PXD061927-1

PXD061927 is an original dataset announced via ProteomeXchange.

Title	Deciphering PTM Crosstalk on Hsp90: Integrating Deep Learning for Drug Sensitivity Prediction
Description	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.
HostingRepository	PRIDE
AnnounceDate	2025-09-08
AnnouncementXML	Submission_2025-09-07_16:05:38.231.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Sarah Backe
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Fusion Lumos

Revision	Datetime	Status	ChangeLog Entry
0	2025-03-17 08:40:31	ID requested
⏵ 1	2025-09-07 16:05:39	announced

10.1016/j.jbc.2025.110519;

Heritz JA, Meluni KA, Backe SJ, Cayaban SJ, Wengert LA, Kunz M, Woodford MR, Bourboulia D, Mollapour M, Integrating deep learning for post-translational modifications crosstalk on Hsp90 and drug binding. J Biol Chem, 301(9):110519(2025) [pubmed]

submitter keyword: Hsp90, AI, Ganetespib, Deep learning, HDAC, Acetylation, Phosphorylation

Mehdi Mollapour
contact affiliation	1 Department of Urology, 2 Upstate Cancer Center, 3 Department of Biochemistry and Molecular Biology, SUNY Upstate Medical University, 750 E. Adams St., Syracuse, NY 13210, USA
contact email	mollapom@uptate.edu
lab head
Sarah Backe
contact affiliation	SUNY Upstate Medical University
contact email	backes@upstate.edu
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/09/PXD061927

PRIDE project URI

• PRIDE
  1. PXD061927
     1. Label: PRIDE project
     2. Name: Deciphering PTM Crosstalk on Hsp90: Integrating Deep Learning for Drug Sensitivity Prediction