Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat.

We describe a two-dimensional thermal proteome profiling strategy that can be combined with an orthogonal chemoproteomics approach to enable comprehensive target profiling of the marketed histone deacetylase inhibitor panobinostat. The N-hydroxycinnamide moiety is identified as critical for potent and tetrahydrobiopterin-competitive inhibition of phenylalanine hydroxylase leading to increases in phenylalanine and decreases in tyrosine levels. These findings provide a rationale for adverse clinical observations and suggest repurposing of the drug for treatment of tyrosinemia.

Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry.

The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.