An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments

We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of an homologous structure. Our algorithm performs Nuclear Vector Replacement (NVR) by Expectation/ Maximization ( EM) to compute assignments. NVR correlates experimentally-measured NH residual dipolar couplings (RDCs) and chemical shifts to a given a priori whole-protein 3D structural model. The algorithm requires only uniform N-15-labelling of the protein, and processes unassigned H-N-N-15 HSQC spectra, H-N-N-15 RDCs, and sparse H-N-H-N NOE's (d(NN)s). NVR runs in minutes and efficiently assigns the (H-N, N-15) backbone resonances as well as the sparse dNNs from the 3D N-15-NOESY spectrum, in O(n(3)) time. The algorithm is demonstrated on NMR data from a 76-residue protein, human ubiquitin, matched to four structures, including onemutant ( homolog), determined either by X-ray crystallography or by different NMR experiments ( without RDCs). NVR achieves an average assignment accuracy of over 99%. We further demonstrate the feasibility of our algorithm for different and larger proteins, using different combinations of real and simulated NMR data for hen lysozyme ( 129 residues) and streptococcal protein G ( 56 residues), matched to a variety of 3D structural models.