DNABINDPROT: fluctuation-based predictor of DNA-binding residues within a network of interacting residues
Open Access
- 15 May 2010
- journal article
- research article
- Published by Oxford University Press (OUP) in Nucleic Acids Research
- Vol. 38 (suppl_2) , W417-W423
- https://doi.org/10.1093/nar/gkq396
Abstract
DNABINDPROT is designed to predict DNA-binding residues, based on the fluctuations of residues in high-frequency modes by the Gaussian network model. The residue pairs that display high mean-square distance fluctuations are analyzed with respect to DNA binding, which are then filtered with their evolutionary conservation profiles and ranked according to their DNA-binding propensities. If the analyses are based on the exact outcome of fluctuations in the highest mode, using a conservation threshold of 5, the results have a sensitivity, specificity, precision and accuracy of 9.3%, 90.5%, 18.1% and 78.6%, respectively, on a dataset of 36 unbound–bound protein structure pairs. These values increase up to 24.3%, 93.4%, 45.3% and 83.3% for the respective cases, when the neighboring two residues are considered. The relatively low sensitivity appears with the identified residues being selective and susceptible more for the binding core residues rather than all DNA-binding residues. The predicted residues that are not tagged as DNA-binding residues are those whose fluctuations are coupled with DNA-binding sites. They are in close proximity as well as plausible for other functional residues, such as ligand and protein–protein interaction sites. DNABINDPROT is free and open to all users without login requirement available at: http://www.prc.boun.edu.tr/appserv/prc/dnabindprot/.Keywords
This publication has 50 references indexed in Scilit:
- DBD-Hunter: a knowledge-based method for the prediction of DNA–protein interactionsNucleic Acids Research, 2008
- Prediction of DNA-binding residues from sequenceBioinformatics, 2007
- ISIS: interaction sites identified from sequenceBioinformatics, 2007
- Predicting DNA-binding sites of proteins from amino acid sequenceBMC Bioinformatics, 2006
- Efficient Prediction of Nucleic Acid Binding Function from Low-resolution Protein StructuresJournal of Molecular Biology, 2006
- Predicting protein interaction sites from residue spatial sequence profile and evolution rateFEBS Letters, 2005
- Kernel-based machine learning protocol for predicting DNA-binding proteinsNucleic Acids Research, 2005
- An evolution based classifier for prediction of protein interfaces without using protein structuresBioinformatics, 2005
- Moment-based Prediction of DNA-binding ProteinsJournal of Molecular Biology, 2004
- Annotating Nucleic Acid-Binding Function Based on Protein StructureJournal of Molecular Biology, 2003