Probing the role of the protonation state of a minor groove-linker histidine in Exd-Hox–DNA binding

Jiang, Yibei; Chiu, Tsu-Pei; Mitra, Raktim; Rohs, Remo

doi:10.1016/j.bpj.2023.12.013

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…Therefore, DNA shape is a critical property that cannot be overlooked ( 3 ). Our definition of DNA shape includes the important properties of conformational flexibility ( 4 ) and minor-groove electrostatic potential ( 5 ). Nevertheless, we are critically aware that local DNA shape is only one scale where genes are regulated ( 6–8 ).…”

Section: Introductionmentioning

confidence: 99%

Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers

Li,

Rohs

2024

Nucleic Acids Research

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Sequence-dependent DNA shape plays an important role in understanding protein–DNA binding mechanisms. High-throughput prediction of DNA shape features has become a valuable tool in the field of protein–DNA recognition, transcription factor–DNA binding specificity, and gene regulation. However, our widely used webserver, DNAshape, relies on statistically summarized pentamer query tables to query DNA shape features. These query tables do not consider flanking regions longer than two base pairs, and acquiring a query table for hexamers or higher-order k-mers is currently still unrealistic due to limitations in achieving sufficient statistical coverage in molecular simulations or structural biology experiments. A recent deep-learning method, Deep DNAshape, can predict DNA shape features at the core of a DNA fragment considering flanking regions of up to seven base pairs, trained on limited simulation data. However, Deep DNAshape is rather complicated to install, and it must run locally compared to the pentamer-based DNAshape webserver, creating a barrier for users. Here, we present the Deep DNAshape webserver, which has the benefits of both methods while being accurate, fast, and accessible to all users. Additional improvements of the webserver include the detection of user input in real time, the ability of interactive visualization tools and different modes of analyses. URL: https://deepdnashape.usc.edu

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Section: Introductionmentioning

confidence: 99%

Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers

Li,

Rohs

2024

Nucleic Acids Research

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View full text Add to dashboard Cite

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Predicting DNA structure using a deep learning method

Li,

Chiu,

Rohs

2024

Nat Commun

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Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k-mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, DNA structural features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing an understanding of the effects of flanking regions on DNA structure in a target region of a sequence. The Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as versatile and powerful tool for diverse DNA structure-related studies.

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Probing the role of the protonation state of a minor groove-linker histidine in Exd-Hox–DNA binding

Cited by 2 publications

References 67 publications

Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers

Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers

Predicting DNA structure using a deep learning method

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