GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments.

ABSTRACT: Thermal proteome profiling (TPP) is a proteome wide technology that enables unbiased detection of protein drug interactions as well as changes in post-translational state of proteins between different biological conditions. Statistical analysis of temperature range TPP (TPP-TR) datasets relies on comparing protein melting curves, describing the amount of non-denatured proteins as a function of temperature, between different conditions (e.g. presence or absence of a drug). However, state-of-the-art models are restricted to sigmoidal melting behaviours while unconventional melting curves, representing up to 50% of TPP-TR datasets, have recently been shown to carry important biological information. We present a novel statistical framework, based on hierarchical Gaussian process models and nam

SUBMITTER: Le Sueur C 

PROVIDER: S-EPMC11463780 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature