Accelerated NMR Spectroscopy: Merge Optimization with Deep Learning

Multi-dimensional NMR spectroscopy is an invaluable biophysical tool in studies of structure, interactions, and dynamics of large molecules like proteins and nuclear acids. Non-uniform sampling is a powerful approach for shortening measurement time and increasing spectra resolution. Several methods have been established for spectra reconstruction from the undersampled data, typical approaches include model-based optimization and data-driven deep learning. The former is well theoretically grounded and provides high-quality spectra, while the latter has a huge advantage in reconstruction time potential and push further limits of spectra quality and analysis. Combining the merits of the two, we propose a model-inspired deep learning, for reliable, high-quality, and ultra-fast spectra reconstruction, exemplified by multi-dimensional spectra of several representative proteins. We demonstrate that the proposed network needs very few parameters, and shows very high robustness in respect to dissimilarity of the training and target data in the spectra size, type, and sampling level. This work can be considered as a proof-of-concept of merging optimization with deep learning in NMR spectroscopy.