<i>reciprocalspaceship</i>: a Python library for crystallographic data analysis

Greisman, Jack B.; Dalton, Kevin M.; Hekstra, Doeke R.

doi:10.1107/s160057672100755x

Cited by 23 publications

(22 citation statements)

References 32 publications

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“…In Xtrapol8, the user has the choice to calculate q -weighted, k -weighted or non-weighted Fourier difference maps, but the default is set to q -weighting. In the k -weighting scheme, the extent to which outliers are downweighed can be adjusted by an additional parameter, that has been set to 0.05 19 or 1.0 8 , 20 depending on reports, with the first one being the default in Xtrapol8.…”

Section: Resultsmentioning

confidence: 99%

Xtrapol8 enables automatic elucidation of low-occupancy intermediate-states in crystallographic studies

et al. 2022

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Unstable states studied in kinetic, time-resolved and ligand-based crystallography are often characterized by a low occupancy, which hinders structure determination by conventional methods. To automatically extract structural information pertaining to these states, we developed Xtrapol8, a program which (i) applies various flavors of Bayesian-statistics weighting to generate the most informative Fourier difference maps; (ii) determines the occupancy of the intermediate states by use of methods hitherto not available; (iii) calculates extrapolated structure factors using the various proposed formalisms while handling the issue of negative structure factor amplitudes, and (iv) refines the corresponding structures in real and reciprocal-space. The use of Xtrapol8 could accelerate data processing in kinetic and time-resolved crystallographic studies, and as well foster the identification of drug-targetable states in ligand-based crystallography.

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Section: Resultsmentioning

confidence: 99%

Xtrapol8 enables automatic elucidation of low-occupancy intermediate-states in crystallographic studies

et al. 2022

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“…To characterize the effect of high-redundancy data collection, we scaled and merged each of the four data sets with increasing numbers of consecutive frames in AIMLESS (Evans & Murshudov, 2013). We computed half-data-set correlation coefficients of the anomalous differences (CC anom ) for different numbers of frames using reciprocalspaceship (Greisman et al, 2021) to implement repeated twofold crossvalidation. With this, we determined the mean and standard deviations of the CC anom by resolution bin for the HEWL S-SAD data set.…”

Section: Assessing the Impact Of Redundancy On CC Anommentioning

confidence: 99%

Native SAD phasing at room temperature

Greisman¹,

Dalton²,

Sheehan³

et al. 2022

Acta Cryst Sect D Struct Biol

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Single-wavelength anomalous diffraction (SAD) is a routine method for overcoming the phase problem when solving macromolecular structures. This technique requires the accurate measurement of intensities to determine differences between Bijvoet pairs. Although SAD experiments are commonly conducted at cryogenic temperatures to mitigate the effects of radiation damage, such temperatures can alter the conformational ensemble of the protein and may impede the merging of data from multiple crystals due to non-uniform freezing. Here, a strategy is presented to obtain high-quality data from room-temperature, single-crystal experiments. To illustrate the strengths of this approach, native SAD phasing at 6.55 keV was used to solve four structures of three model systems at 295 K. The resulting data sets allow automatic phasing and model building, and reveal alternate conformations that reflect the structure of proteins at room temperature.

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“…Although Dalton et al (2022) demonstrated that Student's t-distribution offers superior performance to a normally distributed error model in some cases, it may not be the optimal choice. For a positively distributed quantity such as intensity, the optimal error model may not be symmetric (see, for example, Greisman et al, 2021). Future work may address this shortcoming by deriving a more appropriate error model.…”

Section: Error Modelsmentioning

confidence: 99%

Correcting systematic errors in diffraction data with modern scaling algorithms

Aldama,

Dalton,

Hekstra

2023

Acta Crystallogr D Struct Biol

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X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.

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reciprocalspaceship: a Python library for crystallographic data analysis

Cited by 23 publications

References 32 publications

Xtrapol8 enables automatic elucidation of low-occupancy intermediate-states in crystallographic studies

Xtrapol8 enables automatic elucidation of low-occupancy intermediate-states in crystallographic studies

Native SAD phasing at room temperature

Correcting systematic errors in diffraction data with modern scaling algorithms

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