC), and X-ray crystallography to investigate the function of MagC and show that MagC associates with the macroglobulin complex and with the peptidoglycan (PG). However, the catalytic residues of MagC display an inactive conformation that could suggest that it binds to PG but does not degrade it. We hypothesize that MagC could serve as an anchor between the MagD macroglobulin and the PG and could provide stabilization and/or regulation for the entire complex.
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Affiliation(s)
- Samira Zouhir
- Brazilian Biosciences National Laboratory (LNBio), CNPEM, Campinas, Brazil
| | | | | | - Ina Attrée
- Unité de Biologie Cellulaire et Infection, CEA, INSERM, CNRS, Université Grenoble Alpes, France
| | - Andréa Dessen
- Brazilian Biosciences National Laboratory (LNBio), CNPEM, Campinas, Brazil.,CNRS, CEA, IBS, Université Grenoble Alpes, France
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22
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Hammel M, Tainer JA. X-ray scattering reveals disordered linkers and dynamic interfaces in complexes and mechanisms for DNA double-strand break repair impacting cell and cancer biology. Protein Sci 2021; 30:1735-1756. [PMID: 34056803 PMCID: PMC8376411 DOI: 10.1002/pro.4133] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 12/17/2022]
Abstract
Evolutionary selection ensures specificity and efficiency in dynamic metastable macromolecular machines that repair DNA damage without releasing toxic and mutagenic intermediates. Here we examine non‐homologous end joining (NHEJ) as the primary conserved DNA double‐strand break (DSB) repair process in human cells. NHEJ has exemplary key roles in networks determining the development, outcome of cancer treatments by DSB‐inducing agents, generation of antibody and T‐cell receptor diversity, and innate immune response for RNA viruses. We determine mechanistic insights into NHEJ structural biochemistry focusing upon advanced small angle X‐ray scattering (SAXS) results combined with X‐ray crystallography (MX) and cryo‐electron microscopy (cryo‐EM). SAXS coupled to atomic structures enables integrated structural biology for objective quantitative assessment of conformational ensembles and assemblies in solution, intra‐molecular distances, structural similarity, functional disorder, conformational switching, and flexibility. Importantly, NHEJ complexes in solution undergo larger allosteric transitions than seen in their cryo‐EM or MX structures. In the long‐range synaptic complex, X‐ray repair cross‐complementing 4 (XRCC4) plus XRCC4‐like‐factor (XLF) form a flexible bridge and linchpin for DNA ends bound to KU heterodimer (Ku70/80) and DNA‐PKcs (DNA‐dependent protein kinase catalytic subunit). Upon binding two DNA ends, auto‐phosphorylation opens DNA‐PKcs dimer licensing NHEJ via concerted conformational transformations of XLF‐XRCC4, XLF–Ku80, and LigIVBRCT–Ku70 interfaces. Integrated structures reveal multifunctional roles for disordered linkers and modular dynamic interfaces promoting DSB end processing and alignment into the short‐range complex for ligation by LigIV. Integrated findings define dynamic assemblies fundamental to designing separation‐of‐function mutants and allosteric inhibitors targeting conformational transitions in multifunctional complexes.
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Affiliation(s)
- Michal Hammel
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - John A Tainer
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA.,Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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23
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Abstract
Structural biology methods have delivered over 150 000 high-resolution structures of macromolecules, which have fundamentally altered our understanding of biology and our approach to developing new medicines. However, the description of molecular flexibility is instrinsically flawed and in almost all cases, regardless of the experimental method used for structure determination, there remains a strong overfitting bias during molecular model building and refinement. In the worst case this can lead to wholly incorrect structures and thus incorrect biological interpretations. Here, by reparametrizing the description of these complex structures in terms of bonds rather than atomic positions, and by modelling flexibility using a deterministic ensemble of structures, it is demonstrated that structures can be described using fewer parameters than in conventional refinement. The current implementation, applied to X-ray diffraction data, significantly reduces the extent of overfitting, allowing the experimental data to reveal more biological information in electron-density maps.
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Affiliation(s)
- Helen M. Ginn
- Division of Life Sciences, Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0DE, United Kingdom
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24
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Abstract
This essay, which was written to commemorate the 50th anniversary of the Protein Data Bank, opens with some comments about the intentions of the scientists who pressed for its establishment and the nature of services it provides. It includes a brief account of the events that resulted in the determination of the crystal structure of the large ribosomal subunit from Haloarcula marismortui. The magnitude of the challenge the first ribosome crystal structures posed for the PDB is commented upon, and in the description of subsequent developments in the ribosome structure field that follows, it is pointed out that cryo-EM has replaced X-ray crystallography as the method of choice for investigating ribosome structure.
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Affiliation(s)
- Peter B Moore
- Department of Chemistry, Yale University, New Haven, Connecticut, USA.
