ArticleNature methods2024
Geometric deep learning of protein-DNA binding specificity.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- Readout of intrinsic and induced DNA shape by homeodomain transcription factor complexes.Biophysical journal · 2026Article
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- SoPPIs: a highly parallelized protein-protein-interaction screening method in prokaryotic and eukaryotic hosts.Nucleic acids research · 2026Article
- Constitutive activation of a hybrid two-component regulator reveals cross-regulation of polysaccharide utilization genes in Bacteroides.The Journal of biological chemistry · 2026Article
- Caveat emptor: predicting and modeling protein-DNA recognition and binding via machine-learning computational approaches.Nucleic acids research · 2026Review
- Informational blueprints reveal condition-dependent gene regulatory architectures.bioRxiv : the preprint server for biology · 2026Article
- Predicting protein-nucleic acid interactions via protein language models with biophysical and evolutionary priors.iScience · 2026Article
- Neutrophil CD14 is a driver and a therapeutic target for deep vein thrombosis.Blood advances · 2026Article
- Dormancy regulon reduction was pivotal to the evolution of Mycobacterium tuberculosis.Nature communications · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- A geometric deep learning framework for genome-wide prediction of enzyme turnover number.Genome biology · 2026Article
- Quantitative modulation of a spatial enhancer through the biophysical properties of a transcription factor binding site.Science advances · 2026Article
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- Computational design of sequence-specific DNA-binding proteins.Nature structural & molecular biology · 2025Article
- RNA sequence design and protein-DNA specificity prediction with NA-MPNN.bioRxiv : the preprint server for biology · 2025Article
- Novel fold and wing structure of Forkhead transcription factor facilitate DNA binding.Nucleic acids research · 2025Article
- Runaway evolution of telomeres in ascomycetous yeasts was accompanied by the replacement of ancestral telomeric proteins.Nucleic acids research · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding specificity, to a wide range of DNA sequences. This information is not directly accessible in a single structure. Here, to access this information, we present Deep Predictor of Binding Specificity (DeepPBS), a geometric deep-learning model designed to predict binding specificity from protein-DNA structure. DeepPBS can be applied to experimental or predicted structures. Interpretable protein heavy atom importance scores for interface residues can be extracted. When aggregated at the protein residue level, these scores are validated through mutagenesis experiments. Applied to designed proteins targeting specific DNA sequences, DeepPBS was demonstrated to predict experimentally measured binding specificity. DeepPBS offers a foundation for machine-aided studies that advance our understanding of molecular interactions and guide experimental designs and synthetic biology.
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Registered trials
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.