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

Bioinformatics
Fábio MadeiraGeoffrey J Barton

Abstract

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. 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 ...Continue Reading

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Datasets Mentioned

BETA
AAH65225.1

Methods Mentioned

BETA
coimmunoprecipitation

Software Mentioned

ANIA
JABAWS
ANN
Netphorest Scansite
Netphorest
DisEMBL
ROCR
ANIA Integrated Analysis
R package RSNNS
PyML

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