Robust Structure-based Protein NMR Assignments using Native Structural Ensembles
Time: November 12, 2007, 1pm - 2pm
Place: D344, LSRC
Speaker:Mehmet Serkan Apaydin

An important step in NMR protein structure determination is the assignment of resonances and NOEs to corresponding nuclei. Structure-based assignment (SBA) uses a model structure (``template'') for the target protein to expedite this process. Nuclear Vector Replacement (NVR) is an SBA framework that combines multiple sources of NMR data and has high accuracy when the template is close to the target protein's structure (less than 2 Angstroms backbone RMSD). However, a close template may not always be available. We extend the circle of convergence of NVR for distant templates by using an ensemble of structures. This ensemble corresponds to the low-frequency perturbations of the given template and is obtained using Normal Mode Analysis (NMA). Our algorithm assigns resonances and sparse NOEs using each of the structures in the ensemble separately, and aggregates the results using a voting scheme based on maximum bipartite matching. Experimental results on a 76-residue protein, human ubiquitin, using 4 distant template structures (each with a backbone RMSD ranging between 3.2-7.7 Angstroms to the target protein) show an increase of up to 22% in the assignment accuracy. Our algorithm also improves the robustness of NVR with respect to structural noise. We provide a confidence measure for each assignment using the percentage of the structures that agree on that assignment. We use this measure to assign a subset (more than 35\%) of peaks with more than 90\% accuracy. We further validate our algorithm on data for two additional proteins with NVR. We then show the general applicability of our approach by applying our NMA ensemble-based voting scheme to another SBA tool, MARS. For three test proteins with corresponding templates, including the 370-residue maltose binding protein, we increase the number of reliable assignments made by MARS, by up to 4.2-fold. Finally, we show that our voting scheme is sound and optimal, by proving that it is a maximum likelihood estimator of the correct assignments.

Joint work with Bruce Donald and Vincent Conitzer.