@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 {\textgreater}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.},
  language = {en},
  urldate = {2015-03-04TZ},
  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 = {March},
  year = {2015},
  keywords = {14-3-3, Bioinformatics, mine, prediction},
  pages = {btv133}
}

{"_id":"FprDZGW46xrRWXwsK","bibbaseid":"madeira-tinti-murugesan-berrett-stafford-toth-cole-mackintosh-barton-1433predimprovedmethodstopredict1433bindingphosphopeptides-2015","downloads":0,"creationDate":"2015-05-20T11:21:44.937Z","title":"14-3-3-Pred: Improved methods to predict 14-3-3-binding phosphopeptides","author_short":["Madeira, F.","Tinti, M.","Murugesan, G.","Berrett, E.","Stafford, M.","Toth, R.","Cole, C.","MacKintosh, C.","Barton, G.<nbsp>J."],"year":2015,"bibtype":"article","biburl":"https://api.zotero.org/groups/352526/items?key=QwX3sH6GOtXLqQKqvgcDGTL0&format=bibtex&limit=100","bibdata":{"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 \\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.","author":["Madeira, Fábio","Tinti, Michele","Murugesan, Gavuthami","Berrett, Emily","Stafford, Margaret","Toth, Rachel","Cole, Christian","MacKintosh, Carol","Barton, Geoffrey J."],"author_short":["Madeira, F.","Tinti, M.","Murugesan, G.","Berrett, E.","Stafford, M.","Toth, R.","Cole, C.","MacKintosh, C.","Barton, G.<nbsp>J."],"bibtex":"@article{ madeira_14-3-3-pred:_2015,\n title = {14-3-3-{Pred}: {Improved} methods to predict 14-3-3-binding phosphopeptides},\n issn = {1367-4803, 1460-2059},\n shorttitle = {14-3-3-{Pred}},\n url = {http://bioinformatics.oxfordjournals.org/content/early/2015/03/03/bioinformatics.btv133},\n doi = {10.1093/bioinformatics/btv133},\n 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 {\\textgreater}2000 potential interactors identified in high-throughput experiments.\nResults: 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.\nAvailability: 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.\nContact: cmackintosh@dundee.ac.uk and gjbarton@dundee.ac.uk\nSupplementary information: Supplementary data are available at Bioinformatics online.},\n language = {en},\n urldate = {2015-03-04TZ},\n journal = {Bioinformatics},\n 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.},\n month = {March},\n year = {2015},\n keywords = {14-3-3, Bioinformatics, mine, prediction},\n pages = {btv133}\n}","bibtype":"article","doi":"10.1093/bioinformatics/btv133","id":"madeira_14-3-3-pred:_2015","issn":"1367-4803, 1460-2059","journal":"Bioinformatics","key":"madeira_14-3-3-pred:_2015","keywords":"14-3-3, Bioinformatics, mine, prediction","language":"en","month":"March","pages":"btv133","shorttitle":"14-3-3-Pred","title":"14-3-3-Pred: Improved methods to predict 14-3-3-binding phosphopeptides","type":"article","url":"http://bioinformatics.oxfordjournals.org/content/early/2015/03/03/bioinformatics.btv133","urldate":"2015-03-04TZ","year":"2015","bibbaseid":"madeira-tinti-murugesan-berrett-stafford-toth-cole-mackintosh-barton-1433predimprovedmethodstopredict1433bindingphosphopeptides-2015","role":"author","urls":{"Paper":"http://bioinformatics.oxfordjournals.org/content/early/2015/03/03/bioinformatics.btv133"},"keyword":["14-3-3","Bioinformatics","mine","prediction"],"downloads":0},"search_terms":["pred","improved","methods","predict","binding","phosphopeptides","madeira","tinti","murugesan","berrett","stafford","toth","cole","mackintosh","barton"],"keywords":["14-3-3","bioinformatics","mine","prediction"],"authorIDs":[],"dataSources":["7zTitphxmFtzrdB7A"]}