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25
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Schneider DK, Shi W, Andi B, Jakoncic J, Gao Y, Bhogadi DK, Myers SF, Martins B, Skinner JM, Aishima J, Qian K, Bernstein HJ, Lazo EO, Langdon T, Lara J, Shea-McCarthy G, Idir M, Huang L, Chubar O, Sweet RM, Berman LE, McSweeney S, Fuchs MR. FMX - the Frontier Microfocusing Macromolecular Crystallography Beamline at the National Synchrotron Light Source II. J Synchrotron Radiat 2021; 28:650-665. [PMID: 33650577 PMCID: PMC7941291 DOI: 10.1107/s1600577520016173] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 12/11/2020] [Indexed: 05/26/2023]
Abstract
Two new macromolecular crystallography (MX) beamlines at the National Synchrotron Light Source II, FMX and AMX, opened for general user operation in February 2017 [Schneider et al. (2013). J. Phys. Conf. Ser. 425, 012003; Fuchs et al. (2014). J. Phys. Conf. Ser. 493, 012021; Fuchs et al. (2016). AIP Conf. Proc. SRI2015, 1741, 030006]. FMX, the micro-focusing Frontier MX beamline in sector 17-ID-2 at NSLS-II, covers a 5-30 keV photon energy range and delivers a flux of 4.0 × 1012 photons s-1 at 1 Å into a 1 µm × 1.5 µm to 10 µm × 10 µm (V × H) variable focus, expected to reach 5 × 1012 photons s-1 at final storage-ring current. This flux density surpasses most MX beamlines by nearly two orders of magnitude. The high brightness and microbeam capability of FMX are focused on solving difficult crystallographic challenges. The beamline's flexible design supports a wide range of structure determination methods - serial crystallography on micrometre-sized crystals, raster optimization of diffraction from inhomogeneous crystals, high-resolution data collection from large-unit-cell crystals, room-temperature data collection for crystals that are difficult to freeze and for studying conformational dynamics, and fully automated data collection for sample-screening and ligand-binding studies. FMX's high dose rate reduces data collection times for applications like serial crystallography to minutes rather than hours. With associated sample lifetimes as short as a few milliseconds, new rapid sample-delivery methods have been implemented, such as an ultra-high-speed high-precision piezo scanner goniometer [Gao et al. (2018). J. Synchrotron Rad. 25, 1362-1370], new microcrystal-optimized micromesh well sample holders [Guo et al. (2018). IUCrJ, 5, 238-246] and highly viscous media injectors [Weierstall et al. (2014). Nat. Commun. 5, 3309]. The new beamline pushes the frontier of synchrotron crystallography and enables users to determine structures from difficult-to-crystallize targets like membrane proteins, using previously intractable crystals of a few micrometres in size, and to obtain quality structures from irregular larger crystals.
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Affiliation(s)
| | - Wuxian Shi
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Babak Andi
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jean Jakoncic
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Yuan Gao
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | | | - Stuart F. Myers
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Bruno Martins
- Facility for Rare Isotope Beams, Michigan State University, East Lansing, MI 48824, USA
| | - John M. Skinner
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jun Aishima
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Kun Qian
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Herbert J. Bernstein
- Ronin Institute for Independent Scholarship, c/o NSLS-II, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Edwin O. Lazo
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Thomas Langdon
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - John Lara
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | | | - Mourad Idir
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Lei Huang
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Oleg Chubar
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Robert M. Sweet
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Lonny E. Berman
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Sean McSweeney
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Martin R. Fuchs
- Photon Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA
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26
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Riley BT, Wankowicz SA, de Oliveira SHP, van Zundert GCP, Hogan DW, Fraser JS, Keedy DA, van den Bedem H. qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps. Protein Sci 2021; 30:270-285. [PMID: 33210433 PMCID: PMC7737783 DOI: 10.1002/pro.4001] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 01/04/2023]
Abstract
New X-ray crystallography and cryo-electron microscopy (cryo-EM) approaches yield vast amounts of structural data from dynamic proteins and their complexes. Modeling the full conformational ensemble can provide important biological insights, but identifying and modeling an internally consistent set of alternate conformations remains a formidable challenge. qFit efficiently automates this process by generating a parsimonious multiconformer model. We refactored qFit from a distributed application into software that runs efficiently on a small server, desktop, or laptop. We describe the new qFit 3 software and provide some examples. qFit 3 is open-source under the MIT license, and is available at https://github.com/ExcitedStates/qfit-3.0.
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Affiliation(s)
- Blake T. Riley
- Structural Biology InitiativeCUNY Advanced Science Research CenterNew YorkNew YorkUSA
| | - Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Biophysics Graduate ProgramUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | | | - Daniel W. Hogan
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - James S. Fraser
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Daniel A. Keedy
- Structural Biology InitiativeCUNY Advanced Science Research CenterNew YorkNew YorkUSA
- Department of Chemistry and BiochemistryCity College of New YorkNew YorkNew YorkUSA
- Ph.D. Programs in Biochemistry, Biology, and ChemistryThe Graduate Center, City University of New YorkNew YorkUSA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Atomwise, Inc.San FranciscoCaliforniaUSA
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27
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Mendez D, Bolotovsky R, Bhowmick A, Brewster AS, Kern J, Yano J, Holton JM, Sauter NK. Beyond integration: modeling every pixel to obtain better structure factors from stills. IUCrJ 2020; 7:1151-1167. [PMID: 33209326 PMCID: PMC7642780 DOI: 10.1107/s2052252520013007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/23/2020] [Indexed: 05/25/2023]
Abstract
Most crystallographic data processing methods use pixel integration. In serial femtosecond crystallography (SFX), the intricate interaction between the reciprocal lattice point and the Ewald sphere is integrated out by averaging symmetrically equivalent observations recorded across a large number (104-106) of exposures. Although sufficient for generating biological insights, this approach converges slowly, and using it to accurately measure anomalous differences has proved difficult. This report presents a novel approach for increasing the accuracy of structure factors obtained from SFX data. A physical model describing all observed pixels is defined to a degree of complexity such that it can decouple the various contributions to the pixel intensities. Model dependencies include lattice orientation, unit-cell dimensions, mosaic structure, incident photon spectra and structure factor amplitudes. Maximum likelihood estimation is used to optimize all model parameters. The application of prior knowledge that structure factor amplitudes are positive quantities is included in the form of a reparameterization. The method is tested using a synthesized SFX dataset of ytterbium(III) lysozyme, where each X-ray laser pulse energy is centered at 9034 eV. This energy is 100 eV above the Yb3+ L-III absorption edge, so the anomalous difference signal is stable at 10 electrons despite the inherent energy jitter of each femtosecond X-ray laser pulse. This work demonstrates that this approach allows the determination of anomalous structure factors with very high accuracy while requiring an order-of-magnitude fewer shots than conventional integration-based methods would require to achieve similar results.
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Affiliation(s)
- Derek Mendez
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert Bolotovsky
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Asmit Bhowmick
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aaron S. Brewster
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jan Kern
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Junko Yano
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - James M. Holton
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Department of Biochemistry and Biophysics, UC San Francisco, San Francisco, CA 94158, USA
| | - Nicholas K. Sauter
- Molecular Biophysics and Integrated Bioimaging Division (MBIB), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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28
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Abstract
Crystallographic resolution is a key characteristic of diffraction data and represents one of the first decisions an experimenter has to make in data evaluation. Conservative approaches to the high-resolution cutoff determination are based on a number of criteria applied to the processed X-ray diffraction data only. However, high-resolution data that are weaker than arbitrary cutoffs can still result in the improvement of electron-density maps and refined structure models. Therefore, the impact of reflections from resolution shells higher than those previously used in conservative structure refinement should be analysed by the paired refinement protocol. For this purpose, a tool called PAIREF was developed to provide automation of this protocol. As a new feature, a complete cross-validation procedure has also been implemented. Here, the design, usage and control of the program are described, and its application is demonstrated on six data sets. The results prove that the inclusion of high-resolution data beyond the conventional criteria can lead to more accurate structure models.
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Affiliation(s)
- Martin Malý
- Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 11519, Czech Republic
- Institute of Biotechnology of the Czech Academy of Sciences, Biocev, Průmyslová 595, Vestec 25250, Czech Republic
| | - Kay Diederichs
- University of Konstanz, Box M647, Konstanz 78457, Germany
| | - Jan Dohnálek
- Institute of Biotechnology of the Czech Academy of Sciences, Biocev, Průmyslová 595, Vestec 25250, Czech Republic
| | - Petr Kolenko
- Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 11519, Czech Republic
- Institute of Biotechnology of the Czech Academy of Sciences, Biocev, Průmyslová 595, Vestec 25250, Czech Republic
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29
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Assmann GM, Wang M, Diederichs K. Making a difference in multi-data-set crystallography: simple and deterministic data-scaling/selection methods. Acta Crystallogr D Struct Biol 2020; 76:636-652. [PMID: 32627737 PMCID: PMC7336379 DOI: 10.1107/s2059798320006348] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/11/2020] [Indexed: 12/20/2022] Open
Abstract
Phasing by single-wavelength anomalous diffraction (SAD) from multiple crystallographic data sets can be particularly demanding because of the weak anomalous signal and possible non-isomorphism. The identification and exclusion of non-isomorphous data sets by suitable indicators is therefore indispensable. Here, simple and robust data-selection methods are described. A multi-dimensional scaling procedure is first used to identify data sets with large non-isomorphism relative to clusters of other data sets. Within each cluster that it identifies, further selection is based on the weighted ΔCC1/2, a quantity representing the influence of a set of reflections on the overall CC1/2 of the merged data. The anomalous signal is further improved by optimizing the scaling protocol. The success of iterating the selection and scaling steps was verified by substructure determination and subsequent structure solution. Three serial synchrotron crystallography (SSX) SAD test cases with hundreds of partial data sets and one test case with 62 complete data sets were analyzed. Structure solution was dramatically simplified with this procedure, and enabled solution of the structures after a few selection/scaling iterations. To explore the limits, the procedure was tested with much fewer data than originally required and could still solve the structure in several cases. In addition, an SSX data challenge, minimizing the number of (simulated) data sets necessary to solve the structure, was significantly underbid.
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Affiliation(s)
- Greta M. Assmann
- Department of Biology, University of Konstanz, Box 647, D-78457 Konstanz, Germany
| | - Meitian Wang
- Swiss Light Source, Paul Scherrer Institute, CH-5232 Villigen, Switzerland
| | - Kay Diederichs
- Department of Biology, University of Konstanz, Box 647, D-78457 Konstanz, Germany
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30
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Affiliation(s)
- Andrea Thorn
- Rudolf-Virchow-Zentrum - Center for Integrative and Translational Bioimaging, Universität Würzburg, Würzburg, Deutschland
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31
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Sauter NK, Kern J, Yano J, Holton JM. Towards the spatial resolution of metalloprotein charge states by detailed modeling of XFEL crystallographic diffraction. Acta Crystallogr D Struct Biol 2020; 76:176-192. [PMID: 32038048 PMCID: PMC7008510 DOI: 10.1107/s2059798320000418] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 01/14/2020] [Indexed: 12/25/2022] Open
Abstract
Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2-3 × 10-4 (ΔE/E). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (ΔE/E = 3 × 10-3) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction.
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Affiliation(s)
- Nicholas K. Sauter
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jan Kern
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Junko Yano
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - James M. Holton
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA
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32
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Hatti KS, McCoy AJ, Oeffner RD, Sammito MD, Read RJ. Factors influencing estimates of coordinate error for molecular replacement. Acta Crystallogr D Struct Biol 2020; 76:19-27. [PMID: 31909740 PMCID: PMC6939440 DOI: 10.1107/s2059798319015730] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 11/21/2019] [Indexed: 11/24/2022] Open
Abstract
Good prior estimates of the effective root-mean-square deviation (r.m.s.d.) between the atomic coordinates of the model and the target optimize the signal in molecular replacement, thereby increasing the success rate in difficult cases. Previous studies using protein structures solved by X-ray crystallography as models showed that optimal error estimates (refined after structure solution) were correlated with the sequence identity between the model and target, and with the number of residues in the model. Here, this work has been extended to find additional correlations between parameters of the model and the target and hence improved prior estimates of the coordinate error. Using a graph database, a curated set of 6030 molecular-replacement calculations using models that had been solved by X-ray crystallography was analysed to consider about 120 model and target parameters. Improved estimates were achieved by replacing the sequence identity with the Gonnet score for sequence similarity, as well as by considering the resolution of the target structure and the MolProbity score of the model. This approach was extended by analysing 12 610 additional molecular-replacement calculations where the model was determined by NMR. The median r.m.s.d. between pairs of models in an ensemble was found to be correlated with the estimated r.m.s.d. to the target. For models solved by NMR, the overall coordinate error estimates were larger than for structures determined by X-ray crystallography, and were more highly correlated with the number of residues.
