ArticleProceedings of the National Academy of Sciences of the United States of America2023
Physicochemical models of protein-DNA binding with standard and modified base pairs.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 25 citations in OpenAlex.
- A CThe New phytologist · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- DNA breathing and shape encode distinct mechanisms of transcription factor binding.Bioinformatics advances · 2026Article
- Article
- Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- Interfacial water confers transcription factors with dinucleotide specificity.Nature structural & molecular biology · 2025Article
- DNAdesign: feature-aware in silico design of synthetic DNA through mutation.Bioinformatics (Oxford, England) · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Contribution of DNA breathing to physical interactions with transcription factors.bioRxiv : the preprint server for biology · 2025Article
- DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.Nucleic acids research · 2025Article
- Enhanced Disease Resistance Mechanism of the CmoAP2/ERF Transcription Factor in Pumpkin through Genetic Mutations.ACS omega · 2024Article
- Geometric deep learning of protein-DNA binding specificity.Nature methods · 2024Article
- Updated understanding of the protein-DNA recognition code used by C2H2 zinc finger proteins.Current opinion in structural biology · 2024Review
- Skipping events impose repeated binding attempts: profound kinetic implications of protein-DNA conformational changes.Nucleic acids research · 2024Article
- Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.Nucleic acids research · 2024Article
- Systematic Comparison of Atomistic Force Fields for the Mechanical Properties of Double-Stranded DNA.Journal of chemical theory and computation · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Dissection of integrated readout reveals the structural thermodynamics of DNA selection by transcription factors.Structure (London, England : 1993) · 2024Article
- Deciphering the dynamic code: DNA recognition by transcription factors in the ever-changing genome.TranscriptionReview
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3 authors at 1 institution in 1 country.
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Abstract
DNA-binding proteins play important roles in various cellular processes, but the mechanisms by which proteins recognize genomic target sites remain incompletely understood. Functional groups at the edges of the base pairs (bp) exposed in the DNA grooves represent physicochemical signatures. As these signatures enable proteins to form specific contacts between protein residues and bp, their study can provide mechanistic insights into protein-DNA binding. Existing experimental methods, such as X-ray crystallography, can reveal such mechanisms based on physicochemical interactions between proteins and their DNA target sites. However, the low throughput of structural biology methods limits mechanistic insights for selection of many genomic sites. High-throughput binding assays enable prediction of potential target sites by determining relative binding affinities of a protein to massive numbers of DNA sequences. Many currently available computational methods are based on the sequence of standard Watson-Crick bp. They assume that the contribution of overall binding affinity is independent for each base pair, or alternatively include dinucleotides or short
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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.