The Integration of Proteome-Wide PTM Data with Protein Structural and Sequence Features Identifies Phosphorylations that Mediate 14-3-3 Interactions. Issue 2 (30th January 2023) - Egbert, C.M.; Warr, L.R.; Pennington, K.L.; Thornton, M.M.; Vaughan, A.J.; Ashworth, S.W.; Heaton, M.J.; English, N.; Torres, M.P.; Andersen, J.L.
Graphical abstract: The integration of protein features (amino acid sequence, protein disorder, and PTM observations) into one model helps identify phosphorylations that mediate 14-3-3 interactions. Highlights: 14-3-3 interacts with a large network of interactions to control core mechanisms of molecular biology, but mechanistic understanding of these interactions requires identifying the phosphorylations that mediate 14-3-3 binding—currently a costly and time-consuming challenge. Global analysis of 14-3-3 docking site phosphorylations indicates that structural and MS-based observation frequency of PTMs can be used as predictive variables (in addition to amino acid sequence) to identify 14-3-3 docking site phosphorylations. These protein features are incorporated into a publicly available web app (14-3-3 site-finder) for the PTM and 14-3-3 communities to use in identifying 14-3-3 docking site phosphorylations. We demonstrate the value of the approach by identifying 14-3-3 docking site phosphorylations that are not predicted by sequence alone, including a phosphorylation at T2467 on the RhoGEF AKAP13 that mediates 14-3-3 binding. Abstract: 14-3-3s are abundant proteins that regulate essentially all aspects of cell biology, including cell cycle, motility, metabolism, and cell death. 14-3-3s work by docking to phosphorylated Ser/Thr residues on a large network of client proteins and modulating client protein function in a variety of ways. In recent years, aided by improvements in proteomics, the discovery of 14-3-3 client proteins has far outpaced our ability to understand the biological impact of individual 14-3-3 interactions. The rate-limiting step in this process is often the identification of the individual phospho-serines/threonines that mediate 14-3-3 binding, which are difficult to distinguish from other phospho-sites by sequence alone. Furthermore, trial-and-error molecular approaches to identify these phosphorylations are costly and can take months or years to identify even a single 14-3-3 docking site phosphorylation. To help overcome this challenge, we used machine learning to analyze predictive features of 14-3-3 binding sites. We found that accounting for intrinsic protein disorder and the unbiased mass spectrometry identification rate of a given phosphorylation significantly improves the identification of 14-3-3 docking site phosphorylations across the proteome. We incorporated these features, coupled with consensus sequence prediction, into a publicly available web app, called " 14 - 3-3 site-finder" . We demonstrate the strength of this approach through its ability to identify 14-3-3 binding sites that do not conform to the loose consensus sequence of 14-3-3 docking phosphorylations, which we validate with 14-3-3 client proteins, including TNK1, CHEK1, MAPK7, and others. In addition, by using this approach, we identify a phosphorylation on A-kinase anchor protein-13 (AKAP13) at Ser2467 that dominantly controls its interaction with 14-3-3.