Rapid protein structure assessment via a forward model for NMR spectra

The revolution in protein structure prediction by deep learning offers tremendous opportunities to accelerate biological discovery, provided that models can be validated with experimental data. NMR spectroscopy is a powerful biophysical technique to probe structure and dynamics of proteins at an atomic level. Traditional NMR methods require collection and interpretation of several large experimental data sets to calculate a structure. To reduce the amount of experimental data and accelerate analysis, we present a novel forward model for simulating NMR spectra from protein structures and use image analysis to score similarity between simulation and experiment. We have developed a software interface and analysis pipeline, BPHON (a ChimeraX extension), and tested its performance on standard protein data sets. We then apply BPHON to challenging, real-world experimental use cases, including a transporter membrane protein EmrE and alpha-synuclein fibrils observed in neurological disease. From the sets of candidate structures, BPHON quantitatively ranks the agreement with the experimental data, enabling high-resolution structure refinement for membrane proteins and classification of fibril samples from Parkinson disease subjects.