Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental ...conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low‐dimensional space, at a position given by their CC* values as defined by Karplus & Diederichs (2012), Science, 336, 1030–1033 in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data‐set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random‐error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal‐to‐noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.
A multidimensional scaling analysis of pairwise correlation coefficients is presented which positions data sets in a sphere with unit radius of an , low‐dimensional space at radii inversely proportional to their levels of random error and at spherical angles related to their mutual systematic differences. This reduction in dimensionality can not only be used for classification purposes, but also to derive data‐set relations on a continuous scale.
In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R merge values reporting on the agreement between multiple ...measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R merge values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.
An indicator which is calculated after the data reduction of a test data set may be used to estimate the (systematic) instrument error at a macromolecular X‐ray source. The numerical value of the ...indicator is the highest signal‐to‐noise I/σ(I) value that the experimental setup can produce and its reciprocal is related to the lower limit of the merging R factor. In the context of this study, the stability of the experimental setup is influenced and characterized by the properties of the X‐ray beam, shutter, goniometer, cryostream and detector, and also by the exposure time and spindle speed. Typical values of the indicator are given for data sets from the JCSG archive. Some sources of error are explored with the help of test calculations using SIM_MX Diederichs (2009), Acta Cryst. D65, 535–542. One conclusion is that the accuracy of data at low resolution is usually limited by the experimental setup rather than by the crystal. It is also shown that the influence of vibrations and fluctuations may be mitigated by a reduction in spindle speed accompanied by stronger attenuation.
•Common data quality indicators grouped as primary, secondary, or wrong/misleading.•A gedanken experiment reveals shortcomings of some common indicators.•Reviews evidence that massive multiplicity ...improves data for phasing and refinement.•Cites examples showing value of extending data past conventional resolution cutoffs.•A derived relationship between CC1/2 and 〈I/σ〉mrgd makes CC1/2 more intuitive.
The quality of macromolecular crystal structures depends, in part, on the quality and quantity of the data used to produce them. Here, we review recent shifts in our understanding of how to use data quality indicators to select a high resolution cutoff that leads to the best model, and of the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. Key factors supporting this shift are the introduction of more robust correlation coefficient based indicators of the precision of merged data sets as well as the recognition of the substantial useful information present in extensive amounts of data once considered too weak to be of value.
In serial crystallography, a very incomplete partial data set is obtained from each diffraction experiment (a `snapshot'). In some space groups, an indexing ambiguity exists which requires that the ...indexing mode of each snapshot needs to be established with respect to a reference data set. In the absence of such re‐indexing information, crystallographers have thus far resorted to a straight merging of all snapshots, yielding a perfectly twinned data set of higher symmetry which is poorly suited for structure solution and refinement. Here, two algorithms have been designed for assembling complete data sets by clustering those snapshots that are indexed in the same way, and they have been tested using 15 445 snapshots from photosystem I Chapman et al. (2011), Nature (London), 470, 73–77 and with noisy model data. The results of the clustering are unambiguous and enabled the construction of complete data sets in the correct space group P63 instead of (twinned) P6322 that researchers have been forced to use previously in such cases of indexing ambiguity. The algorithms thus extend the applicability and reach of serial crystallography.
Recent developments in CrystFEL White, Thomas A.; Mariani, Valerio; Brehm, Wolfgang ...
Journal of applied crystallography,
April 2016, Letnik:
49, Številka:
2
Journal Article
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CrystFEL is a suite of programs for processing data from `serial crystallography' experiments, which are usually performed using X‐ray free‐electron lasers (FELs) but also increasingly with other ...X‐ray sources. The CrystFEL software suite has been under development since 2009, just before the first hard FEL experiments were performed, and has been significantly updated and improved since then. This article describes the most important improvements which have been made to CrystFEL since the first release version. These changes include the addition of new programs to the suite, the ability to resolve `indexing ambiguities' and several ways to improve the quality of the integrated data by more accurately modelling the underlying diffraction physics.
Developments in the CrystFEL software suite, for processing diffraction data from `serial crystallography' experiments, are described.
