Multidimensional NMR methods for protein structure determination

Structural studies of proteins are critical for understanding biological processes at the molecular level. Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for obtaining structural and dynamic information on proteins and protein-ligand complexes. In the present review, methodologies for NMR structure determination of proteins and macromolecular complexes are described. In addition, a number of recent advances that reduce the molecular weight limitations previously imposed on NMR studies of biomolecules are discussed, highlighting applications of these technologies to protein systems studied in our laboratories.     

De novo protein structure determination using sparse NMR data

We describe a method for generating moderate to high-resolution protein structures using limited NMR data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with 1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.     

Automated protein structure calculation from NMR data

Current software is almost at the stage to permit completely automatic structure determination of small proteins of <15 kDa, from NMR spectra to structure validation with minimal user interaction. This goal is welcome, as it makes structure calculation more objective and therefore more easily validated, without any loss in the quality of the structures generated. Moreover, it releases expert spectroscopists to carry out research that cannot be automated. It should not take much further effort to extend automation to ca 20 kDa. However, there are technological barriers to further automation, of which the biggest are identified as: routines for peak picking; adoption and sharing of a common framework for structure calculation, including the assembly of an automated and trusted package for structure validation; and sample preparation, particularly for larger proteins. These barriers should be the main target for development of methodology for protein structure determination, particularly by structural genomics consortia.

Structure determination of a new protein from backbone-centered NMR data and NMR-assisted structure prediction

Targeting of proteins for structure determination in structural genomic programs often includes the use of threading and fold recognition methods to exclude proteins belonging to well-populated fold families, but such methods can still fail to recognize preexisting folds. The authors illustrate here a method in which limited amounts of structural data are used to improve an initial homology search and the data are subsequently used to produce a structure by data-constrained refinement of an identified structural template. The data used are primarily NMR-based residual dipolar couplings, but they also include additional chemical shift and backbone-nuclear Overhauser effect data. Using this methodology, a backbone structure was efficiently produced for a 10 kDa protein (PF1455) from Pyrococcus furiosus. Its relationship to existing structures and its probable function are discussed.     

Requirements on paramagnetic relaxation enhancement data for membrane protein structure determination by NMR

Nuclear magnetic resonance (NMR) structure calculations of the α-helical integral membrane proteins DsbB, GlpG, and halorhodopsin show that distance restraints from paramagnetic relaxation enhancement (PRE) can provide sufficient structural information to determine their structure with an accuracy of about 1.5?? in the absence of other long-range conformational restraints. Our systematic study with simulated NMR data shows that about one spin label per transmembrane helix is necessary for obtaining enough PRE distance restraints to exclude wrong topologies, such as pseudo mirror images, if only limited other NMR restraints are available. Consequently, an experimentally realistic amount of PRE data enables α-helical membrane protein structure determinations that would not be feasible with the very limited amount of conventional NOESY data normally available for these systems. These findings are in line with our recent first de novo NMR structure determination of a heptahelical integral membrane protein, proteorhodopsin, that relied extensively on PRE data.     

A systematic comparison of three structure determination methods from NMR data: Dependence upon quality and quantity of data

Summary We have systematically examined how the quality of NMR protein structures depends on (1) the number of NOE distance constraints. (2) their assumed precision, (3) the method of structure calculation and (4) the size of the protein. The test sets of distance constraints have been derived from the crystal structures of crambin (5 kDa) and staphylococcal nuclease (17 kDa). Three methods of structure calculation have been compared: Distance Geometry (DGEOM), Restrained Molecular Dynamics (XPLOR) and the Double Iterated Kalman Filter (DIKF). All three methods can reproduce the general features of the starting structure under all conditions tested. In many instances the apparent precision of the calculated structure (as measured by the RMS dispersion from the average) is greater than its accuracy (as measured by the RMS deviation of the average structure from the starting crystal structure). The global RMS deviations from the reference structures decrease exponentially as the number of constraints is increased, and after using about 30% of all potential constraints, the crrors asymptotically approach a limiting value. Increasing the assumed precision of the constraints has the same qualitative effect as increasing the number of constraints. For comparable numbers of constraints/residue, the precision of the calculated structure is less for the larger than for the smaller protein, regardless of the method of calculation. The accuracy of the average structure calculated by Restrained Molecular Dynamics is greater than that of structures obtained by purely geometric methods (DGEOM and DIKF).     

Improved strategies for the determination of protein structures from NMR data: the solution structure of acyl carrier protein

The hybrid method that combines the early stages of a distance geometry program with simulated annealing in the presence of NMR constraints was optimized to obtain structures fully consistent with the observed NMR data. This was achieved by using more restrictive bounds of the NOE constraints than those usually used in the literature and by grouping the NOEs into classes dependent on the quality of the experimental NOE data. The 'floating' stereospecific assignment introduced at the simulated annealing stage of the calculations further improved the definition of the local conformation. An improved sampling and convergence property of the hybrid method was obtained by means of fitting the substructure obtained from the distance geometry program to different conformations. Compared to the standard hybrid methods, this procedure gave superior structures for a 77 amino acid protein, acyl carrier protein from Escherichia coli.     

Microgram-scale protein structure determination by NMR

Using conventional triple-resonance nuclear magnetic resonance (NMR) experiments with a 1 mm triple-resonance microcoil NMR probe, we determined near complete resonance assignments and three-dimensional (3D) structure of the 68-residue Methanosarcina mazei TRAM protein using only 72 mug (6 microl, 1.4 mM) of protein. This first example of a complete solution NMR structure determined using microgram quantities of protein demonstrates the utility of microcoil-probe NMR technologies for protein samples that can be produced in only limited quantities.     

