ArticleNature communications2024
Predicting DNA structure using a deep learning method.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Single-molecule imaging reveals DNA shape read-out by the INO80 chromatin remodeler.Nature communications · 2026Article
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- Readout of intrinsic and induced DNA shape by homeodomain transcription factor complexes.Biophysical journal · 2026Article
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- Identifying RNA acInterdisciplinary sciences, computational life sciences · 2026Article
- Thermodynamics of Indirect Readout in Cre-bioRxiv : the preprint server for biology · 2026Article
- cgNA+min: computation of sequence-dependent dsDNA energy-minimizing minicircles.Nucleic acids research · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- Article
- Relevance of DNA tridimensional shape in RNA:DNA:DNA triple helix formation.Computational and structural biotechnology journal · 2026Article
- Intrinsically disordered regions facilitate target search to drive promoter selectivity by a yeast transcription factor.Nature communications · 2025Article
- Article
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- Article
- Novel fold and wing structure of Forkhead transcription factor facilitate DNA binding.Nucleic acids research · 2025Article
- Sequence-Dependent Shape and Stiffness of DNA and RNA Double Helices: Hexanucleotide Scale and Beyond.Journal of chemical information and modeling · 2025Article
- De novo promoter design method based on deep generative and dynamic evolution algorithm.Nucleic acids research · 2025Article
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3 authors.
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Abstract
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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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.