» Articles » PMID: 28642456

Predicting Conformational Ensembles and Genome-wide Transcription Factor Binding Sites from DNA Sequences

Overview
Journal Sci Rep
Specialty Science
Date 2017 Jun 24
PMID 28642456
Citations 7
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.

Kalsan M, Jabeen A, Ahmad S Methods Mol Biol. 2024; 2812:317-343.

PMID: 39068371 DOI: 10.1007/978-1-0716-3886-6_18.


Moderation of Structural DNA Properties by Coupled Dinucleotide Contents in Eukaryotes.

Sievers A, Sauer L, Bisch M, Sprengel J, Hausmann M, Hildenbrand G Genes (Basel). 2023; 14(3).

PMID: 36981025 PMC: 10048725. DOI: 10.3390/genes14030755.


Dissecting and predicting different types of binding sites in nucleic acids based on structural information.

Jiang Z, Xiao S, Liu R Brief Bioinform. 2021; 23(1).

PMID: 34624074 PMC: 8769709. DOI: 10.1093/bib/bbab411.


Comprehensive study of nuclear receptor DNA binding provides a revised framework for understanding receptor specificity.

Penvose A, Keenan J, Bray D, Ramlall V, Siggers T Nat Commun. 2019; 10(1):2514.

PMID: 31175293 PMC: 6555819. DOI: 10.1038/s41467-019-10264-3.


MTTFsite: cross-cell type TF binding site prediction by using multi-task learning.

Zhou J, Lu Q, Gui L, Xu R, Long Y, Wang H Bioinformatics. 2019; 35(24):5067-5077.

PMID: 31161194 PMC: 6954652. DOI: 10.1093/bioinformatics/btz451.


References
1.
Beveridge D, Cheatham 3rd T, Mezei M . The ABCs of molecular dynamics simulations on B-DNA, circa 2012. J Biosci. 2012; 37(3):379-97. PMC: 4029509. DOI: 10.1007/s12038-012-9222-6. View

2.
Rohs R, West S, Sosinsky A, Liu P, Mann R, Honig B . The role of DNA shape in protein-DNA recognition. Nature. 2009; 461(7268):1248-53. PMC: 2793086. DOI: 10.1038/nature08473. View

3.
Stormo G . DNA binding sites: representation and discovery. Bioinformatics. 2000; 16(1):16-23. DOI: 10.1093/bioinformatics/16.1.16. View

4.
Slattery M, Zhou T, Yang L, Dantas Machado A, Gordan R, Rohs R . Absence of a simple code: how transcription factors read the genome. Trends Biochem Sci. 2014; 39(9):381-99. PMC: 4149858. DOI: 10.1016/j.tibs.2014.07.002. View

5.
Ahmad S, Gromiha M, Sarai A . Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information. Bioinformatics. 2004; 20(4):477-86. DOI: 10.1093/bioinformatics/btg432. View