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Affiliation(s)
- Kaushik S. Hatti
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, England
| | - Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, England
| | - Robert D. Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, England
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, England
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, England
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Leonarski F, Mozzanica A, Brückner M, Lopez-Cuenca C, Redford S, Sala L, Babic A, Billich H, Bunk O, Schmitt B, Wang M. JUNGFRAU detector for brighter x-ray sources: Solutions for IT and data science challenges in macromolecular crystallography. Struct Dyn 2020; 7:014305. [PMID: 32128347 PMCID: PMC7044001 DOI: 10.1063/1.5143480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 02/04/2020] [Indexed: 05/08/2023]
Abstract
In this paper, we present a data workflow developed to operate the adJUstiNg Gain detector FoR the Aramis User station (JUNGFRAU) adaptive gain charge integrating pixel-array detectors at macromolecular crystallography beamlines. We summarize current achievements for operating at 9 GB/s data-rate a JUNGFRAU with 4 Mpixel at 1.1 kHz frame-rate and preparations to operate at 46 GB/s data-rate a JUNGFRAU with 10 Mpixel at 2.2 kHz in the future. In this context, we highlight the challenges for computer architecture and how these challenges can be addressed with innovative hardware including IBM POWER9 servers and field-programmable gate arrays. We discuss also data science challenges, showing the effect of rounding and lossy compression schemes on the MX JUNGFRAU detector images.
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Affiliation(s)
- Filip Leonarski
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Aldo Mozzanica
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Martin Brückner
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Carlos Lopez-Cuenca
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Sophie Redford
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Leonardo Sala
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Andrej Babic
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | | | - Oliver Bunk
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Bernd Schmitt
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
| | - Meitian Wang
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
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34
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Naschberger A, Juyoux P, von Velsen J, Rupp B, Bowler MW. Controlled dehydration, structural flexibility and gadolinium MRI contrast compound binding in the human plasma glycoprotein afamin. Acta Crystallogr D Struct Biol 2019; 75:1071-1083. [PMID: 31793901 PMCID: PMC6889915 DOI: 10.1107/s2059798319013500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 10/02/2019] [Indexed: 01/29/2023] Open
Abstract
Afamin, which is a human blood plasma glycoprotein, a putative multifunctional transporter of hydrophobic molecules and a marker for metabolic syndrome, poses multiple challenges for crystallographic structure determination, both practically and in analysis of the models. Several hundred crystals were analysed, and an unusual variability in cell volume and difficulty in solving the structure despite an ∼34% sequence identity with nonglycosylated human serum albumin indicated that the molecule exhibits variable and context-sensitive packing, despite the simplified glycosylation in insect cell-expressed recombinant afamin. Controlled dehydration of the crystals was able to stabilize the orthorhombic crystal form, reducing the number of molecules in the asymmetric unit from the monoclinic form and changing the conformational state of the protein. An iterative strategy using fully automatic experiments available on MASSIF-1 was used to quickly determine the optimal protocol to achieve the phase transition, which should be readily applicable to many types of sample. The study also highlights the drawback of using a single crystallographic structure model for computational modelling purposes given that the conformational state of the binding sites and the electron density in the binding site, which is likely to result from PEGs, greatly varies between models. This also holds for the analysis of nonspecific low-affinity ligands, where often a variety of fragments with similar uncertainty can be modelled, inviting interpretative bias. As a promiscuous transporter, afamin also seems to bind gadoteridol, a magnetic resonance imaging contrast compound, in at least two sites. One pair of gadoteridol molecules is located near the human albumin Sudlow site, and a second gadoteridol molecule is located at an intermolecular site in proximity to domain IA. The data from the co-crystals support modern metrics of data quality in the context of the information that can be gleaned from data sets that would be abandoned on classical measures.
