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Nuclear Magnetic Resonance (NMR) spectroscopy is one of the major techniquesin structural biology with over 11,800 protein structures deposited in theProtein Data Bank. NMR can elucidate structures and dynamics of small andmedium size proteins in solution, living cells, and solids, but has beenlimited by the tedious data analysis process. It typically requires weeks ormonths of manual work of a trained expert to turn NMR measurements into aprotein structure. Automation of this process is an open problem, formulated inthe field over 30 years ago. Here, we present a solution to this challenge thatenables the completely automated analysis of protein NMR data within hoursafter completing the measurements. Using only NMR spectra and the proteinsequence as input, our machine learning-based method, ARTINA, delivers signalpositions, resonance assignments, and structures strictly without any humanintervention. Tested on a 100-protein benchmark comprising 1329multidimensional NMR spectra, ARTINA demonstrated its ability to solvestructures with 1.44 {\AA} median RMSD to the PDB reference and to identify91.36% correct NMR resonance assignments. ARTINA can be used by non-experts,reducing the effort for a protein assignment or structure determination by NMRessentially to the preparation of the sample and the spectra measurements.

Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA