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Open Access
Abstract: DNA gyrase is a bacterial type IIA topoisomerase that can create temporary double-stranded DNA breaks to regulate DNA topology and an archetypical target of antibiotics. The widely used quinolone class of drugs use a water–metal ion bridge in interacting with the GyrA subunit of DNA gyrase. Zoliflodacin sits in the same pocket as quinolones but interacts with the GyrB subunit and also stabilizes lethal double-stranded DNA breaks. Gepotidacin has been observed to sit on the twofold axis of the complex, midway between the two four-base-pair separated DNA-cleavage sites and has been observed to stabilize singe-stranded DNA breaks. Here, we use information from three crystal structures of complexes of Staphlococcus aureus DNA gyrase (one with a precursor of gepotidacin and one with the progenitor of zoliflodacin) to propose a simple single moving metal-ion-catalyzed DNA-cleavage mechanism. Our model explains why the catalytic tyrosine is in the tyrosinate (negatively charged) form for DNA cleavage. Movement of a single catalytic metal-ion (Mg2+ or Mn2+) guides water-mediated protonation and cleavage of the scissile phosphate, which is then accepted by the catalytic tyrosinate. Type IIA topoisomerases need to be able to rapidly cut the DNA when it becomes positively supercoiled (in front of replication forks and transcription bubbles) and we propose that the original purpose of the small Greek Key domain, common to all type IIA topoisomerases, was to allow access of the catalytic metal to the DNA-cleavage site. Although the proposed mechanism is consistent with published data, it is not proven and other mechanisms have been proposed. Finally, how such mechanisms can be experimentally distinguished is considered.
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Dec 2024
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I23-Long wavelength MX
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Diamond Proposal Number(s):
[29990]
Open Access
Abstract: One of the challenges for experimental structural biology in the 21st century is to see chemical reactions happen. Staphylococcus aureus (S. aureus) DNA gyrase is a type IIA topoisomerase that can create temporary double-stranded DNA breaks to regulate DNA topology. Drugs, such as gepotidacin, zoliflodacin and the quinolone moxifloxacin, can stabilize these normally transient DNA strand breaks and kill bacteria. Crystal structures of uncleaved DNA with a gepotidacin precursor (2.1 Å GSK2999423) or with doubly cleaved DNA and zoliflodacin (or with its progenitor QPT-1) have been solved in the same P61 space-group (a = b ≈ 93 Å, c ≈ 412 Å). This suggests that it may be possible to observe the two DNA cleavage steps (and two DNA-religation steps) in this P61 space-group. Here, a 2.58 Å anomalous manganese dataset in this crystal form is solved, and four previous crystal structures (1.98 Å, 2.1 Å, 2.5 Å and 2.65 Å) in this crystal form are re-refined to clarify crystal contacts. The structures clearly suggest a single moving metal mechanism—presented in an accompanying (second) paper. A previously published 2.98 Å structure of a yeast topoisomerase II, which has static disorder around a crystallographic twofold axis, was published as containing two metals at one active site. Re-refined coordinates of this 2.98 Å yeast structure are consistent with other type IIA topoisomerase structures in only having one metal ion at each of the two different active sites.
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Nov 2024
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Catherine L.
Lawson
,
Andriy
Kryshtafovych
,
Grigore D.
Pintilie
,
Stephen
Burley
,
Jiří
Černý
,
Vincent B.
Chen
,
Paul
Emsley
,
Alberto
Gobbi
,
Andrzej
Joachimiak
,
Sigrid
Noreng
,
Michael G.
Prisant
,
Randy J.
Read
,
Jane S.
Richardson
,
Alexis L.
Rohou
,
Bohdan
Schneider
,
Benjamin D.
Sellers
,
Chenghua
Shao
,
Elizabeth
Sourial
,
Chris I.
Williams
,
Christopher J.
Williams
,
Ying
Yang
,
Venkat
Abbaraju
,
Pavel V.
Afonine
,
Matthew L.
Baker
,
Paul S.
Bond
,
Tom L.
Blundell
,
Tom
Burnley
,
Arthur
Campbell
,
Renzhi
Cao
,
Jianlin
Cheng
,
Grzegorz
Chojnowski
,
Kevin D.
Cowtan
,
Frank
Dimaio
,
Reza
Esmaeeli
,
Nabin
Giri
,
Helmut
Grubmüller
,
Soon Wen
Hoh
,
Jie
Hou
,
Corey F.
Hryc
,
Carola
Hunte
,
Maxim
Igaev
,
Agnel P.
Joseph
,
Wei-Chun
Kao
,
Daisuke
Kihara
,
Dilip
Kumar
,
Lijun
Lang
,
Sean
Lin
,
Sai R.
Maddhuri Venkata Subramaniya
,
Sumit
Mittal
,
Arup
Mondal
,
Nigel W.
Moriarty
,
Andrew
Muenks
,
Garib N.
Murshudov
,
Robert A.
Nicholls
,
Mateusz
Olek
,
Colin M.
Palmer
,
Alberto
Perez
,
Emmi
Pohjolainen
,
Karunakar R.
Pothula
,
Christopher N.
