14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides. (3rd March 2015)

• Record Type: Journal Article
• Authors: Madeira, Fábio; Tinti, Michele; Murugesan, Gavuthami; Berrett, Emily; Stafford, Margaret; Toth, Rachel; Cole, Christian; MacKintosh, Carol; Barton, Geoffrey J.
• Is Part Of: Bioinformatics. Volume 31:Number 14(2015)
• Page Start: 2276
• Page End: 2283
• Publication Date: 2015-03-03
• Subjects: Bioinformatics -- Periodicals; Genomics -- Data processing -- Periodicals; Computational biology -- Periodicals; 572.80285
• DOI: 10.1093/bioinformatics/btv133
• Languages: English
• ISSNs: 1367-4803

Abstract

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. Contact: [e-mail] or [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.