A unifying Bayesian framework for merging X-ray diffraction data

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

doi:10.1038/s41467-022-35280-8

Cited by 16 publications

(47 citation statements)

References 65 publications

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“…Neural networks are universal function approximators (Hornik et al, 1989), meaning that the same inference routine can be applied to many types of diffraction data without the need to construct and calibrate detailed physical models of the experiment. The algorithm then proceeds to estimate merged structurefactor amplitudes (along with the scale function) from unmerged reflection intensities by maximizing the following objective function (for a derivation and implementation details, see Dalton et al, 2022),…”

Section: Scaling Diffraction Data By Variational Inferencementioning

confidence: 99%

“…In synchrotron and XFEL serial crystallography, we also recommend the use of Careless for modestly sized data sets. Dalton et al (2022) demonstrated that the model performs well for such serial data, especially when layers with per-image parameters (image layers) are appended to the neural network. Careless performs well when all data fit within the memory of a single GPU accelerator (typically data sets of up to 10 000 images).…”

Section: Should I Use Careless?mentioning

confidence: 99%

“…One of the strengths of Careless is its flexibility. As demonstrated by Dalton et al (2022), Careless can model drastically different types of diffraction experiments. However, this flexibility also results in several hyperparameters that the user must adjust.…”

Section: Common Hyperparametersmentioning

confidence: 99%

“…One of the major benefits of the variational approach to X-ray data processing is its extensibility. We anticipate that over time, we and others will expand on the core model presented in Dalton et al (2022) to further improve its accuracy and applicability. Below, we outline several anticipated extensions.…”

Section: Future Directionsmentioning

confidence: 99%

“…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).…”

Section: Error Modelsmentioning

confidence: 99%

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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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Section: Scaling Diffraction Data By Variational Inferencementioning

confidence: 99%

Section: Should I Use Careless?mentioning

confidence: 99%

Section: Common Hyperparametersmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Section: Error Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Correcting systematic errors in diffraction data with modern scaling algorithms

Aldama,

Dalton,

Hekstra

2023

Acta Crystallogr D Struct Biol

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Practical considerations for the analysis of time-resolved x-ray data

Schmidt

2023

Structural Dynamics

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The field of time-resolved macromolecular crystallography has been expanding rapidly after free electron lasers for hard x rays (XFELs) became available. Techniques to collect and process data from XFELs spread to synchrotron light sources. Although time-scales and data collection modalities can differ substantially between these types of light sources, the analysis of the resulting x-ray data proceeds essentially along the same pathway. At the base of a successful time-resolved experiment is a difference electron density (DED) map that contains chemically meaningful signal. If such a difference map cannot be obtained, the experiment has failed. Here, a practical approach is presented to calculate DED maps and use them to determine structural models.

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BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules

Henning,

Kosheleva,

Šrajer

et al. 2024

Structural Dynamics

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A major goal in biomedical science is to move beyond static images of proteins and other biological macromolecules to the internal dynamics underlying their function. This level of study is necessary to understand how these molecules work and to engineer new functions and modulators of function. Stemming from a visionary commitment to this problem by Keith Moffat decades ago, a community of structural biologists has now enabled a set of x-ray scattering technologies for observing intramolecular dynamics in biological macromolecules at atomic resolution and over the broad range of timescales over which motions are functionally relevant. Many of these techniques are provided by BioCARS, a cutting-edge synchrotron radiation facility built under Moffat leadership and located at the Advanced Photon Source at Argonne National Laboratory. BioCARS enables experimental studies of molecular dynamics with time resolutions spanning from 100 ps to seconds and provides both time-resolved x-ray crystallography and small- and wide-angle x-ray scattering. Structural changes can be initiated by several methods—UV/Vis pumping with tunable picosecond and nanosecond laser pulses, substrate diffusion, and global perturbations, such as electric field and temperature jumps. Studies of dynamics typically involve subtle perturbations to molecular structures, requiring specialized computational techniques for data processing and interpretation. In this review, we present the challenges in experimental macromolecular dynamics and describe the current state of experimental capabilities at this facility. As Moffat imagined years ago, BioCARS is now positioned to catalyze the scientific community to make fundamental advances in understanding proteins and other complex biological macromolecules.

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A unifying Bayesian framework for merging X-ray diffraction data

Cited by 16 publications

References 65 publications

Correcting systematic errors in diffraction data with modern scaling algorithms

Correcting systematic errors in diffraction data with modern scaling algorithms

Practical considerations for the analysis of time-resolved x-ray data

BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules

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