Deciphering PTM Crosstalk on Hsp90: Integrating Deep Learning for Drug Sensitivity Prediction

Post-translational modification (PTM) of proteins regulates cellular proteostasis by expanding protein functional diversity. This naturally leads to increased proteome complexity as the result of PTM crosstalk. Here, we used a heavily modified molecular chaperone, Heat shock protein-90 (Hsp90), to investigate this concept. Hsp90 is at the hub of proteostasis and cellular signaling networks in cancer and is, therefore, an attractive therapeutic target in cancer. We showed that deletion of HDAC3 and HDAC8 in human cells led to increased binding of Hsp90 to both ATP and drugs. When bound to its ATP-competitive inhibitor, Hsp90 from both HDAC3 and HDAC8 knock out human cells exhibited similar PTMs, mainly phosphorylation and acetylation, and created a common proteomic network signature. We used both a deep-learning artificial intelligence (AI) prediction model and data based on mass-spectrometry analysis of Hsp90 isolated from the mammalian cells bound to its drugs to decipher PTM crosstalk. The alignment of data from both methods demonstrates that the deep-learning prediction model offers a highly efficient and rapid approach for deciphering PTM crosstalk on complex proteins such as Hsp90.