  A new deep neural network based on the WaveNet architecture (WNN) is
presented, which is designed to grasp specific patterns in the NMR spectra.
When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits
from pattern recognition of the corresponding point spread function (PSF)
pattern produced by each spectral peak resulting in the highest quality and
robust reconstruction of the NUS spectra as demonstrated in simulations and
exemplified in this work on 2D 1H-15N correlation spectra of three
representative globular proteins with different sizes: Ubiquitin (8.6 kDa),
Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also
demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13 HMQC
spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS
schedule, which so far was not fully exploited, can be used for designing new
powerful NMR processing techniques that surpass the existing algorithmic
methods.
