Stereospecific assignments of protein NMR resonances based on the tertiary structure and 2D/3D NOE data

Antineurodegenerative Labdane Diterpenoid Glycosides from the Twigs of Pinus koraiensis

Eleven new labdane-type diterpenoid glycosides, koraiensides A-K (1-11), together with two known analogues were isolated from the twigs of Pinus koraiensis. Their structures were elucidated via NMR, HRMS, and ECD data, DP4+ statistical analysis, and hydrolysis. The metabolites were tested for induction of nerve growth factor in C6 glioma cells to evaluate their potential neuroprotective activity. The compounds were measured for production of nitric oxide levels in lipopolysaccharide (LPS)-activated murine microglia BV2 cells to assess their antineuroinflammatory activity. Compounds 10 and 13 showed NGF secretion inducing effects from C6 glioma cells (162.3 ± 13.9% and 162.7 ± 6.9%, respectively). Compound 6 showed an IC₅₀ value of 24.1 μM, implying significant inhibition of NO production.

Nuclear overhauser spectroscopy of chiral CHD methylene groups

Nuclear magnetic resonance spectroscopy (NMR) can provide a great deal of information about structure and dynamics of biomolecules. The quality of an NMR structure strongly depends on the number of experimental observables and on their accurate conversion into geometric restraints. When distance restraints are derived from nuclear Overhauser effect spectroscopy (NOESY), stereo-specific assignments of prochiral atoms can contribute significantly to the accuracy of NMR structures of proteins and nucleic acids. Here we introduce a series of NOESY-based pulse sequences that can assist in the assignment of chiral CHD methylene protons in random fractionally deuterated proteins. Partial deuteration suppresses spin-diffusion between the two protons of CH2 groups that normally impedes the distinction of cross-relaxation networks for these two protons in NOESY spectra. Three and four-dimensional spectra allow one to distinguish cross-relaxation pathways involving either of the two methylene protons so that one can obtain stereospecific assignments. In addition, the analysis provides a large number of stereospecific distance restraints. Non-uniform sampling was used to ensure optimal signal resolution in 4D spectra and reduce ambiguities of the assignments. Automatic assignment procedures were modified for efficient and accurate stereospecific assignments during automated structure calculations based on 3D spectra. The protocol was applied to calcium-loaded calbindin D9k. A large number of stereospecific assignments lead to a significant improvement of the accuracy of the structure.

CH3-specific NMR assignment of alanine, isoleucine, leucine and valine methyl groups in high molecular weight proteins using a single sample

A new strategy for the NMR assignment of aliphatic side-chains in large perdeuterated proteins is proposed. It involves an alternative isotopic labeling protocol, the use of an out-and-back (13)C-(13)C TOCSY experiment ((H)C-TOCSY-C-TOCSY-(C)H) and an optimized non-uniform sampling protocol. It has long been known that the non-linearity of an aliphatic spin-system (for example Ile, Val, or Leu) substantially compromises the efficiency of the TOCSY transfers. To permit the use of this efficient pulse scheme, a series of optimized precursors were designed to yield linear (13)C perdeuterated side-chains with a single protonated CH3 group in these three residues. These precursors were added to the culture medium for incorporation into expressed proteins. For Val and Leu residues, the topologically different spin-systems introduced for the pro-R and pro-S methyl groups enable stereospecific assignment. All CH3 can be simultaneously assigned on a single sample using a TOCSY experiment. It only requires the tuning of a mixing delay and is thus more versatile than the relayed COSY experiment. Enhanced resolution and sensi-tivity can be achieved by non-uniform sampling combined with the removal of the large JCC coupling by deconvolution prior to the processing by iterative soft thresholding. This strategy has been used on malate synthase G where a large percentage of the CH3 groups could be correlated directly up to the backbone Ca. It is anticipated that this robust combined strategy can be routinely applied to large proteins.

