These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
2 files

Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks

submitted on 23.03.2021, 14:13 and posted on 24.03.2021, 07:51 by Gogulan Karunanithy, Harry Mackenzie, Flemming Hansen
Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains.


Developing Artificial Intelligence and Deep Learning for the analysis of correlation spectroscopy data

Biotechnology and Biological Sciences Research Council

Find out more...

Characterising structure, interactions and dynamics of large molecular machines and intrinsically disordered proteins using novel carbon-detected NMR

Biotechnology and Biological Sciences Research Council

Find out more...

The UCL Integrated NMR Centre:Bridging fine-scale analysis of macromolecules and cell physiology with high-throughput population scale metabolomics, drug target validation and biomarker development .

Wellcome Trust

Find out more...


Email Address of Submitting Author


University College London


United Kingdom

ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest