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

Fábio Madeira, Michele Tinti, Gavuthami Murugesan, Emily Berrett, Margaret Stafford, Rachel Toth, Christian Cole, Carol MacKintosh, Geoffrey J. Barton (Lead / Corresponding author)

    Research output: Contribution to journalArticlepeer-review

    137 Citations (Scopus)
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    Motivation: The 14-3-3 family of phosphoprotein-binding proteins regulate 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 (PSSM), 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, PSSM 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: A standalone prediction webserver 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.

    Contact: cmackintosh@dundee.ac.uk and gjbarton@dundee.ac.uk

    Supplementary information: Supplementary data are available at Bioinformatics online.

    Original languageEnglish
    Early online date3 Mar 2015
    Publication statusPublished - 2015


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