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Affiliation(s)
- Andreas Naschberger
- Department of Genetic Epidemiology, Medical University Innsbruck, Schöpfstrasse 41, A-6020 Innsbruck, Austria
| | - Pauline Juyoux
- Grenoble Outstation, European Molecular Biology Laboratory, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Jill von Velsen
- Grenoble Outstation, European Molecular Biology Laboratory, 71 Avenue des Martyrs, 38042 Grenoble, France
| | - Bernhard Rupp
- Department of Genetic Epidemiology, Medical University Innsbruck, Schöpfstrasse 41, A-6020 Innsbruck, Austria
- C.V.M.O., k. k. Hofkristallamt, 991 Audrey Place, Vista, California, USA
| | - Matthew W. Bowler
- Grenoble Outstation, European Molecular Biology Laboratory, 71 Avenue des Martyrs, 38042 Grenoble, France
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35
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Marchenkova MA, Kuranova IP, Timofeev VI, Boikova AS, Dorovatovskii PV, Dyakova YA, Ilina KB, Pisarevskiy YV, Kovalchuk MV. The binding of precipitant ions in the tetragonal crystals of hen egg white lysozyme. J Biomol Struct Dyn 2019; 38:5159-5172. [PMID: 31760865 DOI: 10.1080/07391102.2019.1696706] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The bonds between lysozyme molecules and precipitant ions in single crystals grown with chlorides of several metals are analysed on the basis of crystal structure data. Crystals of tetragonal hen egg lysozyme (HEWL) were grown with chlorides of several alkali and transition metals (LiCl, NaCl, KCl, NiCl2 and CuCl2) as precipitants and the three-dimensional structures were determined at 1.35 Å resolution by X-ray diffraction method. The positions of metal and chloride ions attached to the protein were located, divided into three groups and analysed. Some of them, in accordance with the recently proposed and experimentally confirmed crystal growth model, provide connections in protein dimers and octamers that are precursor clusters in the crystallization lysozyme solution. The first group, including Cu+2, Ni+2 and Na+1 cations, binds specifically to the protein molecule. The second group consists of metal and chloride ions bound inside the dimers and octamers. The third group of ions can participate in connections between the octamers that are suggested as building units during the crystal growth. The arrangement of chloride and metal ions associated with lysozyme molecule at all stages of the crystallization solution formation and crystal growth is discussed.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Margarita A Marchenkova
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Inna P Kuranova
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Vladimir I Timofeev
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Anastasiia S Boikova
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | | | - Yulia A Dyakova
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Kseniia B Ilina
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Yury V Pisarevskiy
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation
| | - Mikhail V Kovalchuk
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation.,National Research Centre "Kurchatov Institute", Moscow, Russian Federation.,The Faculty of Physics, St. Petersburg State University, St. Petersburg, Russian Federation
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36
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Wych DC, Fraser JS, Mobley DL, Wall ME. Liquid-like and rigid-body motions in molecular-dynamics simulations of a crystalline protein. Struct Dyn 2019; 6:064704. [PMID: 31867408 PMCID: PMC6920053 DOI: 10.1063/1.5132692] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 11/19/2019] [Indexed: 05/05/2023]
Abstract
To gain insight into crystalline protein dynamics, we performed molecular-dynamics (MD) simulations of a periodic 2 × 2 × 2 supercell of staphylococcal nuclease. We used the resulting MD trajectories to simulate X-ray diffraction and to study collective motions. The agreement of simulated X-ray diffraction with the data is comparable to previous MD simulation studies. We studied collective motions by analyzing statistically the covariance of alpha-carbon position displacements. The covariance decreases exponentially with the distance between atoms, which is consistent with a liquidlike motions (LLM) model, in which the protein behaves like a soft material. To gain finer insight into the collective motions, we examined the covariance behavior within a protein molecule (intraprotein) and between different protein molecules (interprotein). The interprotein atom pairs, which dominate the overall statistics, exhibit LLM behavior; however, the intraprotein pairs exhibit behavior that is consistent with a superposition of LLM and rigid-body motions (RBM). Our results indicate that LLM behavior of global dynamics is present in MD simulations of a protein crystal. They also show that RBM behavior is detectable in the simulations but that it is subsumed by the LLM behavior. Finally, the results provide clues about how correlated motions of atom pairs both within and across proteins might manifest in diffraction data. Overall, our findings increase our understanding of the connection between molecular motions and diffraction data and therefore advance efforts to extract information about functionally important motions from crystallography experiments.
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Affiliation(s)
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, USA
| | | | - Michael E. Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Author to whom correspondence should be addressed:
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37
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Atakisi H, Conger L, Moreau DW, Thorne RE. Resolution and dose dependence of radiation damage in biomolecular systems. IUCrJ 2019; 6:1040-1053. [PMID: 31709060 PMCID: PMC6830208 DOI: 10.1107/s2052252519008777] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 06/19/2019] [Indexed: 05/30/2023]
Abstract
The local Fourier-space relation between diffracted intensity I, diffraction wavevector q and dose D, , is key to probing and understanding radiation damage by X-rays and energetic particles in both diffraction and imaging experiments. The models used in protein crystallography for the last 50 years provide good fits to experimental I(q) versus nominal dose data, but have unclear physical significance. More recently, a fit to diffraction and imaging experiments suggested that the maximum tolerable dose varies as q -1 or linearly with resolution. Here, it is shown that crystallographic data have been strongly perturbed by the effects of spatially nonuniform crystal irradiation and diffraction during data collection. Reanalysis shows that these data are consistent with a purely exponential local dose dependence, = I 0(q)exp[-D/D e(q)], where D e(q) ∝ q α with α ≃ 1.7. A physics-based model for radiation damage, in which damage events occurring at random locations within a sample each cause energy deposition and blurring of the electron density within a small volume, predicts this exponential variation with dose for all q values and a decay exponent α ≃ 2 in two and three dimensions, roughly consistent with both diffraction and imaging experiments over more than two orders of magnitude in resolution. The B-factor model used to account for radiation damage in crystallographic scaling programs is consistent with α = 2, but may not accurately capture the dose dependencies of structure factors under typical nonuniform illumination conditions. The strong q dependence of radiation-induced diffraction decays implies that the previously proposed 20-30 MGy dose limit for protein crystallography should be replaced by a resolution-dependent dose limit that, for atomic resolution data sets, will be much smaller. The results suggest that the physics underlying basic experimental trends in radiation damage at T ≃ 100 K is straightforward and universal. Deviations of the local I(q, D) from strictly exponential behavior may provide mechanistic insights, especially into the radiation-damage processes responsible for the greatly increased radiation sensitivity observed at T ≃ 300 K.
