期刊
BIOINFORMATICS
卷 31, 期 14, 页码 2276-2283出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv133
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资金
- Wellcome Trust [100150/Z/12/Z, WT09230, WT083481, WT097945]
- Medical Research Council Developmental Pathway Funding Scheme [G0801767]
- AstraZeneca
- Boehringer-Ingelheim
- GlaxoSmithKline
- Merck KgaA
- Janssen Pharmaceutica
- Pfizer
- Wellcome Trust [100150/Z/12/Z] Funding Source: Wellcome Trust
- Diabetes UK [12/0004557] Funding Source: researchfish
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.
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