Rowley
,
Daipayan
Sarkar
,
Luisa U.
Schäfer
,
Christopher J.
Schlicksup
,
Gunnar F.
Schröder
,
Mrinal
Shekhar
,
Dong
Si
,
Abhishek
Singharoy
,
Oleg V.
Sobolev
,
Genki
Terashi
,
Andrea C.
Vaiana
,
Sundeep C.
Vedithi
,
Jacob
Verburgt
,
Xiao
Wang
,
Rangana
Warshamanage
,
Martyn
Winn
,
Simone
Weyand
,
Keitaro
Yamashita
,
Minglei
Zhao
,
Michael F.
Schmid
,
Helen M.
Berman
,
Wah
Chiu
Abstract: The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein–nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9–2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.
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Jun 2024
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I24-Microfocus Macromolecular Crystallography
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Rachel
Bolton
,
Moritz M.
Machelett
,
Jack
Stubbs
,
Danny
Axford
,
Nicolas
Caramello
,
Lucrezia
Catapano
,
Martin
Maly
,
Matthew J.
Rodrigues
,
Charlotte
Cordery
,
Graham J.
Tizzard
,
Fraser
Macmillan
,
Sylvain
Engilberge
,
David
Von Stetten
,
Takehiko
Tosha
,
Hiroshi
Sugimoto
,
Jonathan A. R.
Worrall
,
Jeremy S.
Webb
,
Mike
Zubkov
,
Simon
Coles
,
Eric
Mathieu
,
Roberto A.
Steiner
,
Garib
Murshudov
,
Tobias E.
Schrader
,
Allen M.
Orville
,
Antoine
Royant
,
Gwyndaf
Evans
,
Michael A.
Hough
,
Robin L.
Owen
,
Ivo
Tews
Diamond Proposal Number(s):
[15722, 14493, 23570]
Open Access
Abstract: The marine cyanobacterium Prochlorococcus is a main contributor to global photosynthesis, whilst being limited by iron availability. Cyanobacterial genomes generally encode two different types of FutA iron-binding proteins: periplasmic FutA2 ABC transporter subunits bind Fe(III), while cytosolic FutA1 binds Fe(II). Owing to their small size and their economized genome Prochlorococcus ecotypes typically possess a single futA gene. How the encoded FutA protein might bind different Fe oxidation states was previously unknown. Here, we use structural biology techniques at room temperature to probe the dynamic behavior of FutA. Neutron diffraction confirmed four negatively charged tyrosinates, that together with a neutral water molecule coordinate iron in trigonal bipyramidal geometry. Positioning of the positively charged Arg103 side chain in the second coordination shell yields an overall charge-neutral Fe(III) binding state in structures determined by neutron diffraction and serial femtosecond crystallography. Conventional rotation X-ray crystallography using a home source revealed X-ray-induced photoreduction of the iron center with observation of the Fe(II) binding state; here, an additional positioning of the Arg203 side chain in the second coordination shell maintained an overall charge neutral Fe(II) binding site. Dose series using serial synchrotron crystallography and an XFEL X-ray pump–probe approach capture the transition between Fe(III) and Fe(II) states, revealing how Arg203 operates as a switch to accommodate the different iron oxidation states. This switching ability of the Prochlorococcus FutA protein may reflect ecological adaptation by genome streamlining and loss of specialized FutA proteins.
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Mar 2024
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Jon
Agirre
,
Mihaela
Atanasova
,
Haroldas
Bagdonas
,
Charles B.
Ballard
,
Arnaud
Basle
,
James
Beilsten-Edmands
,
Rafael J.
Borges
,
David G.
Brown
,
J. Javier
Burgos-Marmol
,
John M.
Berrisford
,
Paul S.
Bond
,
Iracema
Caballero
,
Lucrezia
Catapano
,
Grzegorz
Chojnowski
,
Atlanta G.
Cook
,
Kevin D.
Cowtan
,
Tristan I.
Croll
,
Judit É.
Debreczeni
,
Nicholas E.
Devenish
,
Eleanor J.
Dodson
,
Tarik R.
Drevon
,
Paul
Emsley
,
Gwyndaf
Evans
,
Phil R.
Evans
,
Maria
Fando
,
James
Foadi
,
Luis
Fuentes-Montero
,
Elspeth F.
Garman
,
Markus
Gerstel
,
Richard J.
Gildea
,
Kaushik
Hatti
,
Maarten L.
Hekkelman
,
Philipp
Heuser
,
Soon Wen
Hoh
,
Michael A.
Hough
,
Huw T.
Jenkins
,
Elisabet
Jiménez
,
Robbie P.
Joosten
,
Ronan M.
Keegan
,
Nicholas
Keep
,
Eugene B.
Krissinel
,
Petr
Kolenko
,
Oleg
Kovalevskiy
,
Victor S.
Lamzin
,
David M.
Lawson
,
Andrey
Lebedev
,
Andrew G. W.
Leslie
,
Bernhard
Lohkamp
,
Fei
Long
,
Martin
Maly
,
Airlie
Mccoy
,
Stuart J.
Mcnicholas
,
Ana
Medina
,
Claudia
Millán
,
James W.
Murray
,
Garib N.
