Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Durán, Elisa; Djebali, Sarah; González, Santi; Flores, Oscar; Mercader, Josep M.; Guigó, Roderic; Torrents, David; Soler-López, Montserrat; Orozco, Modesto

doi:10.1093/nar/gkt511

Cited by 13 publications

(8 citation statements)

References 43 publications

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“…Recently, the report by Duran et al. (65) strongly supports the hypothesis that an ancient regulatory mechanism encoded by the intrinsic physical properties of the DNA may contribute to the complexity of transcription regulation in the human genome.…”

Section: Methodssupporting

confidence: 65%

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

Lin

Deng

Ding

et al. 2014

Nucleic Acids Research

474

245

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The σ54 promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ54 promoters. Here, a predictor called ‘iPro54-PseKNC’ was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called ‘pseudo k-tuple nucleotide composition’, which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ54 promoters.

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Section: Methodssupporting

confidence: 65%

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

Lin

Deng

Ding

et al. 2014

Nucleic Acids Research

474

245

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“…The above observation strongly indicates that DNA speaks a universal language. The strength of the physical signals of DNA language at promoters has also been previously observed by combining experiments and simulations studies (32).…”

Section: Combining All Parameters For Obtaining a Single Criterionsupporting

confidence: 52%

Toward a Universal Structural and Energetic Model for Prokaryotic Promoters

Mishra

Siwach

Misra

et al. 2018

Biophysical Journal

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With almost no consensus promoter sequence in prokaryotes, recruitment of RNA polymerase (RNAP) to precise transcriptional start sites (TSSs) has remained an unsolved puzzle. Uncovering the underlying mechanism is critical for understanding the principle of gene regulation. We attempted to search the hidden code in ∼16,500 promoters of 12 prokaryotes representing two kingdoms in their structure and energetics. Twenty-eight fundamental parameters of DNA structure including backbone angles, basepair axis, and interbasepair and intrabasepair parameters were used, and information was extracted from x-ray crystallography data. Three parameters (solvation energy, hydrogen-bond energy, and stacking energy) were selected for creating energetics profiles using in-house programs. DNA of promoter regions was found to be inherently designed to undergo a change in every parameter undertaken for the study, in all prokaryotes. The change starts from some distance upstream of TSSs and continues past some distance from TSS, hence giving a signature state to promoter regions. These signature states might be the universal hidden codes recognized by RNAP. This observation was reiterated when randomly selected promoter sequences (with little sequence conservation) were subjected to structure generation; all developed into very similar three-dimensional structures quite distinct from those of conventional B-DNA and coding sequences. Fine structural details at important motifs (viz. -11, -35, and -75 positions relative to TSS) of promoters reveal novel to our knowledge and pointed insights for RNAP interaction at these locations; it could be correlated with how some particular structural changes at the -11 region may allow insertion of RNAP amino acids in interbasepair space as well as facilitate the flipping out of bases from the DNA duplex.

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“…The information density in higher orders of the NP and BP models seems rather low, but it sums up to considerable sizes across the modelled region (Supplementary Figures S14 and S15). We speculate that the nucleotide dependencies in our models reflect, at least in part, DNA structural properties of core promoter regions that contribute to TSS recognition ( 55 ).…”

Section: Resultsmentioning

confidence: 99%

Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences

Siebert

Söding

2016

Nucleic Acids Res

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Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k − 1 act as priors for those of order k. This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly (P = 1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26–101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs.

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Unravelling the hidden DNA structural/physical code provides novel insights on promoter location

Cited by 13 publications

References 43 publications

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

Toward a Universal Structural and Energetic Model for Prokaryotic Promoters

Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences

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