Integrating deep learning for post-translational modifications crosstalk on Hsp90 and drug binding

Post-translational modification (PTM) of proteins regulates cellular proteostasis by expanding protein functional diversity. This naturally leads to increased proteome complexity as a result of PTM crosstalk. Here, we used the molecular chaperone protein, Heat shock protein-90 (Hsp90), which is subj...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	The Journal of biological chemistry Ročník 301; číslo 9; s. 110519
Hlavní autoři:	Heritz, Jennifer A., Meluni, Katherine A., Backe, Sarah J., Cayaban, Sara J., Wengert, Laura A., Kunz, Meik, Woodford, Mark R., Bourboulia, Dimitra, Mollapour, Mehdi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.09.2025 American Society for Biochemistry and Molecular Biology
Témata:	Acetylation artificial intelligence chaperone cochaperone Deep Learning histone deacetylase Histone Deacetylases - genetics Histone Deacetylases - metabolism Hsp90 HSP90 Heat-Shock Proteins - antagonists & inhibitors HSP90 Heat-Shock Proteins - chemistry HSP90 Heat-Shock Proteins - genetics HSP90 Heat-Shock Proteins - metabolism Humans Phosphorylation Protein Binding Protein Processing, Post-Translational Repressor Proteins GIST cochaperone CML KO AI PTM histone deacetylase artificial intelligence Hsp90 acetylation deep learning Q R chaperone MIND-S Bio-GB GB phosphorylation WT HDAC
ISSN:	0021-9258, 1083-351X, 1083-351X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Post-translational modification (PTM) of proteins regulates cellular proteostasis by expanding protein functional diversity. This naturally leads to increased proteome complexity as a result of PTM crosstalk. Here, we used the molecular chaperone protein, Heat shock protein-90 (Hsp90), which is subject to a plethora of PTMs, 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 demonstrated that deletion of histone deacetylase 3 (HDAC3) and histone deacetylase 8 (HDAC8) in human cells led to increased binding of Hsp90 to both ATP and its ATP-competitive inhibitor, Ganetespib. When bound to this 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work.
ISSN:	0021-9258 1083-351X 1083-351X
DOI:	10.1016/j.jbc.2025.110519