ArticleNucleic acids research2011
From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.
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 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Advances in Methods for Accurate Prediction of RNA-Small Molecule Binding Sites: From Isolated to AI-Integrated Strategies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Structure-based prediction of protein-nucleic acid binding using graph neural networks.Biophysical reviews · 2024Review
- Dissecting and predicting different types of binding sites in nucleic acids based on structural information.Briefings in bioinformatics · 2022Article
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.BMC bioinformatics · 2017Article
- Protein-RNA interactions: structural biology and computational modeling techniques.Biophysical reviews · 2016Review
- BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins.Nucleic acids research · 2016Article
- Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.BMC bioinformatics · 2016Article
- Analysis and classification of DNA-binding sites in single-stranded and double-stranded DNA-binding proteins using protein information.IET systems biology · 2014Article
- Identification of single-stranded and double-stranded DNA binding proteins based on protein structure.BMC bioinformatics · 2014Article
- Differential geometric analysis of alterations in MH α-helices.Journal of computational chemistry · 2013Article
- Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters.Nucleic acids research · 2012Article
- Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.BMC bioinformatics · 2012Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.
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