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Affiliation(s)
- Hakan Atakisi
- Physics Department, Cornell University, Ithaca, NY 14853, USA
| | | | - David W. Moreau
- Physics Department, Cornell University, Ithaca, NY 14853, USA
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38
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Affiliation(s)
- Alexandre G. Urzhumtsev
- Centre for Integrative Biology, IGBMC, CNRS–INSERM–UdS, Illkirch, France
- Département de Physique, Faculté des Sciences et des Technologies, Université de Lorraine, Vandoeuvre-lès-Nancy, France
| | - Vladimir Y. Lunin
- Institute of Mathematical Problems of Biology RAS, Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, Russia
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39
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Brosey CA, Tainer JA. Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology. Curr Opin Struct Biol 2019; 58:197-213. [PMID: 31204190 PMCID: PMC6778498 DOI: 10.1016/j.sbi.2019.04.004] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 04/10/2019] [Accepted: 04/15/2019] [Indexed: 11/27/2022]
Abstract
Small-angle X-ray scattering (SAXS) has emerged as an enabling
integrative technique for comprehensive analyses of macromolecular structures
and interactions in solution. Over the past two decades, SAXS has become a
mainstay of the structural biologist’s toolbox, supplying multiplexed
measurements of molecular shape and dynamics that unveil biological function.
Here, we discuss evolving SAXS theory, methods, and applications that extend the
field of small-angle scattering beyond simple shape characterization. SAXS,
coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved
(TR-SAXS) methods, is now providing high-resolution insight into macromolecular
flexibility and ensembles, delineating biophysical landscapes, and facilitating
high-throughput library screening to assess macromolecular properties and to
create opportunities for drug discovery. Looking forward, we consider SAXS in
the integrative era of hybrid structural biology methods, its potential for
illuminating cellular supramolecular and mesoscale structures, and its capacity
to complement high-throughput bioinformatics sequencing data. As advances in the
field continue, we look forward to proliferating uses of SAXS based upon its
abilities to robustly produce mechanistic insights for biology and medicine.
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Affiliation(s)
- Chris A Brosey
- Molecular and Cellular Oncology and Cancer Biology, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - John A Tainer
- Molecular and Cellular Oncology and Cancer Biology, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA; MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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40
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41
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Abstract
A synthetic data set demonstrating a particularly challenging case of indexing ambiguity in the context of radiation damage was generated. This set shall serve as a standard benchmark and reference point for the ongoing development of new methods and new approaches to robust structure solution when single-crystal methods are insufficient. Of the 100 short wedges of data, only the first 36 are currently necessary to solve the structure by `cheating', or using the correct reference structure as a guide. The total wall-clock time and number of crystals required to solve the structure without cheating is proposed as a metric for the efficacy and efficiency of a given multi-crystal automation pipeline.
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Affiliation(s)
- James M. Holton
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158-2330, USA
- Divison of Molecular Biophysics and Bioengineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
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42
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Tang HYH, Shin DS, Hura GL, Yang Y, Hu X, Lightstone FC, McGee MD, Padgett HS, Yannone SM, Tainer JA. Structural Control of Nonnative Ligand Binding in Engineered Mutants of Phosphoenolpyruvate Carboxykinase. Biochemistry 2018; 57:6688-6700. [PMID: 30376300 DOI: 10.1021/acs.biochem.8b00963] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein engineering to alter recognition underlying ligand binding and activity has enormous potential. Here, ligand binding for Escherichia coli phosphoenolpyruvate carboxykinase (PEPCK), which converts oxaloacetate into CO2 and phosphoenolpyruvate as the first committed step in gluconeogenesis, was engineered to accommodate alternative ligands as an exemplary system with structural information. From our identification of bicarbonate binding in the PEPCK active site at the supposed CO2 binding site, we probed binding of nonnative ligands with three oxygen atoms arranged to resemble the bicarbonate geometry. Crystal structures of PEPCK and point mutants with bound nonnative ligands thiosulfate and methanesulfonate along with strained ATP and reoriented oxaloacetate intermediates and unexpected bicarbonate were determined and analyzed. The mutations successfully altered the bound ligand position and orientation and its specificity: mutated PEPCKs bound either thiosulfate or methanesulfonate but never both. Computational calculations predicted a methanesulfonate binding mutant and revealed that release of the active site ordered solvent exerts a strong influence on ligand binding. Besides nonnative ligand binding, one mutant altered the Mn2+ coordination sphere: instead of the canonical octahedral ligand arrangement, the mutant in question had an only five-coordinate arrangement. From this work, critical features of ligand binding, position, and metal ion cofactor geometry required for all downstream events can be engineered with small numbers of mutations to provide insights into fundamental underpinnings of protein-ligand recognition. Through structural and computational knowledge, the combination of designed and random mutations aids in the robust design of predetermined changes to ligand binding and activity to engineer protein function.
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Affiliation(s)
- Henry Y H Tang
- Molecular Biophysics and Integrated Bioimaging Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States.,Department of Chemistry , University of California , Berkeley , California 94720 , United States
| | - David S Shin
- Molecular Biophysics and Integrated Bioimaging Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States
| | - Greg L Hura
- Molecular Biophysics and Integrated Bioimaging Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States.,Department of Biochemistry and Chemistry , University of California , Santa Cruz , California 95064 , United States
| | - Yue Yang
- Biosciences and Biotechnology Division , Lawrence Livermore National Laboratory , Livermore , California 94550 , United States
| | - Xiaoyu Hu
- Department of Chemical Engineering , Tsinghua University , Beijing 100084 , China
| | - Felice C Lightstone
- Biosciences and Biotechnology Division , Lawrence Livermore National Laboratory , Livermore , California 94550 , United States
| | - Matthew D McGee
- Novici Biotech LLC , Vacaville , California 95688 , United States
| | - Hal S Padgett
- Novici Biotech LLC , Vacaville , California 95688 , United States
| | - Steven M Yannone
- Molecular Biophysics and Integrated Bioimaging Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States
| | - John A Tainer
- Molecular Biophysics and Integrated Bioimaging Division , Lawrence Berkeley National Laboratory , Berkeley , California 94720 , United States.,Department of Molecular and Cellular Oncology , The University of Texas M. D. Anderson Cancer Center , Houston , Texas 77030 , United States
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43
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Abstract
X-ray crystallography is experiencing a renaissance as a method for probing the protein conformational ensemble. The inherent limitations of Bragg analysis, however, which only reveals the mean structure, have given way to a surge in interest in diffuse scattering, which is caused by structure variations. Diffuse scattering is present in all macromolecular crystallography experiments. Recent studies are shedding light on the origins of diffuse scattering in protein crystallography, and provide clues for leveraging diffuse scattering to model protein motions with atomic detail.
