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Bioinformatics. 2015 Jul 15;31(14):2276-83. doi: 10.1093/bioinformatics/btv133. Epub 2015 Mar 3.

14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides.

Author information

1
Division of Computational Biology.
2
Division of Cell and Developmental Biology.
3
MRC Protein Phosphorylation Unit.
4
Division of Signal Transduction Therapy.
5
Division of Computational Biology, Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, UK.

Abstract

MOTIVATION:

The 14-3-3 family of phosphoprotein-binding proteins regulates many cellular processes by docking onto pairs of phosphorylated Ser and Thr residues in a constellation of intracellular targets. Therefore, there is a pressing need to develop new prediction methods that use an updated set of 14-3-3-binding motifs for the identification of new 14-3-3 targets and to prioritize the downstream analysis of >2000 potential interactors identified in high-throughput experiments.

RESULTS:

Here, a comprehensive set of 14-3-3-binding targets from the literature was used to develop 14-3-3-binding phosphosite predictors. Position-specific scoring matrix, support vector machines (SVM) and artificial neural network (ANN) classification methods were trained to discriminate experimentally determined 14-3-3-binding motifs from non-binding phosphopeptides. ANN, position-specific scoring matrix and SVM methods showed best performance for a motif window spanning from -6 to +4 around the binding phosphosite, achieving Matthews correlation coefficient of up to 0.60. Blind prediction showed that all three methods outperform two popular 14-3-3-binding site predictors, Scansite and ELM. The new methods were used for prediction of 14-3-3-binding phosphosites in the human proteome. Experimental analysis of high-scoring predictions in the FAM122A and FAM122B proteins confirms the predictions and suggests the new 14-3-3-predictors will be generally useful.

AVAILABILITY AND IMPLEMENTATION:

A standalone prediction web server is available at http://www.compbio.dundee.ac.uk/1433pred. Human candidate 14-3-3-binding phosphosites were integrated in ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome database.

PMID:
25735772
PMCID:
PMC4495292
DOI:
10.1093/bioinformatics/btv133
[Indexed for MEDLINE]
Free PMC Article

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