ArticleNucleic acids research2011
Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Computational design of sequence-specific DNA-binding proteins.Nature structural & molecular biology · 2025Article
- Article
- Identification of the Q-type ZFP gene family in Triticeaes and drought stress expression analysis in common wheat.Genetica · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Geometric deep learning of protein-DNA binding specificity.Nature methods · 2024Article
- Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking discrimination.PLoS computational biology · 2020Article
- Macromolecular modeling and design in Rosetta: recent methods and frameworks.Nature methods · 2020Review
- The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.Journal of chemical theory and computation · 2017Article
- Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules.Journal of chemical theory and computation · 2016Article
- A structural approach reveals how neighbouring C2H2 zinc fingers influence DNA binding specificity.Nucleic acids research · 2015Article
- A systematic survey of the Cys2His2 zinc finger DNA-binding landscape.Nucleic acids research · 2015Article
- Structure-based modeling of protein: DNA specificity.Briefings in functional genomics · 2015Review
- Recognition Code of ZNF191(243-368) and Its Interaction with DNA.Bioinorganic chemistry and applications · 2015Article
- Reprogramming homing endonuclease specificity through computational design and directed evolution.Nucleic acids research · 2014Article
- Protein-DNA binding: complexities and multi-protein codes.Nucleic acids research · 2014Review
- Article
- De novo prediction of DNA-binding specificities for Cys2His2 zinc finger proteins.Nucleic acids research · 2014Article
- Determining the specificities of TALENs, Cas9, and other genome-editing enzymes.Methods in enzymology · 2014Review
- Seamless genome editing in human pluripotent stem cells using custom endonuclease-based gene targeting and the piggyBac transposon.Nature protocols · 2013Article
- Probing the role of interfacial waters in protein-DNA recognition using a hybrid implicit/explicit solvation model.Proteins · 2013Article
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2 authors.
Funding
Abstract
Sequence-specific DNA recognition by gene regulatory proteins is critical for proper cellular functioning. The ability to predict the DNA binding preferences of these regulatory proteins from their amino acid sequence would greatly aid in reconstruction of their regulatory interactions. Structural modeling provides one route to such predictions: by building accurate molecular models of regulatory proteins in complex with candidate binding sites, and estimating their relative binding affinities for these sites using a suitable potential function, it should be possible to construct DNA binding profiles. Here, we present a novel molecular modeling protocol for protein-DNA interfaces that borrows conformational sampling techniques from de novo protein structure prediction to generate a diverse ensemble of structural models from small fragments of related and unrelated protein-DNA complexes. The extensive conformational sampling is coupled with sequence space exploration so that binding preferences for the target protein can be inferred from the resulting optimized DNA sequences. We apply the algorithm to predict binding profiles for a benchmark set of eleven C2H2 zinc finger transcription factors, five of known and six of unknown structure. The predicted profiles are in good agreement with experimental binding data; furthermore, examination of the modeled structures gives insight into observed binding preferences.
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