Stereospecific assignments in proteins using exact NOEs

Recently developed methods to measure distances in proteins with high accuracy by "exact" nuclear Overhauser effects (eNOEs) make it possible to determine stereospecific assignments, which are particularly important to fully exploit the accuracy of the eNOE distance measurements. Stereospecific assignments are determined by comparing the eNOE-derived distances to protein structure bundles calculated without stereospecific assignments, or an independently determined crystal structure. The absolute and relative CYANA target function difference upon swapping the stereospecific assignment of a diastereotopic group yields the respective stereospecific assignment. We applied the method to the eNOE data set that has recently been obtained for the third immunoglobulin-binding domain of protein G (GB3). The 884 eNOEs provide relevant data for 47 of the total of 75 diastereotopic groups. Stereospecific assignments could be established for 45 diastereotopic groups (96 %) using the X-ray structure, or for 27 diastereotopic groups (57 %) using structures calculated with the eNOE data set without stereospecific assignments, all of which are in agreement with those determined previously. The latter case is relevant for structure determinations based on eNOEs. The accuracy of the eNOE distance measurements is crucial for making stereospecific assignments because applying the same method to the traditional NOE data set for GB3 with imprecise upper distance bounds yields only 13 correct stereospecific assignments using the X-ray structure or 2 correct stereospecific assignments using NMR structures calculated without stereospecific assignments.

Towards fully automated structure-based NMR resonance assignment of ¹⁵N-labeled proteins from automatically picked peaks

In NMR resonance assignment, an indispensable step in NMR protein studies, manually processed peaks from both N-labeled and C-labeled spectra are typically used as inputs. However, the use of homologous structures can allow one to use only N-labeled NMR data and avoid the added expense of using C-labeled data. We propose a novel integer programming framework for structure-based backbone resonance assignment using N-labeled data. The core consists of a pair of integer programming models: one for spin system forming and amino acid typing, and the other for backbone resonance assignment. The goal is to perform the assignment directly from spectra without any manual intervention via automatically picked peaks, which are much noisier than manually picked peaks, so methods must be error-tolerant. In the case of semi-automated/manually processed peak data, we compare our system with the Xiong-Pandurangan-Bailey-Kellogg's contact replacement (CR) method, which is the most error-tolerant method for structure-based resonance assignment. Our system, on average, reduces the error rate of the CR method by five folds on their data set. In addition, by using an iterative algorithm, our system has the added capability of using the NOESY data to correct assignment errors due to errors in predicting the amino acid and secondary structure type of each spin system. On a publicly available data set for human ubiquitin, where the typing accuracy is 83%, we achieve 91% accuracy, compared to the 59% accuracy obtained without correcting for such errors. In the case of automatically picked peaks, using assignment information from yeast ubiquitin, we achieve a fully automatic assignment with 97% accuracy. To our knowledge, this is the first system that can achieve fully automatic structure-based assignment directly from spectra. This has implications in NMR protein mutant studies, where the assignment step is repeated for each mutant.

Error tolerant NMR backbone resonance assignment and automated structure generation

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.

Robust structure-based resonance assignment for functional protein studies by NMR

High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly H(N)-H(N) NOEs networks, as well as (1)H-(15) N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.

NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure

Motivation: A prerequisite for any protein study by NMR is the assignment of the resonances from the ¹⁵N−¹H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%.

Results: We propose here a new program, called NOEnet, implementing an efficient complete search algorithm that ensures the correctness of the assignment results. NOEnet exploits the network character of unambiguous NOE constraints to realize an exhaustive search of all matching possibilities of the NOE network onto the structural one. NOEnet has been successfully tested on EIN, a large protein of 28 kDa, using only NOE data. The complete search of NOEnet finds all possible assignments compatible with experimental data that can be defined as an assignment ensemble. We show that multiple assignment possibilities of large NOE networks are restricted to a small spatial assignment range (SAR), so that assignment ensembles, obtained from accessible experimental data, are precise enough to be used for functional proteins studies, like protein–ligand interaction or protein dynamics studies. We believe that NOEnet can become a major tool for the structure-based backbone resonance assignment strategy in NMR.

Availability: The NOEnet program will be available under: http://www.icsn.cnrs-gif.fr/download/nmr

Contact:carine@icsn.cnrs-gif.fr; eric.guittet@icsn.cnrs-gif.fr

Supplementary Information:Supplementary data are available at Bioinformatics online.