Evidence map›Paper›PMID 38517699›Full record

ArticleBriefings in bioinformatics2024

DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.

Ziying Zhang, Yaxian Cai, Biao Zhang, Wei Zheng, Lydia Freddolino, Guijun Zhang, Xiaogen Zhou

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. MultilevelCurrent research in structural biology · 2026
    Article
  2. Article
  3. Article
  4. Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta.bioRxiv : the preprint server for biology · 2026
    Article
  5. EMProt improves structure determination from cryo-EM maps.Nature structural & molecular biology · 2026
    Article
  6. Article
  7. Article
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  10. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ziying ZhangCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Yaxian CaiCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Biao ZhangCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Wei ZhengDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-2984-9003
Lydia FreddolinoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-5821-4226
Guijun ZhangCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0002-7815-5884
Xiaogen ZhouCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0001-6839-1923

Funding

Structure-based functional annotation of microbial genomesR01AI134678 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lydia Petra Freddolino, SCOTT J. HULTGREN · 2018 to 2026
$6.5M
National Nature Science Foundation of China 62203389National Science and Technology Major Project of China 2022ZD0115103National Science Foundation ACI1548562NIAID NIH HHS R01 AI134678
6 · The paper itself

Abstract

The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.

Indexed as

Cryoelectron MicroscopyModels, MolecularProtein Conformationcryo-EMcryo-ETprotein complexprotein domain assemblyprotein structure prediction

Identifiers

PMID38517699
PMCPMC10959074

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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.