Evidence map›Paper›PMID 17567616›Full record

ArticleNucleic acids research2007

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.

Kengo Kinoshita, Yoichi Murakami, Haruki Nakamura

Abstract read
In one paragraph

Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Review
  2. Deciphering the language of mingling lipids and proteins.Current opinion in structural biology · 2025
    Review
  3. Article
  4. Exploring Ligand Binding Domain Dynamics in the NRs Superfamily.International journal of molecular sciences · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Landscape of protein-small ligand binding modes.Protein science : a publication of the Protein Society · 2016
    Article
  11. Article
  12. Article
  13. Article
  14. Exhaustive comparison and classification of ligand-binding surfaces in proteins.Protein science : a publication of the Protein Society · 2013
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

3 authors.

Kengo KinoshitaInstitute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minatoku, Tokyo, 108-8639, Japan. kino@ims.u-tokyo.ac.jp
Yoichi Murakami
Haruki Nakamura

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

Indexed as

Computer GraphicsDatabases, ProteinModels, MolecularSoftwareUser-Computer InterfaceBinding SitesComputational BiologyDatabase Management SystemsImaging, Three-DimensionalInformation Storage and RetrievalLigandsProtein ConformationProteinsStatic ElectricityLigandsProteins

Identifiers

PMID17567616
PMCPMC1933152

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.