Identification of rogue datasets in serial crystallography

Aßmann, Greta Marie; Diederichs, Kay

doi:10.1107/s2053273315099726

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…After that, the algorithm attempts to scale all obtained XDS_ASCII. HKL files using XSCALE, and runs several rounds of ΔCC 1/2 rejection of non-isomorphous minisets using xdscc12 subprogram as described 17 (until there are no rejected minisets at the subsequent iteration). For most Table 6.…”

Section: Data Recordsmentioning

confidence: 99%

Small-wedge synchrotron and serial XFEL datasets for Cysteinyl leukotriene GPCRs

Marin¹,

Luginina²,

Gusach³

et al. 2020

Sci Data

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Structural studies of challenging targets such as G protein-coupled receptors (GPCRs) have accelerated during the last several years due to the development of new approaches, including small-wedge and serial crystallography. Here, we describe the deposition of seven datasets consisting of X-ray diffraction images acquired from lipidic cubic phase (LCP) grown microcrystals of two human GPCRs, Cysteinyl leukotriene receptors 1 and 2 (CysLT1R and CysLT2R), in complex with various antagonists. Five datasets were collected using small-wedge synchrotron crystallography (SWSX) at the European Synchrotron Radiation Facility with multiple crystals under cryo-conditions. Two datasets were collected using X-ray free electron laser (XFEL) serial femtosecond crystallography (SFX) at the Linac Coherent Light Source, with microcrystals delivered at room temperature into the beam within LCP matrix by a viscous media microextrusion injector. All seven datasets have been deposited in the open-access databases Zenodo and CXIDB. Here, we describe sample preparation and annotate crystallization conditions for each partial and full datasets. We also document full processing pipelines and provide wrapper scripts for SWSX and SFX data processing.A Correction to this paper has been published: https://doi.org/10.1038/s41597-020-00759-w

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Section: Data Recordsmentioning

confidence: 99%

Small-wedge synchrotron and serial XFEL datasets for Cysteinyl leukotriene GPCRs

Marin¹,

Luginina²,

Gusach³

et al. 2020

Sci Data

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“…The four data sets just discussed are similar in quality to many of the other XFEL data sets that have been deposited in the PDB. Diederichs and colleagues have expressed concern about the quality of some of them, and about the wisdom of using cumulative CC 1/2 values as measure of data set quality . It has been recommended that individual values at both low and high resolution bins should be reported, instead of just an overall value .…”

Section: Resultsmentioning

confidence: 99%

On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets

et al. 2017

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In 2012, Karplus and Diederichs demonstrated that the Pearson correlation coefficient CC is a far better indicator of the quality and resolution of crystallographic data sets than more traditional measures like merging R-factor or signal-to-noise ratio. More specifically, they proposed that CC be computed for data sets in thin shells of increasing resolution so that the resolution dependence of that quantity can be examined. Recently, however, the CC values of entire data sets, i.e., cumulative correlation coefficients, have been used as a measure of data quality. Here, we show that the difference in cumulative CC value between a data set that has been accurately measured and a data set that has not is likely to be small. Furthermore, structures obtained by molecular replacement from poorly measured data sets are likely to suffer from extreme model bias.

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“…Step 3: Mapping similarity with cc_analysis Previous use of cc_analysis (6,41,42) employed pairwise Pearson's correlation coefficients as quantifiers for similarity. Pearson's correlation coefficients have a value range from -1 to 1.…”

Section: 𝑓 Imentioning

confidence: 99%

Pairwise sequence similarity mapping with PaSiMap: reclassification of immunoglobulin domains from titin as case study

Mayans

Diederichs

et al. 2022

Preprint

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Sequence comparison is critical for the functional assignment of newly identified protein genes. As uncharacterised protein sequences accumulate, there is an increasing need for sensitive tools for their classification. Here, we present a novel multidimensional scaling pipeline, PaSiMap, which creates a map of pairwise sequence similarities. Uniquely, PaSiMap distinguishes between unique and shared features, allowing for a distinct view of protein-sequence relationships. We demonstrate PaSiMap’s efficiency in detecting sequence groups and outliers using titin’s 169 immunoglobulin (Ig) domains. We show that Ig domain similarity is hierarchical, being firstly determined by chain location, then by the loop features of the Ig fold and, finally, by super-repeat position. The existence of a previously unidentified domain repeat in the distal, constitutive I-band is revealed. Prototypic Igs, plus notable outliers, are identified and thereby domain classification improved. This re-classification can now guide future molecular research. In summary, we demonstrate that PaSiMap is a sensitive tool for the classification of protein sequences, which adds a new perspective in the understanding of inter-protein relationships. PaSiMap is applicable to any biological system defined by a linear sequence, including nucleotides.

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Identification of rogue datasets in serial crystallography

Cited by 5 publications

References 13 publications

Small-wedge synchrotron and serial XFEL datasets for Cysteinyl leukotriene GPCRs

Small-wedge synchrotron and serial XFEL datasets for Cysteinyl leukotriene GPCRs

On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets

Pairwise sequence similarity mapping with PaSiMap: reclassification of immunoglobulin domains from titin as case study

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