ArticleNucleic acids research2005
Protein-DNA binding specificity predictions with structural models.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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Who cites it
105 citing papers in PubMed.
- An information theory approach to quantifying the sequence-dependent response of nucleic acid motors with applications to nanopore DNA sequencing.Nature communications · 2026Article
- Article
- ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.Briefings in bioinformatics · 2024Article
- Noncanonical DNA Cleavage by BamHI Endonuclease in Laterally Confined DNA Monolayers Is a Step Function of DNA Density and Sequence.Molecules (Basel, Switzerland) · 2022Article
- Identifying essential proteins from protein-protein interaction networks based on influence maximization.BMC bioinformatics · 2022Article
- Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.Proteins · 2022Article
- Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions.PLoS computational biology · 2021Article
- Comparative Analysis of the IclR-Family of Bacterial Transcription Factors and Their DNA-Binding Motifs: Structure, Positioning, Co-Evolution, Regulon Content.Frontiers in microbiology · 2021Article
- Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites.Nucleic acids research · 2020Article
- New insights into protein-DNA binding specificity from hydrogen bond based comparative study.Nucleic acids research · 2019Article
- DNA-protein interaction: identification, prediction and data analysis.Molecular biology reports · 2019Review
- Static Kinks or Flexible Hinges: Multiple Conformations of Bent DNA Bound to Integration Host Factor Revealed by Fluorescence Lifetime Measurements.The journal of physical chemistry. B · 2018Article
- FoldX accurate structural protein-DNA binding prediction using PADA1 (Protein Assisted DNA Assembly 1).Nucleic acids research · 2018Article
- Structural changes in DNA-binding proteins on complexation.Nucleic acids research · 2018Article
- Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver.Bioinformatics (Oxford, England) · 2018Article
- EMQIT: a machine learning approach for energy based PWM matrix quality improvement.Biology direct · 2017Article
- Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.BMC bioinformatics · 2017Article
- An efficient algorithm for improving structure-based prediction of transcription factor binding sites.BMC bioinformatics · 2017Article
- Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors.PLoS computational biology · 2017Article
- Computational Redesign of Acyl-ACP Thioesterase with Improved Selectivity toward Medium-Chain-Length Fatty Acids.ACS catalysis · 2017Article
45 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-DNA interactions play a central role in transcriptional regulation and other biological processes. Investigating the mechanism of binding affinity and specificity in protein-DNA complexes is thus an important goal. Here we develop a simple physical energy function, which uses electrostatics, solvation, hydrogen bonds and atom-packing terms to model direct readout and sequence-specific DNA conformational energy to model indirect readout of DNA sequence by the bound protein. The predictive capability of the model is tested against another model based only on the knowledge of the consensus sequence and the number of contacts between amino acids and DNA bases. Both models are used to carry out predictions of protein-DNA binding affinities which are then compared with experimental measurements. The nearly additive nature of protein-DNA interaction energies in our model allows us to construct position-specific weight matrices by computing base pair probabilities independently for each position in the binding site. Our approach is less data intensive than knowledge-based models of protein-DNA interactions, and is not limited to any specific family of transcription factors. However, native structures of protein-DNA complexes or their close homologs are required as input to the model. Use of homology modeling can significantly increase the extent of our approach, making it a useful tool for studying regulatory pathways in many organisms and cell types.
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