Integrating bioinformatics tools to investigate protein phosphorylation

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1 Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece
2 Laboratory of Biomedical Microbiology and Immunology, Department of Microbiology and Immunology, University of Veterinary Medicine and Pharmacy, Komenskeho, Kosice, Slovakia
3 Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
4 IMGT, the international ImMunoGeneTics information system, Institute of Human Genetics, Montpellier, France
DOI
10.7287/peerj.preprints.916v1
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
Vlachakis D, Bencurova E, Bhide M, Kossida S. 2015. Integrating bioinformatics tools to investigate protein phosphorylation. PeerJ PrePrints 3:e916v1

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

DOI: 10.7287/peerj.preprints.916v1/supp-1