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Affiliation(s)
- Michael E Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Alexander M Wolff
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA.
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44
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Ogorzalek TL, Hura GL, Belsom A, Burnett KH, Kryshtafovych A, Tainer JA, Rappsilber J, Tsutakawa SE, Fidelis K. Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy. Proteins 2018; 86 Suppl 1:202-214. [PMID: 29314274 DOI: 10.1002/prot.25452] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 12/18/2017] [Accepted: 01/01/2018] [Indexed: 12/13/2022]
Abstract
Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution.
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Affiliation(s)
- Tadeusz L Ogorzalek
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Greg L Hura
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Adam Belsom
- Wellcome Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, U.K
| | - Kathryn H Burnett
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, CA, 95616, USA
| | - John A Tainer
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA.,Department of Molecular and Cellular Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Juri Rappsilber
- Wellcome Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3BF, U.K.,Chair of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Susan E Tsutakawa
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Krzysztof Fidelis
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, CA, 95616, USA
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45
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Affiliation(s)
| | - Diana Fusco
- Department of Physics, University of California, Berkeley, California 94720, United States
| | - Pavel V. Afonine
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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46
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Tsai CL, Tainer JA. Robust Production, Crystallization, Structure Determination, and Analysis of [Fe-S] Proteins: Uncovering Control of Electron Shuttling and Gating in the Respiratory Metabolism of Molybdopterin Guanine Dinucleotide Enzymes. Methods Enzymol 2017; 599:157-196. [PMID: 29746239 DOI: 10.1016/bs.mie.2017.11.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
[Fe-S] clusters are essential cofactors in all domains of life. They play many biological roles due to their unique abilities for electron transfer and conformational control. Yet, producing and analyzing Fe-S proteins can be difficult and even misleading if not done anaerobically. Due to unique redox properties of [Fe-S] clusters and their oxygen sensitivity, they pose multiple challenges and can lose enzymatic activity or cause their component proteins to be structurally disordered due to [Fe-S] cluster oxidation and loss in air. Here we highlight tested protocols and strategies enabling efficient and stable [Fe-S] protein production, purification, crystallization, X-ray diffraction data collection, and structure determination. From multiple high-resolution anaerobic crystal structures, we furthermore analyze exemplary data defining [Fe-S] clusters, substrate entry, and product exit for the functional oxidation states of type II molybdo-bis(molybdopterin guanine dinucleotide) (Mo-bisMGD) enzymes. Notably, these enzymes perform electron shuttling between quinone pools and specific substrates to catalyze respiratory metabolism. The identified structure-activity relationships for this enzyme class have broad implications germane to perchlorate environments on Earth and Mars extending to an alternative mechanism underlying metabolic origins for the evolution of the oxygen atmosphere. Integrated structural analyses of type II Mo-bisMGD enzymes unveil novel distinctive shared molecular mechanisms for dynamic control of substrate entry and product release gated by hydrophobic residues. Collective findings support a prototypic model for type II Mo-bisMGD enzymes including insights for a fundamental molecular mechanistic understanding of selectivity and regulation by a conformationally gated channel with general implications for [Fe-S] cluster respiratory enzymes.
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Affiliation(s)
- Chi-Lin Tsai
- The University of Texas M. D. Anderson Cancer Center, Houston, TX, United States
| | - John A Tainer
- The University of Texas M. D. Anderson Cancer Center, Houston, TX, United States; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
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47
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Wang J, Brudvig GW, Batista VS, Moore PB. On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets. Protein Sci 2017; 26:2410-2416. [DOI: 10.1002/pro.3314] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 09/26/2017] [Accepted: 09/27/2017] [Indexed: 02/03/2023]
Affiliation(s)
- Jimin Wang
- Department of Molecular Biophysics and BiochemistryYale UniversityNew Haven Connecticut06520‐8114
| | - Gary W. Brudvig
- Department of Molecular Biophysics and BiochemistryYale UniversityNew Haven Connecticut06520‐8114
- Department of ChemistryYale UniversityNew Haven Connecticut06520‐8107
| | - Victor S. Batista
- Department of ChemistryYale UniversityNew Haven Connecticut06520‐8107
| | - Peter B. Moore
- Department of Molecular Biophysics and BiochemistryYale UniversityNew Haven Connecticut06520‐8114
- Department of ChemistryYale UniversityNew Haven Connecticut06520‐8107
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48
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Warkentin MA, Atakisi H, Hopkins JB, Walko D, Thorne RE. Lifetimes and spatio-temporal response of protein crystals in intense X-ray microbeams. IUCrJ 2017; 4:785-794. [PMID: 29123681 PMCID: PMC5668864 DOI: 10.1107/s2052252517013495] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 09/20/2017] [Indexed: 05/22/2023]
Abstract
Serial synchrotron-based crystallography using intense microfocused X-ray beams, fast-framing detectors and protein microcrystals held at 300 K promises to expand the range of accessible structural targets and to increase overall structure-pipeline throughputs. To explore the nature and consequences of X-ray radiation damage under microbeam illumination, the time-, dose- and temperature-dependent evolution of crystal diffraction have been measured with maximum dose rates of 50 MGy s-1. At all temperatures and dose rates, the integrated diffraction intensity for a fixed crystal orientation shows non-exponential decays with dose. Non-exponential decays are a consequence of non-uniform illumination and the resulting spatial evolution of diffracted intensity within the illuminated crystal volume. To quantify radiation-damage lifetimes and the damage state of diffracting crystal regions, a revised diffraction-weighted dose (DWD) is defined and it is shown that for Gaussian beams the DWD becomes nearly independent of actual dose at large doses. An apparent delayed onset of radiation damage seen in some intensity-dose curves is in fact a consequence of damage. Intensity fluctuations at high dose rates may arise from the impulsive release of gaseous damage products. Accounting for these effects, data collection at the highest dose rates increases crystal radiation lifetimes near 300 K (but not at 100 K) by a factor of ∼1.5-2 compared with those observed at conventional dose rates. Improved quantification and modeling of the complex spatio-temporal evolution of protein microcrystal diffraction in intense microbeams will enable more efficient data collection, and will be essential in improving the accuracy of structure factors and structural models.
