DNA–Protein Binding is Dominated by Short Anchoring Elements

Unveiling the complexities of gene expression regulation, the study explores the intricate DNA-binding mechanisms of transcription factors (TFs). By employing the KaScape method previously developed to measure both bound and unbound populations at thermodynamic equilibrium, "anchoring elements" (AEs), 3-4 base pair sequences, are identified in Arabidopsis WRKY and human PU.1 TFs crucial for binding affinity. Building on the BEESEM method, the study introduces the AEEscape algorithm, which advances the AE concept by creating a precise model of the position-specific k-mer binding energy landscape. This method allows for the direct identification of the dominant role of AEs from experimental data. Moreover, when integrated with genomic data, it reveals an energetic funnel around transcription factor binding sites (TFBSs), which is directly correlated with the density of AEs (AED). The findings not only offer a fresh perspective on TF-TFBS interactions but also highlight the critical role of AED in gene regulation. These insights can pave the way for innovative strategies to manipulate gene expression.

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Publication Details

Journal
Advanced Science
Published
2025-03-26
DOI
https://doi.org/10.1002/advs.202414823
Citations
2
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
1.61
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article

DNA–Protein Binding is Dominated by Short Anchoring Elements

Hong Chen, Xiao‐Dong Su, Hao Ge, Yongping Xu
2 citations
Advanced Science
Genomics and Chromatin Dynamics
1.61
article

DNA–Protein Binding is Dominated by Short Anchoring Elements

Hong Chen, Xiao‐Dong Su, Hao Ge, Yongping Xu
article en
2 citations

Abstract

Unveiling the complexities of gene expression regulation, the study explores the intricate DNA-binding mechanisms of transcription factors (TFs). By employing the KaScape method previously developed to measure both bound and unbound populations at thermodynamic equilibrium, "anchoring elements" (AEs), 3-4 base pair sequences, are identified in Arabidopsis WRKY and human PU.1 TFs crucial for binding affinity. Building on the BEESEM method, the study introduces the AEEscape algorithm, which advances the AE concept by creating a precise model of the position-specific k-mer binding energy landscape. This method allows for the direct identification of the dominant role of AEs from experimental data. Moreover, when integrated with genomic data, it reveals an energetic funnel around transcription factor binding sites (TFBSs), which is directly correlated with the density of AEs (AED). The findings not only offer a fresh perspective on TF-TFBS interactions but also highlight the critical role of AED in gene regulation. These insights can pave the way for innovative strategies to manipulate gene expression.

Advanced ScienceVol. 12(19)
Peking University (CN)
Affordable and clean energy
Openalex Percentile: Top 18%
Genomics and Chromatin Dynamics
1.61
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