We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides.
- Authors
Madeira, Fábio; Tinti, Michele; Murugesan, Gavuthami; Berrett, Emily; Stafford, Margaret; Toth, Rachel; Cole, Christian; MacKintosh, Carol; Barton, Geoffrey J.
- 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 þ4 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.
- Subjects
14-3-3 proteins; PHOSPHOPEPTIDES; PHOSPHOPROTEINS; SERINE; THREONINE; SUPPORT vector machines; ARTIFICIAL neural networks
- Publication
Bioinformatics, 2015, Vol 31, Issue 14, p2276
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btv133