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Residue‐level prediction of DNA‐binding sites and its application on DNA‐binding protein predictions

Residue‐level prediction of DNA‐binding sites and its application on DNA‐binding protein predictions Protein–DNA interactions are crucial to many cellular activities such as expression‐control and DNA‐repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA‐binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA‐binding sites; to improve the prediction by distance‐dependent refinement; use these predictions to identify DNA‐binding proteins. We use a support vector machines (SVM)‐based approach to harness the features of the DNA‐binding residues to distinguish them from non‐binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA‐binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post‐processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA‐binding residues can be used to differentiate DNA‐binding proteins from non‐DNA‐binding proteins with an accuracy of 78%. Results presented here demonstrate that machine‐learning can be applied to automated identification of DNA‐binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site‐directed mutagenesis and macromolecular docking. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Febs Letters Wiley

Residue‐level prediction of DNA‐binding sites and its application on DNA‐binding protein predictions

Bhardwaj, Nitin; Lu, Hui
Febs Letters , Volume 581 (5) – Mar 6, 2007
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References (41)

Publisher
Wiley
Copyright
© 2015 Federation of European Biochemical Societies
eISSN
1873-3468
DOI
10.1016/j.febslet.2007.01.086
Publisher site
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Abstract

Protein–DNA interactions are crucial to many cellular activities such as expression‐control and DNA‐repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA‐binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA‐binding sites; to improve the prediction by distance‐dependent refinement; use these predictions to identify DNA‐binding proteins. We use a support vector machines (SVM)‐based approach to harness the features of the DNA‐binding residues to distinguish them from non‐binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA‐binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post‐processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA‐binding residues can be used to differentiate DNA‐binding proteins from non‐DNA‐binding proteins with an accuracy of 78%. Results presented here demonstrate that machine‐learning can be applied to automated identification of DNA‐binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site‐directed mutagenesis and macromolecular docking.

Journal

Febs Letters – Wiley

Published: Mar 6, 2007

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