Abstract
The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.
Highlights
The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications
The guiding principle of these experiments is that heating generally causes proteins to denature and become insoluble. This heating can be performed at various temperatures and the remaining soluble protein quantified by mass-spectrometry (MS)
Thermodynamic theory predicts that the melting curve of proteins should have a sigmoid behaviour[27]
Summary
The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. The guiding principle of these experiments is that heating generally causes proteins to denature and become insoluble This heating can be performed at various temperatures and the remaining soluble protein quantified by mass-spectrometry (MS). One approach involves summarising melting curves into a Tm-the temperature at which relative solubility has halved[1,5] This is followed by comparison of Tm values across the two contexts using the appropriate z-score. A more powerful approach is to employ techniques from functional data analysis[30,31,32] and use the whole melting curve for statistics[29]
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