Evidence map›Paper›PMID 28642456›Full record

ArticleScientific reports2017

Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences.

Munazah Andrabi, Andrew Paul Hutchins, Diego Miranda-Saavedra, Hidetoshi Kono, Ruth Nussinov, Kenji Mizuguchi, Shandar Ahmad

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

7 authors.

Munazah AndrabiNational Institutes of Biomedical Innovation Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka, 5670085, Japan.
Andrew Paul HutchinsDepartment of Biology, Southern University of Science and Technology of China, Shenzhen, 518055, China.
Diego Miranda-SaavedraWorld Premier International (WPI) Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, 565-0871, Osaka, Japan.
Hidetoshi KonoMolecular Modeling and Simulation (MMS) Group, National Institutes for Quantum and Radiological Science and Technology, 8-1-7, Umemidai, Kizugawa, Kyoto, 619-0215, Japan.
Ruth NussinovNational Cancer Institute, Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick, Maryland, USA.
Kenji MizuguchiNational Institutes of Biomedical Innovation Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka, 5670085, Japan.ORCID 0000-0003-3021-7078
Shandar AhmadNational Institutes of Biomedical Innovation Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka, 5670085, Japan. shandar@jnu.ac.in.

Funding

Biomolecular Recognition and Binding MechanismsZIABC010441 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$9.4M
Method Development: Efficient Computer Vision Based AlgorithmsZIABC010442 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$2.4M
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

DNA shape is emerging as an important determinant of transcription factor binding beyond just the DNA sequence. The only tool for large scale DNA shape estimates, DNAshape was derived from Monte-Carlo simulations and predicts four broad and static DNA shape features, Propeller twist, Helical twist, Minor groove width and Roll. The contributions of other shape features e.g. Shift, Slide and Opening cannot be evaluated using DNAshape. Here, we report a novel method DynaSeq, which predicts molecular dynamics-derived ensembles of a more exhaustive set of DNA shape features. We compared the DNAshape and DynaSeq predictions for the common features and applied both to predict the genome-wide binding sites of 1312 TFs available from protein interaction quantification (PIQ) data. The results indicate a good agreement between the two methods for the common shape features and point to advantages in using DynaSeq. Predictive models employing ensembles from individual conformational parameters revealed that base-pair opening - known to be important in strand separation - was the best predictor of transcription factor-binding sites (TFBS) followed by features employed by DNAshape. Of note, TFBS could be predicted not only from the features at the target motif sites, but also from those as far as 200 nucleotides away from the motif.

Indexed as

Base SequenceBinding SitesComputational BiologyGenome-Wide Association StudyNucleic Acid ConformationBase CompositionDatabases, GeneticMolecular Dynamics SimulationProtein BindingTranscription FactorsTranscription Factors

Identifiers

PMID28642456
PMCPMC5481346

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.