  Nuclear Magnetic Resonance (NMR) spectroscopy is one of the major techniques
in structural biology with over 11,800 protein structures deposited in the
Protein Data Bank. NMR can elucidate structures and dynamics of small and
medium size proteins in solution, living cells, and solids, but has been
limited by the tedious data analysis process. It typically requires weeks or
months of manual work of a trained expert to turn NMR measurements into a
protein structure. Automation of this process is an open problem, formulated in
the field over 30 years ago. Here, we present a solution to this challenge that
enables the completely automated analysis of protein NMR data within hours
after completing the measurements. Using only NMR spectra and the protein
sequence as input, our machine learning-based method, ARTINA, delivers signal
positions, resonance assignments, and structures strictly without any human
intervention. Tested on a 100-protein benchmark comprising 1329
multidimensional NMR spectra, ARTINA demonstrated its ability to solve
structures with 1.44 {\AA} median RMSD to the PDB reference and to identify
91.36% correct NMR resonance assignments. ARTINA can be used by non-experts,
reducing the effort for a protein assignment or structure determination by NMR
essentially to the preparation of the sample and the spectra measurements.
