GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments
Fig 5
Including non-sigmoidal melting curves in peptide-level TPP-TR datasets largely increases the number of discoveries.
Functionally relevant phosphosites are expected to induce a change in melting behaviour by influencing, among others, protein conformations and protein-protein interactions. Mono-phosphorylated peptides functionality can be predicted using the functional score, a machine-learning based score [36], ranging from 0 to 1, with larger values indicating more functionally relevant phosphosites. To detect functionally relevant phosphosites, the melting behaviour of phosphorylated peptides are compared to the melting behaviour of the non-phosphorylated peptides associated to the same entry in the protein database. GPMelt with a three-level HGP model is used to reanalyse the phospho-TPP dataset [11]. (A) Considering non-conventional melting curves in the analysis makes it possible to include almost twice (1.78) as many phospho-peptides compared to the published melting point (Tm) analysis. (B) By increasing the phospho-peptides coverage, GPMelt captures about five times more (4.9) mono-phosphorylated peptides than the published Tm analysis, and captures phosphosites associated with significantly higher functional scores than non-captured phosphosites (one-sided Wilcoxon signed-rank test). GPMelt hit selection: any phospho-peptide for which the associated Λ value is so extreme that it is strictly above any values belonging to the null distribution approximation (S = 1e4 samples per protein). 443 mono-phosphorylated peptides are selected by GPMelt, among which 388 have an associated functional score. The Tm analysis selects 90 mono-phosphorylated peptides, with 85 presenting a known functional score.