Work

Deep Learning for Hyperpolarized NMR of Intrinsically Disordered Proteins without Resolution Loss: Access to Short‐Lived Intermediates

Year: 2025

Type: article

Abstract: The inherently low sensitivity of solution-state Nuclear Magnetic Resonance (NMR) has long limited its ability to characterize transient biomolecular states at atomic resolution. While dissolution dynamic nuclear polarization (dDNP) offers a compensating signal enhancement, its broader use has been hindered by rapid polarization decay, causing severe spectral distortion. Here, we introduce HyperW-Decon, an approach that enables high-sensitivity, high-resolution NMR of biomolecules in solution. HyperW-Decon combines two key aspects: (i) the use of hyperpolarized water (HyperW) to transfer polarization to proteins through rapid proton exchange; and (ii) a theory-driven, machine learning (ML)-based deconvolution method that corrects polarization-induced artifacts without requiring external reference signals. This approach is based on a first-principles understanding of dDNP line shapes and delivers a scalable solution to spectral distortion. Applied to intrinsically disordered proteins (IDPs) involved in biomineralization, HyperW-Decon reveals previously inaccessible, short-lived ion-peptide encounter complexes with residue resolution.

Source: Chemistry - A European Journal

DOI: 10.1002/chem.202502067

Authors: Ertan Turhan, Christopher Pötzl, Dörte Brandis, Federico Faglia, Milan Zachrdla, Dennis Kurzbach

Institution: University of Vienna

Language: English

Topic: Advanced NMR Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,640

Funders: Austrian Science Fund, Universität Wien, Österreichische Forschungsgemeinschaft