Murshudov
,
Robert A.
Nicholls
,
Martin E. M.
Noble
,
Robert
Oeffner
,
Navraj S.
Pannu
,
James M.
Parkhurst
,
Nicholas
Pearce
,
Joana
Pereira
,
Anastassis
Perrakis
,
Harold R.
Powell
,
Randy J.
Read
,
Daniel J.
Rigden
,
William
Rochira
,
Massimo
Sammito
,
Filomeno
Sanchez Rodriguez
,
George M.
Sheldrick
,
Kathryn L.
Shelley
,
Felix
Simkovic
,
Adam J.
Simpkin
,
Pavol
Skubak
,
Egor
Sobolev
,
Roberto A.
Steiner
,
Kyle
Stevenson
,
Ivo
Tews
,
Jens M. H.
Thomas
,
Andrea
Thorn
,
Josep Triviño
Valls
,
Ville
Uski
,
Isabel
Uson
,
Alexei
Vagin
,
Sameer
Velankar
,
Melanie
Vollmar
,
Helen
Walden
,
David
Waterman
,
Keith S.
Wilson
,
Martyn
Winn
,
Graeme
Winter
,
Marcin
Wojdyr
,
Keitaro
Yamashita
Open Access
Abstract: The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.
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Jun 2023
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I03-Macromolecular Crystallography
I04-1-Macromolecular Crystallography (fixed wavelength)
I04-Macromolecular Crystallography
I24-Microfocus Macromolecular Crystallography
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Open Access
Abstract: This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant.
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Mar 2020
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I02-Macromolecular Crystallography
I03-Macromolecular Crystallography
I04-Macromolecular Crystallography
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Diamond Proposal Number(s):
[302]
Open Access
Abstract: Twinning is a crystal growth anomaly, which has posed a challenge in macromolecular crystallography (MX) since the earliest days. Many approaches have been used to treat twinned data in order to extract structural information. However, in most cases it is usually simpler to rescreen for new crystallization conditions that yield an untwinned crystal form or, if possible, collect data from non-twinned parts of the crystal. Here, we report 11 structures of engineered variants of the E. coli enzyme N-acetyl-neuraminic lyase which, despite twinning and incommensurate modulation, have been successfully indexed, solved and deposited. These structures span a resolution range of 1.45–2.30 Å, which is unusually high for datasets presenting such lattice disorders in MX and therefore these data provide an excellent test set for improving and challenging MX data processing programs.
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Oct 2018
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Open Access
Abstract: An algorithm for modelling the background for each Bragg reflection in a series of X-ray diffraction images containing Debye–Scherrer diffraction from ice in the sample is presented. The method involves the use of a global background model which is generated from the complete X-ray diffraction data set. Fitting of this model to the background pixels is then performed for each reflection independently. The algorithm uses a static background model that does not vary over the course of the scan. The greatest improvement can be expected for data where ice rings are present throughout the data set and the local background shape at the size of a spot on the detector does not exhibit large time-dependent variation. However, the algorithm has been applied to data sets whose background showed large pixel variations (variance/mean > 2) and has been shown to improve the results of processing for these data sets. It is shown that the use of a simple flat-background model as in traditional integration programs causes systematic bias in the background determination at ice-ring resolutions, resulting in an overestimation of reflection intensities at the peaks of the ice rings and an underestimation of reflection intensities either side of the ice ring. The new global background-model algorithm presented here corrects for this bias, resulting in a noticeable improvement in R factors following refinement.
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Sep 2017
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Data acquisition
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Open Access
Abstract: In this paper, AUSPEX, a new software tool for experimental X-ray data analysis, is presented. Exploring the behaviour of diffraction intensities and the associated estimated uncertainties facilitates the discovery of underlying problems and can help users to improve their data acquisition and processing in order to obtain better structural models. The program enables users to inspect the distribution of observed intensities (or amplitudes) against resolution as well as the associated estimated uncertainties (sigmas). It is demonstrated how AUSPEX can be used to visually and automatically detect ice-ring artefacts in integrated X-ray diffraction data. Such artefacts can hamper structure determination, but may be difficult to identify from the raw diffraction images produced by modern pixel detectors. The analysis suggests that a significant portion of the data sets deposited in the PDB contain ice-ring artefacts. Furthermore, it is demonstrated how other problems in experimental X-ray data caused, for example, by scaling and data-conversion procedures can be detected by AUSPEX.
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Sep 2017
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Data acquisition
Detectors
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Abstract: In macromolecular crystallography, integration programs - such as DIALS (Waterman et al. 2013) - are used to estimate the
intensities of Bragg reflections recorded on a series of X-ray diffraction images. The reflection intensities are estimated using
the following procedure. A model for the shape of the reflection profile is estimated from a set of strong reflections. This
model is then applied to each reflection in order to estimate the size and shape of the reflection on the detector surface and
to label each pixel as either foreground or background. The intensity of each reflection is then estimated (in the case of
summation integration) by summing the total counts minus the estimated background counts in the foreground region.
Since the background level under the reflection peak cannot be measured directly, it is estimated from the surrounding
background pixels assuming a given model.
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Aug 2017
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