Advances in automated NMR protein structure determination

Around half of all protein structures solved nowadays using solution-state nuclear magnetic resonance (NMR) spectroscopy have been because of automated data analysis. The pervasiveness of computational approaches in general hides, however, a more nuanced view in which the full variety and richness of the field appears. This review is structured around a comparison of methods associated with three NMR observables: classical nuclear Overhauser effect (NOE) constraint gathering in contrast with more recent chemical shift and residual dipole coupling (RDC) based protocols. In each case, the emphasis is placed on the latest research, covering mainly the past 5 years. By describing both general concepts and representative programs, the objective is to map out a field in which--through the very profusion of approaches--it is all too easy to lose one's bearings.     

A novel strategy for NMR resonance assignment and protein structure determination

The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution – especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.     

Deciphering protein dynamics from NMR data using explicit structure sampling and selection

Perhaps one of the most prominent realizations of recent years is the critical role that protein dynamics plays in many facets of cellular function. While characterization of protein dynamics is fundamental to our understanding of protein function, the ability to explicitly detect an ensemble of protein conformations from dynamics data is a paramount challenge in structural biology. Here, we report a new computational method, Sample and Select, for determining the ensemble of protein conformations consistent with NMR dynamics data. This method can be generalized and extended to different sources of dynamics data, enabling broad applicability in deciphering protein dynamics at different timescales. The structural ensemble derived from Sample and Select will provide structural and dynamic information that should aid us in understanding and manipulating protein function.     

Extracting knowledge from protein structure geometry

Protein structure prediction techniques proceed in two steps, namely the generation of many structural models for the protein of interest, followed by an evaluation of all these models to identify those that are native‐like. In theory, the second step is easy, as native structures correspond to minima of their free energy surfaces. It is well known however that the situation is more complicated as the current force fields used for molecular simulations fail to recognize native states from misfolded structures. In an attempt to solve this problem, we follow an alternate approach and derive a new potential from geometric knowledge extracted from native and misfolded conformers of protein structures. This new potential, Metric Protein Potential (MPP), has two main features that are key to its success. Firstly, it is composite in that it includes local and nonlocal geometric information on proteins. At the short range level, it captures and quantifies the mapping between the sequences and structures of short (7‐mer) fragments of protein backbones through the introduction of a new local energy term. The local energy term is then augmented with a nonlocal residue‐based pairwise potential, and a solvent potential. Secondly, it is optimized to yield a maximized correlation between the energy of a structural model and its root mean square (RMS) to the native structure of the corresponding protein. We have shown that MPP yields high correlation values between RMS and energy and that it is able to retrieve the native structure of a protein from a set of high‐resolution decoys. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Automated structure determination from NMR spectra

Automated methods for protein structure determination by NMR have increasingly gained acceptance and are now widely used for the automated assignment of distance restraints and the calculation of three-dimensional structures. This review gives an overview of the techniques for automated protein structure analysis by NMR, including both NOE-based approaches and methods relying on other experimental data such as residual dipolar couplings and chemical shifts, and presents the FLYA algorithm for the fully automated NMR structure determination of proteins that is suitable to substitute all manual spectra analysis and thus overcomes a major efficiency limitation of the NMR method for protein structure determination.     

De novo high-resolution protein structure determination from sparse spin-labeling EPR data

As many key proteins evade crystallization and remain too large for nuclear magnetic resonance spectroscopy, electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labeling offers an alternative approach for obtaining structural information. Such information must be translated into geometric restraints to be used in computer simulations. Here, distances between spin labels are converted into distance ranges between beta carbons by using a "motion-on-a-cone" model, and a linear-correlation model links spin-label accessibility to the number of neighboring residues. This approach was tested on T4-lysozyme and alphaA-crystallin with the de novo structure prediction algorithm Rosetta. The results demonstrate the feasibility of obtaining highly accurate, atomic-detail models from EPR data by yielding 1.0 A and 2.6 A full-atom models, respectively. Distance restraints between amino acids far apart in sequence but close in space are most valuable for structure determination. The approach can be extended to other experimental techniques such as fluorescence spectroscopy, substituted cysteine accessibility method, or mutational studies.     

High-resolution methyl edited GFT NMR experiments for protein resonance assignments and structure determination

Three-dimensional (3D) structure determination of proteins is benefitted by long-range distance constraints comprising the methyl groups, which constitute the hydrophobic core of proteins. However, in methyl groups (of Ala, Ile, Leu, Met, Thr and Val) there is a significant overlap of ¹³C and ¹H chemical shifts. Such overlap can be resolved using the recently proposed (3,2)D HCCH-COSY, a G-matrix Fourier transform (GFT) NMR based experiment, which facilitates editing of methyl groups into distinct spectral regions by combining their ¹³C chemical shifts with that of the neighboring, directly attached, ¹³C nucleus. Using this principle, we present three GFT experiments: (a) (4,3)D NOESY-HCCH, (b) (4,3)D ¹H-TOCSY-HCCH and (c) (4,3)D ¹³C-TOCSY-HCCH. These experiments provide unique 4D spectral information rapidly with high sensitivity and resolution for side-chain resonance assignments and NOE analysis of methyl groups. This is exemplified by (4,3)D NOESY-HCCH data acquired for 17.9 kDa non-deuterated cytosolic human J-protein co-chaperone, which provided crucial long-range distance constraints for its 3D structure determination.