Thermal Tracks: A Gaussian process-based framework for universal melting curve analysis enabling unconstrained hit...

Area of Science:

• Proteomics
• Statistical Bioinformatics
• Biophysics

Background:

• Existing thermal proteome profiling (TPP) workflows have limitations in analyzing protein thermal stability data.
• Standard TPP methods often assume sigmoidal melting curves and rely on empirical null distributions, restricting the identification of significant changes.
• These constraints can lead to missed biologically relevant insights, especially in complex biological contexts.

Purpose of the Study:

• To introduce Thermal Tracks, a novel Python-based statistical framework for analyzing protein thermal stability.
• To overcome the limitations of conventional TPP workflows by employing flexible modeling of melting curve shapes.
• To provide an unbiased method for generating null distributions in proteome-wide thermal profiling.

Main Methods:

• Utilizes Gaussian Process (GP) models with squared-exponential kernels for flexible melting curve modeling.
• Generates unbiased null distributions through kernel priors, overcoming empirical constraints.
• Implements the framework in Python for accessibility and broad applicability.

Main Results:

• Thermal Tracks can flexibly model diverse melting curve shapes beyond sigmoidal assumptions.
• The framework generates unbiased null distributions, enhancing the statistical power of TPP studies.
• Successfully analyzes proteins with unconventional thermal stability behaviors, including phase-separating and membrane proteins.

Conclusions:

• Thermal Tracks offers a significant advancement over traditional TPP methods for analyzing protein thermal stability.
• The framework's flexibility and unbiased statistical approach enable more comprehensive insights into proteome-wide thermal perturbations.
• Provides an accessible and powerful tool for researchers studying protein stability under various biological conditions.