A Hausdorff-Based Noe Assignment Algorithm Using Protein Backbone Determined From Residual Dipolar Couplings and Rotamer Patterns

Abstract

High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue 37, 39 , employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn 3 +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 Å and all-heavy-atom RMSD < 2.5 Å from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.

Automated NMR assignment and protein structure determination using sparse dipolar coupling constraints

“…This review is tempered by our recent experiences in automated assignments [79, 82, 83, 118, 153, 174], novel algorithms for protein structure determination [152, 156, 117, 89, 110, 151, 155, 154], characterization of protein complexes [118, 99] and membrane proteins [117], and fold recognition using only unassigned NMR data [82, 83, 78, 80]. Recent algorithms for automated assignment and structure determination based on sparse dipolar couplings represent a departure from the stochastic methods frequently employed by the NMR community (e.g., simulated annealing/molecular dynamics (SA/MD), Monte Carlo (MC), etc.)…”

“…Novel techniques, including the algorithms triangle [153] and H ana [174], exploit the accurate, high-throughput backbone structures obtained exactly using sparse RDCs. NOE assignment can be difficult to fully automate, and structure determination of symmetric membrane proteins by NMR can be challenging.…”

Experimental Protein Structure Verification by Scoring with a Single, Unassigned NMR Spectrum

“…The simulated peak list for each model is then compared with the experimental peak list using the COMPASS score, which is based on the modified Hausdorff distance. Hausdorff distances are a popular family of metrics in computational image analysis, and have found applications both in structure comparison and NOESY (nuclear Overhauser effect spectroscopy) peak matching (Zeng et al, 2008; Kozin and Svergun, 2001). …”

NVR-BIP: Nuclear Vector Replacement using Binary Integer Programming for NMR Structure-Based Assignments

“…We extracted the NH- and CH-bond vector coordinates for ubiquitin from the corresponding PDB files listed in Table 3, for hSRI from pdb ID 2A7O, for ff2 from pdb ID 2E71, for pol η from pdb ID 2I5O and for GB1 from pdb ID 3GB1. We extracted the backbone NOEs (NOEs between H α and amide protons) for hSRI, ff2, ubiquitin, pol η and GB1 from the list of assigned NOEs by HANA [17]. This amounts to 156, 105, 155, 78 and 138 NOEs, respectively.…”