The Integration of Proteome-Wide PTM Data with Protein Structural and Sequence Features Identifies Phosphorylations that Mediate 14-3-3 Interactions.

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.

Identification of biomarkers in Lewy-body disorders.

Dementia with Lewy bodies (DLB) may account for up to 30% of all dementia cases. The symptoms of DLB can be difficult to disentangle from other dementia subtypes, particularly Alzheimer's disease (AD). AD and DLB pathologies often overlap within individuals. Like DLB, Parkinson's disease dementia (PDD) also shares common features with DLB. Currently, whether an individual is diagnosed with PDD or DLB depends solely on the timing of symptom onset. Early, accurate diagnosis is needed for optimal management and treatment. It is hoped that the development of existing and new Lewy body disorders biomarkers will facilitate more accurate diagnosis. Reduced dopamine transporter levels in DLB as shown with [123I]FP-CIT-SPECT currently appears to be the most reliable and valid biomarker, although other (predominantly imaging-based) methods also appear to have the high sensitivity and specificity required for a good biomarker. This includes (in DLB compared to AD) reduced cardiac 123I-MIBG uptake, occipital hypometabolism on FDG-PET and preservation of medial temporal lobe structures on CT/MRI. Perfusion SPECT, cerebrospinal fluid protein levels (amyloid, tau and α-synuclein), electroencephalography, saccadic eye movement tracking and 11C-PiB amyloid imaging also hold promise as biomarkers in terms of differentiating DLB, AD, PDD and other neurodegenerative disorders, although findings are less consistent. Studies utilising a combination approach in which two or more potential biomarkers are compared seem to provide very good sensitivity and specificity. In general, longitudinal studies, pathological confirmation of diagnosis and the combined approach may hold the most promise for the identification of biomarkers.