Inferential backbone assignment for sparse data.

This paper develops an approach to protein backbone NMR assignment that effectively assigns large proteins while using limited sets of triple-resonance experiments. Our approach handles proteins with large fractions of missing data and many ambiguous pairs of pseudoresidues, and provides a statistical assessment of confidence in global and position-specific assignments. The approach is tested on an extensive set of experimental and synthetic data of up to 723 residues, with match tolerances of up to 0.5 ppm for Calpha and Cbeta resonance types. The tests show that the approach is particularly helpful when data contain experimental noise and require large match tolerances. The keys to the approach are an empirical Bayesian probability model that rigorously accounts for uncertainty in the data at all stages in the analysis, and a hybrid stochastic tree-based search algorithm that effectively explores the large space of possible assignments.