Integrating bioinformatics tools to investigate protein phosphorylation
- Published
- Accepted
- Subject Areas
- Bioinformatics, Biophysics, Computational Biology
- Keywords
- Protein Phosphorylation, Bioinformatics, Post-Translational Modifications, Benchmark, Phosphorylation Prediction
- Copyright
- © 2015 Vlachakis et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Integrating bioinformatics tools to investigate protein phosphorylation. PeerJ PrePrints 3:e916v1 https://doi.org/10.7287/peerj.preprints.916v1
Abstract
Protein phosphorylation is one of the most important protein post-translational modifications and plays a role in numerous cellular processes including recognition, signaling and degradation. It can be studied experimentally by various methodologies, like employing western blot analysis, site-directed mutagenesis, 2 D gel electrophoresis, mass spectrometry etc. A number of in silico tools have also been developed in order to predict plausible phosphorylation sites in a given protein. In this review, we conducted a benchmark study including the leading protein phosphorylation prediction software, in an effort to determine which performs best. The first place was taken by GPS 2.2, having predicted all phosphorylation sites with a 83% fidelity while in second place came NetPhos 2.0 with 69%.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Raw data
The original phosphorylation data for the reviewers to evaluate