G-protein-coupled receptors (GPCRs) signal primarily through G proteins or arrestins. Arrestin binding to GPCRs blocks G protein interaction and redirects signalling to numerous G-protein-independent ...pathways. Here we report the crystal structure of a constitutively active form of human rhodopsin bound to a pre-activated form of the mouse visual arrestin, determined by serial femtosecond X-ray laser crystallography. Together with extensive biochemical and mutagenesis data, the structure reveals an overall architecture of the rhodopsin-arrestin assembly in which rhodopsin uses distinct structural elements, including transmembrane helix 7 and helix 8, to recruit arrestin. Correspondingly, arrestin adopts the pre-activated conformation, with a ∼20° rotation between the amino and carboxy domains, which opens up a cleft in arrestin to accommodate a short helix formed by the second intracellular loop of rhodopsin. This structure provides a basis for understanding GPCR-mediated arrestin-biased signalling and demonstrates the power of X-ray lasers for advancing the frontiers of structural biology.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Advances in beamline optics, detectors and X‐ray sources allow new techniques of crystallographic data collection. In serial crystallography, a large number of partial datasets from crystals of small ...volume are measured. Merging of datasets from different crystals in order to enhance data completeness and accuracy is only valid if the crystals are isomorphous, i.e. sufficiently similar in cell parameters, unit‐cell contents and molecular structure. Identification and exclusion of non‐isomorphous datasets is therefore indispensable and must be done by means of suitable indicators. To identify rogue datasets, the influence of each dataset on CC1/2 Karplus & Diederichs (2012). Science, 336, 1030–1033, the correlation coefficient between pairs of intensities averaged in two randomly assigned subsets of observations, is evaluated. The presented method employs a precise calculation of CC1/2 that avoids the random assignment, and instead of using an overall CC1/2, an average over resolution shells is employed to obtain sensible results. The selection procedure was verified by measuring the correlation of observed (merged) intensities and intensities calculated from a model. It is found that inclusion and merging of non‐isomorphous datasets may bias the refined model towards those datasets, and measures to reduce this effect are suggested.
In serial crystallography, CC1/2 may be used as an optimization target, and outlier datasets can be identified on the basis of their influence on the average CC1/2 of the merged data. This leads to the ΔCC1/2 method presented here.
The tumor suppressor protein fragile histidine triad (Fhit) is known to be associated with genomic instability and apoptosis. The tumor-suppressive function of Fhit depends on the interaction with ...the alarmone diadenosine triphosphate (Ap3A), a noncanonical nucleotide whose concentration increases upon cellular stress. How the Fhit–Ap3A complex exerts its signaling function is unknown. Here, guided by a chemical proteomics approach employing a synthetic stable Fhit–Ap3A complex, we found that the Fhit–Ap3A complex, but not Fhit or Ap3A alone, impedes translation. Our findings provide a mechanistic model in which Fhit translocates from the nucleolus into the cytosol upon stress to form an Fhit–Ap3A complex. The Fhit–Ap3A complex impedes translation both in vitro and in vivo, resulting in reduced cell viability. Overall, our findings provide a mechanistic model by which the tumor suppressor Fhit collaborates with the alarmone Ap3A to regulate cellular proliferation.
NADH oxidation in the respiratory chain is coupled to ion translocation across the membrane to build up an electrochemical gradient. The sodium-translocating NADH:quinone oxidoreductase (Na(+)-NQR), ...a membrane protein complex widespread among pathogenic bacteria, consists of six subunits, NqrA, B, C, D, E and F. To our knowledge, no structural information on the Na(+)-NQR complex has been available until now. Here we present the crystal structure of the Na(+)-NQR complex at 3.5 Å resolution. The arrangement of cofactors both at the cytoplasmic and the periplasmic side of the complex, together with a hitherto unknown iron centre in the midst of the membrane-embedded part, reveals an electron transfer pathway from the NADH-oxidizing cytoplasmic NqrF subunit across the membrane to the periplasmic NqrC, and back to the quinone reduction site on NqrA located in the cytoplasm. A sodium channel was localized in subunit NqrB, which represents the largest membrane subunit of the Na(+)-NQR and is structurally related to urea and ammonia transporters. On the basis of the structure we propose a mechanism of redox-driven Na(+) translocation where the change in redox state of the flavin mononucleotide cofactor in NqrB triggers the transport of Na(+) through the observed channel.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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