PSPhos: PK-Specific Phosphorylation Site Prediction Using Profile SVM
Because of the importance of phosphorylation and the mystery of the corresponding mechanism in vivo, predicting potential phosphorylation sites with protein kinases (PKs) information is a critical and intriguing challenge in bioinformatics. Although some in silico methods have been introduced for this purpose, sophisticated methods are still in urgent demand to further improve prediction performances. In this paper, we present a novel, versatile and comprehensive program, PSPhos (Profile SVM for Phosphorylation site prediction), based on profile Support Vector Machine. We estimated the performance of PSPhos using experimentally verified substrate sites of two important PK families: ABL and DNA-PK, and compared the performance with the GPS and PPSP algorithms. With the Jack-knife tests, PSPhos consistently performed ∼20% better in sensitivity. Then a more comprehensive comparison between PSPhos and most available web servers for phosphorylation prediction was made on two manually-curated testing data of novel substrate sites. With a high specificity of 100%, the sensitivity obtained by PSPhos on the ABL data set was 16% higher than the best result obtained by several existing methods. On DNA-PK data, only PSPhos could successfully identify true phosphorylation sites with high stringency for minimal false positives. All these results indicate that PSPhos is a useful computational resource for the identification of PK-specific phosphorylation sites, and can provide helpful insights for future experimental design and verification.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
Document Type: Research Article
Publication date: 2007-11-01
More about this publication?
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
- Editorial Board
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites