Evidence map›Paper›PMID 28763006›Full record

ReviewGenes2017

Proteins Recognizing DNA: Structural Uniqueness and Versatility of DNA-Binding Domains in Stem Cell Transcription Factors.

Dhanusha Yesudhas, Maria Batool, Muhammad Ayaz Anwar, Suresh Panneerselvam, Sangdun Choi

Open access · goldAbstract readReview
In one paragraph

Review in Genes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
1.2field-weighted citation impact, top 21% of its field
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

25 citing papers in PubMed, 55 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025
    Article
  5. Article
  6. Evidence for vitellogenin DNA-binding in honey bees.Protein science : a publication of the Protein Society · 2025
    Article
  7. Article
  8. Review
  9. NovelImmune network · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021
    Article
  18. Article
  19. Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins.International journal of molecular sciences · 2021
    Review
  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

5 authors at 1 institution in 1 country.

Dhanusha YesudhasDepartment of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea. dhanusha2504@gmail.com.
Maria BatoolDepartment of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea. mariabatool.28@gmail.com.
Muhammad Ayaz AnwarDepartment of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea. ayaz@ajou.ac.kr.
Suresh PanneerselvamDepartment of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea. sureshcbt@gmail.com.
Sangdun ChoiDepartment of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea. sangdunchoi@ajou.ac.kr.
Ajou University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins in the form of transcription factors (TFs) bind to specific DNA sites that regulate cell growth, differentiation, and cell development. The interactions between proteins and DNA are important toward maintaining and expressing genetic information. Without knowing TFs structures and DNA-binding properties, it is difficult to completely understand the mechanisms by which genetic information is transferred between DNA and proteins. The increasing availability of structural data on protein-DNA complexes and recognition mechanisms provides deeper insights into the nature of protein-DNA interactions and therefore, allows their manipulation. TFs utilize different mechanisms to recognize their cognate DNA (direct and indirect readouts). In this review, we focus on these recognition mechanisms as well as on the analysis of the DNA-binding domains of stem cell TFs, discussing the relative role of various amino acids toward facilitating such interactions. Unveiling such mechanisms will improve our understanding of the molecular pathways through which TFs are involved in repressing and activating gene expression.

Indexed as

base and shape readoutsprotein-DNA interactionprotein-DNA recognitionTF domain family

Identifiers

PMID28763006
PMCPMC5575656
OpenAlexW2739634185

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.