{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T16:31:03Z","timestamp":1744302663770},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":579,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,7,15]]},"abstract":"Abstract<\/jats:title>\n 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.<\/jats:p>\n 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 \u22126 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.<\/jats:p>\n 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.<\/jats:p>\n Contact: \u00a0cmackintosh@dundee.ac.uk or gjbarton@dundee.ac.uk<\/jats:p>\n Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv133","type":"journal-article","created":{"date-parts":[[2015,3,4]],"date-time":"2015-03-04T01:52:31Z","timestamp":1425433951000},"page":"2276-2283","source":"Crossref","is-referenced-by-count":194,"title":["14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides"],"prefix":"10.1093","volume":"31","author":[{"given":"F\u00e1bio","family":"Madeira","sequence":"first","affiliation":[{"name":"1 Division of Computational Biology,"}]},{"given":"Michele","family":"Tinti","sequence":"additional","affiliation":[{"name":"2 Division of Cell and Developmental Biology,"}]},{"given":"Gavuthami","family":"Murugesan","sequence":"additional","affiliation":[{"name":"1 Division of Computational Biology,"}]},{"given":"Emily","family":"Berrett","sequence":"additional","affiliation":[{"name":"1 Division of Computational Biology,"}]},{"given":"Margaret","family":"Stafford","sequence":"additional","affiliation":[{"name":"3 MRC Protein Phosphorylation Unit,"}]},{"given":"Rachel","family":"Toth","sequence":"additional","affiliation":[{"name":"4 Division of Signal Transduction Therapy,"}]},{"given":"Christian","family":"Cole","sequence":"additional","affiliation":[{"name":"1 Division of Computational Biology,"}]},{"given":"Carol","family":"MacKintosh","sequence":"additional","affiliation":[{"name":"2 Division of Cell and Developmental Biology,"}]},{"given":"Geoffrey J.","family":"Barton","sequence":"additional","affiliation":[{"name":"1 Division of Computational Biology,"},{"name":"5 Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, Scotland, UK"}]}],"member":"286","published-online":{"date-parts":[[2015,3,3]]},"reference":[{"key":"2023020202151732700_btv133-B1","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1093\/bioinformatics\/bti443","article-title":"Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces","volume":"21","author":"Aytuna","year":"2005","journal-title":"Bioinformatics"},{"key":"2023020202151732700_btv133-B2","doi-asserted-by":"crossref","first-page":"e1000173","DOI":"10.1371\/journal.pcbi.1000173","article-title":"Support vector machines and kernels for computational biology","volume":"4","author":"Ben-Hur","year":"2008","journal-title":"PLoS Comput. 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