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...

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Veröffentlicht in:The Journal of biological chemistry Jg. 301; H. 9; S. 110519
Hauptverfasser: Heritz, Jennifer A., Meluni, Katherine A., Backe, Sarah J., Cayaban, Sara J., Wengert, Laura A., Kunz, Meik, Woodford, Mark R., Bourboulia, Dimitra, Mollapour, Mehdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.09.2025
American Society for Biochemistry and Molecular Biology
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
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These authors contributed equally to this work.
ISSN:0021-9258
1083-351X
1083-351X
DOI:10.1016/j.jbc.2025.110519