Evidence map›Paper›PMID 39257752›Full record

ArticlebioRxiv : the preprint server for biology2024

Energy filtering enables macromolecular MicroED data at sub-atomic resolution.

Max T B Clabbers, Johan Hattne, Michael W Martynowycz, Tamir Gonen

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Max T B ClabbersHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-5466-6508
Johan HattneHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-8936-0912
Michael W MartynowyczDepartment of Biological Chemistry, University of California, Los Angeles, CA 90095.ORCID 0000-0003-0055-230X
Tamir GonenHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-9254-4069

Funding

TRD4: Design and Engineering of MicroED hardware for nanocrystallization and time resolved studiesP41GM136508 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GONEN, TAMIR · 2020 to 2024
$7.6M
NIGMS NIH HHS P41 GM136508
6 · The paper itself

Abstract

High resolution information is important for accurate structure modelling. However, this level of detail is typically difficult to attain in macromolecular crystallography because the diffracted intensities rapidly fade with increasing resolution. The problem cannot be circumvented by increasing the fluence as this leads to detrimental radiation damage. Previously, we demonstrated that high quality MicroED data can be obtained at low flux conditions using electron counting with direct electron detectors. The improved sensitivity and accuracy of these detectors essentially eliminate the read-out noise, such that the measurement of faint high-resolution reflections is limited by other sources of noise. Inelastic scattering is a major contributor of such noise, increasing background counts and broadening diffraction spots. Here, we demonstrate that a substantial improvement in signal-to-noise ratio can be achieved using an energy filter to largely remove the inelastically scattered electrons. This strategy resulted in sub-atomic resolution MicroED data from proteinase K crystals, enabling accurate structure modelling and the visualization of detailed features. Interestingly, filtering out the noise revealed diffuse scattering phenomena that can hold additional structural information. Our findings suggest that combining energy filtering and electron counting can provide more accurate measurements at higher resolution, providing better insights into protein function and facilitating more precise model refinement.

Identifiers

PMID39257752
PMCPMC11383697

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.