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Affiliation(s)
- Matthew A. Warkentin
- Physics Department, Cornell University, Clark Hall, Ithaca, NY 14853, USA
- Rubota Corporation, 1260 NW Naito Parkway #609, Portland, OR 97209, USA
| | - Hakan Atakisi
- Physics Department, Cornell University, Ithaca, NY 14853, USA
| | | | - Donald Walko
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
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49
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Pearce NM, Krojer T, Bradley AR, Collins P, Nowak RP, Talon R, Marsden BD, Kelm S, Shi J, Deane CM, von Delft F. A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density. Nat Commun 2017; 8:15123. [PMID: 28436492 DOI: 10.1038/ncomms15123] [Citation(s) in RCA: 148] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/03/2017] [Indexed: 12/02/2022] Open
Abstract
In macromolecular crystallography, the rigorous detection of changed states (for example, ligand binding) is difficult unless signal is strong. Ambiguous (‘weak' or ‘noisy') density is experimentally common, since molecular states are generally only fractionally present in the crystal. Existing methodologies focus on generating maximally accurate maps whereby minor states become discernible; in practice, such map interpretation is disappointingly subjective, time-consuming and methodologically unsound. Here we report the PanDDA method, which automatically reveals clear electron density for the changed state—even from inaccurate maps—by subtracting a proportion of the confounding ‘ground state'; changed states are objectively identified from statistical analysis of density distributions. The method is completely general, implying new best practice for all changed-state studies, including the routine collection of multiple ground-state crystals. More generally, these results demonstrate: the incompleteness of atomic models; that single data sets contain insufficient information to model them fully; and that accuracy requires further map-deconvolution approaches. Building a ligand into a weak region of an electron density map of a protein is a subjective process. Here, the authors present a new method to obtain a clear electron density for a bound ligand based on multi-crystal experiments and 3D background correction.
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50
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Naschberger A, Fürnrohr BG, Lenac Rovis T, Malic S, Scheffzek K, Dieplinger H, Rupp B. The N14 anti-afamin antibody Fab: a rare V L1 CDR glycosylation, crystallographic re-sequencing, molecular plasticity and conservative versus enthusiastic modelling. Acta Crystallogr D Struct Biol 2016; 72:1267-1280. [PMID: 27917827 PMCID: PMC5137224 DOI: 10.1107/s205979831601723x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 10/26/2016] [Indexed: 12/31/2022] Open
Abstract
The monoclonal antibody N14 is used as a detection antibody in ELISA kits for the human glycoprotein afamin, a member of the albumin family, which has recently gained interest in the capture and stabilization of Wnt signalling proteins, and for its role in metabolic syndrome and papillary thyroid carcinoma. As a rare occurrence, the N14 Fab is N-glycosylated at Asn26L at the onset of the VL1 antigen-binding loop, with the α-1-6 core fucosylated complex glycan facing out of the L1 complementarity-determining region. The crystal structures of two non-apparent (pseudo) isomorphous crystals of the N14 Fab were analyzed, which differ significantly in the elbow angles, thereby cautioning against the overinterpretation of domain movements upon antigen binding. In addition, the map quality at 1.9 Å resolution was sufficient to crystallographically re-sequence the variable VL and VH domains and to detect discrepancies in the hybridoma-derived sequence. Finally, a conservatively refined parsimonious model is presented and its statistics are compared with those from a less conservatively built model that has been modelled more enthusiastically. Improvements to the PDB validation reports affecting ligands, clashscore and buried surface calculations are suggested.
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Affiliation(s)
- Andreas Naschberger
- Division of Biological Chemistry, Medical University of Innsbruck, Innrain 80, 6020 Innsbruck, Austria
| | - Barbara G. Fürnrohr
- Division of Biological Chemistry, Medical University of Innsbruck, Innrain 80, 6020 Innsbruck, Austria
| | - Tihana Lenac Rovis
- Center for Proteomics, University of Rijeka, B. Branchetta 20, 51000 Rijeka, Croatia
| | - Suzana Malic
- Center for Proteomics, University of Rijeka, B. Branchetta 20, 51000 Rijeka, Croatia
| | - Klaus Scheffzek
- Division of Biological Chemistry, Medical University of Innsbruck, Innrain 80, 6020 Innsbruck, Austria
| | - Hans Dieplinger
- Division of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstrasse 41, 6020 Innsbruck, Austria
- Vitateq Biotechnology GmbH, Innrain 66, 6020 Innsbruck, Austria
| | - Bernhard Rupp
- Division of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstrasse 41, 6020 Innsbruck, Austria
- CVMO, k.-k. Hofkristallamt, 991 Audrey Place, Vista, CA 92084, USA
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