Dataset Information

PDIviz: analysis and visualization of protein-DNA binding interfaces.

ABSTRACT: Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein-DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different pharmacophoric type (hydrogen bond donors/acceptors, hydrophobic and thymine methyl). Each mode comes in three styles to focus the visual analysis on the protein or DNA side of the interface, or on the nucleotide sequence. PDIviz allows for the generation of publication quality images, all calculated data can be written to disk, and a command line interface is provided for automating tasks. The plugin may be helpful for the detailed identification of regions involved in DNA base and shape readout, and can be particularly useful in rapidly pinpointing the overall mode of interaction.Freely available at http://melolab.org/pdiviz/ as a PyMOL plugin. Tested with incentive, educational, and open source versions of PyMOL on Windows, Mac and Linux systems.aschueller@bio.puc.clSupplementary data are available at Bioinformatics online.

SUBMITTER: Ribeiro J

PROVIDER: S-EPMC4528634 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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PDIviz: analysis and visualization of protein-DNA binding interfaces.

Ribeiro Judemir J Melo Francisco F Schüller Andreas A

Bioinformatics (Oxford, England) 20150416 16

<h4>Unlabelled</h4>Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein-DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different p ...[more]

PMID: 25886981

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Project description:BackgroundProtein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.ResultsHere, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.ConclusionsPreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Dataset Information

PDIviz: analysis and visualization of protein-DNA binding interfaces.

Publications

PDIviz: analysis and visualization of protein-DNA binding interfaces.

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