14-3-3-Pred: Improved methods to predict 14-3-3-binding phosphopeptides. Madeira, F., Tinti, M., Murugesan, G., Berrett, E., Stafford, M., Toth, R., Cole, C., MacKintosh, C., & Barton, G. J. Bioinformatics, March, 2015. ![14-3-3-Pred: Improved methods to predict 14-3-3-binding phosphopeptides [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 \textbackslashtextgreater2000 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.
@article{madeira_14-3-3-pred:_2015,
title = {14-3-3-{Pred}: {Improved} methods to predict 14-3-3-binding phosphopeptides},
issn = {1367-4803, 1460-2059},
shorttitle = {14-3-3-{Pred}},
url = {http://bioinformatics.oxfordjournals.org/content/early/2015/03/03/bioinformatics.btv133},
doi = {10.1093/bioinformatics/btv133},
abstract = {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 {\textbackslash}textgreater2000 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.},
language = {en},
urldate = {2015-03-04},
journal = {Bioinformatics},
author = {Madeira, Fábio and Tinti, Michele and Murugesan, Gavuthami and Berrett, Emily and Stafford, Margaret and Toth, Rachel and Cole, Christian and MacKintosh, Carol and Barton, Geoffrey J.},
month = mar,
year = {2015},
keywords = {14-3-3, Bioinformatics, mine, prediction},
pages = {btv133}
}
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