Native SAD Phasing at Room Temperature

Greisman, Jack B.; Dalton, Kevin M.; Sheehan, Candice J.; Klureza, Margaret A.; Hekstra, Doeke R.

doi:10.1101/2021.12.13.472485

Cited by 3 publications

(8 citation statements)

References 59 publications

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“…We next assessed the ability of Careless to extract small crystallographic signals from conventional monochromatic rotation series data (a detailed walk-through of each example is available at https:// github.com/rs-station/careless-examples). To do so, we applied Careless to a sulfur single-wavelength anomalous diffraction (SAD) data set of lysozyme collected at ambient temperature 19 . It consists of a single 1,440 image rotation series collected in 0.5 degree increments at a low X-ray energy, 6.55 keV, at Advanced Photon Source beamline 24-ID-C.…”

Section: Robust Inference Of Anomalous Signal From Monochromatic Diff...mentioning

confidence: 99%

“…Anomalous signal in real space, on cysteine and methionine S atoms, provides an additional measure of the accuracy of the estimated structure factor amplitudes. To this end, we performed limited automated refinement in Phenix using a sulfur-omit version of PDB ID 7L84 19 as a starting model, and inferred peak heights from the resulting anomalous omit map. As shown in Supplementary Table 2, Aimless outperforms Careless with 16 d.f.…”

Section: Robust Inference Of Anomalous Signal From Monochromatic Diff...mentioning

confidence: 99%

“…Data collection for hen egg white lysozyme was described in reference 19 . Data for photoactive yellow protein were collected as described in reference 50 .…”

Section: Data Collection and Analysismentioning

confidence: 99%

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A unifying Bayesian framework for merging X-ray diffraction data

2022

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Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

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Section: Robust Inference Of Anomalous Signal From Monochromatic Diff...mentioning

confidence: 99%

Section: Robust Inference Of Anomalous Signal From Monochromatic Diff...mentioning

confidence: 99%

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A unifying Bayesian framework for merging X-ray diffraction data

2022

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“…We next assessed the ability of Careless to extract small crystallographic signals from conventional monochromatic rotation series data (a detailed walk-through of each example is available at https://github.com/Hekstra-Lab/carelessexamples). To do so, we applied Careless to a sulfur single-wavelength anomalous diffraction (SAD) data set of lysozyme collected at ambient temperature [19]. It consists of a single 1,440 image rotation series collected in 0.5 degree increments at a low X-ray energy, 6.55 keV, at Advanced Photon Source beamline 24-ID-C.…”

Section: Robust Inference Of Anomalous Signal From Monochromatic Diff...mentioning

confidence: 99%

“…Data collection for hen egg white lysozyme was described in ref. [9]. Data for photoactive yellow protein were collected as described in ref.…”

Section: Merging X-ray Data By Variational Inferencementioning

confidence: 99%

A Unifying Bayesian Framework for Merging X-ray Diffraction Data

Dalton

Greisman

Hekstra

2021

Preprint

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The primary data in X-ray diffraction experiments are images which contain bright spots known as reflections. In order to compute the electron density of the crystal which yields these reflections, the intensity of each reflection must be estimated. Redundant observations must be parsimoniously reduced to a set of summary intensities and corresponding error estimates. This task is confounded by a range of systematic effects leading to equivalent reflections being expressed on different scales. Here, we present a modern Bayesian solution to this problem, careless, which uses deep learning and variational inference to simultaneously merge and scale reflection observations. We successfully apply this method to conventional monochromatic X-ray diffraction data, polychromatic diffraction data, and finally X-ray free-electron laser data. Thereby, we find that this single model is generally applicable to many types of X-ray diffraction experiments.

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Mapping protein conformational landscapes from crystallographic drug fragment screens

Saeed,

Klureza,

Hekstra

2024

Preprint

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Proteins are dynamic macromolecules. Knowledge of a protein’s thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein’s structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins’ conformational landscapes.

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Native SAD Phasing at Room Temperature

Cited by 3 publications

References 59 publications

A unifying Bayesian framework for merging X-ray diffraction data

A unifying Bayesian framework for merging X-ray diffraction data

A Unifying Bayesian Framework for Merging X-ray Diffraction Data

Mapping protein conformational landscapes from crystallographic drug fragment screens

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