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


METHODS
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.
Division of Computational Biology.
Fábio
F
Madeira
Division of Cell and Developmental Biology.
Michele
M
Tinti
Division of Computational Biology.
Gavuthami
G
Murugesan
Division of Computational Biology.
Emily
E
Berrett
MRC Protein Phosphorylation Unit.
Margaret
M
Stafford
Division of Signal Transduction Therapy.
Rachel
R
Toth
Division of Computational Biology.
Christian
C
Cole
Division of Cell and Developmental Biology.
Carol
C
MacKintosh
Division of Computational Biology, Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, UK.
Geoffrey J
GJ
Barton
D048948:14-3-3 Proteins / Q000378:metabolism*; D020816:Amino Acid Motifs; D001665:Binding Sites; D057809:HEK293 Cells; D006801:Humans; D016571:Neural Networks, Computer; D010748:Phosphopeptides / Q000737:chemistry / Q000378:metabolism*; D010750:Phosphoproteins / Q000737:chemistry / Q000378:metabolism*; D056510:Position-Specific Scoring Matrices; D020543:Proteome / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012984:Software; D060388:Support Vector Machine
D048948:14-3-3 Proteins; D010748:Phosphopeptides; D010750:Phosphoproteins; D020543:Proteome