Evidence map›Paper›PMID 42427595›Full record

ArticlebioRxiv : the preprint server for biology2026

CryoROLE: describing large inter-domain rotation in single particle cryo-EM.

Chengmin Li, Wooyoung Choi, Hao Wu, Yifan Cheng

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chengmin LiDepartment of Biochemistry and Biophysics, University of California San Francisco.
Wooyoung ChoiDepartment of Biochemistry and Biophysics, University of California San Francisco.
Hao WuDepartment of Biochemistry and Biophysics, University of California San Francisco.
Yifan ChengDepartment of Biochemistry and Biophysics, University of California San Francisco.ORCID 0000-0001-9535-0369

Funding

Project 3U54AI170792 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nevan J Krogan · 2022 to 2026
$35.7M
Advancing cryo-EM technology to address difficult biological questionsR35GM140847 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Yifan Cheng · 2021 to 2026
$2.1M
Acquisition of an electron microscope for high-resolution single particle cryo-EMS10OD021741 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHENG, YIFAN · 2016 to 2016
$2.0M
Linux cluster for near atomic resolution single particle cryo-EMS10OD020054 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHENG, YIFAN · 2015 to 2015
$456k
NIAID NIH HHS U54 AI170792NIGMS NIH HHS R35 GM140847NIH HHS S10 OD020054NIH HHS S10 OD021741
6 · The paper itself

Abstract

In single particle cryo-EM, analysis of continuous conformational heterogeneity has always been challenging. Both linear and deep learning-based methods treat conformational heterogeneity as perturbations to the consensus average conformation, limiting their capability in analyzing large protein motions. While classic conformational classifications are capable of handling large domain motion, they bin continuous protein dynamics into discrete static substates. Here, we present cryoROLE, a computational tool that extracts the continuous conformational dynamics embedded in the static composite map constructed from multi-body refinement into a landscape of relative orientation between the moving domains. Depicted in real space, the landscape allows intuitive interpretations of domain motion and the population of poses in the conformational space. Applying it to various biological systems reveals hidden conformational dynamics that are relevant to protein functions.

Identifiers

PMID42427595
PMCPMC13345010

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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.