PXD069693 is an
original dataset announced via ProteomeXchange.
Dataset Summary
| Title | A machine learning method for calculating highly localized protein stabilities |
| Description | The residue-level free energy of opening (∆Gop) is the ultimate thermodynamic descriptor of localized protein stability, providing valuable information about the protein ensemble at physiologically relevant timescales and conditions. PFNet instantly determines ΔGop for arbitrarily large proteins and complexes from conventional peptide-level hydrogen exchange/mass spectrometry (HX/MS) datasets. It unlocks the full potential of HX/MS, democratizing the method and establishing quantitative, scalable and accessible analysis (https://github.com/glasgowlab/PFNet). Benchmark proteins in this dataset include AR61, the T4 lysozyme WT*, and GR3, the de novo designed mini-protein HHH_rd4_0557. |
| HostingRepository | PRIDE |
| AnnounceDate | 2026-04-10 |
| AnnouncementXML | Submission_2026-04-10_10:00:49.212.xml |
| DigitalObjectIdentifier | |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Chenlin Lu |
| SpeciesList | scientific name: Escherichia phage T4; NCBI TaxID: NEWT:2681598; |
| ModificationList | No PTMs are included in the dataset |
| Instrument | maXis |
Dataset History
| Revision | Datetime | Status | ChangeLog Entry |
| 0 | 2025-10-20 18:52:09 | ID requested | |
| ⏵ 1 | 2026-04-10 10:00:49 | announced | |
Publication List
| Dataset with its publication pending |
Keyword List
Contact List
| Anum Glasgow |
| contact affiliation | Columbia University, Dept. Biochemistry/Mol. Biophysics |
| contact email | ag4522@cumc.columbia.edu |
| lab head | |
| Chenlin Lu |
| contact affiliation | Columbia University Irving Medical Center |
| contact email | lucltsinghua@gmail.com |
| dataset submitter | |
Full Dataset Link List
Dataset FTP location
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| PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD069693
- Label: PRIDE project
- Name: A machine learning method for calculating highly localized protein stabilities