14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides | Zendy

Fábio Madeira | Zendy; Michele Tinti | Zendy; Gavuthami Murugesan | Zendy; Emily Berrett | Zendy; Margaret J. Stafford | Zendy; Rachel Toth | Zendy; Christian Cole | Zendy; Carol MacKintosh | Zendy; Geoffrey J. Barton | Zendy

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14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides

Author(s) -

Fábio Madeira,

Michele Tinti,

Gavuthami Murugesan,

Emily Berrett,

Margaret J. Stafford,

Rachel Toth,

Christian Cole,

Carol MacKintosh,

Geoffrey J. Barton

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv133

Subject(s) - phosphoprotein , support vector machine , computer science , computational biology , artificial neural network , artificial intelligence , data mining , machine learning , biology , phosphorylation , biochemistry

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.

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