A general Monte Carlo/simulated annealing algorithm for resonance assignment in NMR of uniformly labeled biopolymers

Manual and automatic assignment of two different Aβ40 amyloid fibril polymorphs using MAS solid-state NMR spectroscopy

Amyloid fibrils from Alzheimer's amyloid-beta peptides (Aβ) are found to be polymorphic. So far, 14 Aβ40 fibril structures have been determined. The mechanism of why one particular protein sequence adopts so many different three-dimensional structures is yet not understood. In this work, we describe the assignment of the NMR chemical shifts of two Alzheimer's disease fibril polymorphs, P1 and P2, which are formed by the amyloid-beta peptide Aβ40. The assignment is based on 13C-detected 3D NCACX and NCOCX experiments MAS solid-state NMR experiments. The fibril samples are prepared using an extensive seeding protocol in the absence and presence of the small heat shock protein αB-crystallin. In addition to manual assignments, we obtain chemical shift assignments using the automation software ARTINA. We present an analysis of the secondary chemical shifts and a discussion on the differences between the manual and automated assignment strategies.

ASAP: An automatic sequential assignment program for congested multidimensional solid state NMR spectra

Accurate signal assignments can be challenging for congested solid-state NMR (ssNMR) spectra. We describe an automatic sequential assignment program (ASAP) to partially overcome this challenge. ASAP takes three input files: the residue type assignments (RTAs) determined from the better-resolved NCACX spectrum, the full peak list of the NCOCX spectrum, and the protein sequence. It integrates our auto-residue type assignment strategy (ARTIST) with the Monte Carlo simulated annealing (MCSA) algorithm to overcome the hurdle for accurate signal assignments caused by incomplete side-chain resonances and spectral congestion. Combined, ASAP demonstrates robust performance and accelerates signal assignments of large proteins (>200 residues) that lack crystalline order.

Charting the solid-state NMR signals of polysaccharides: A database-driven roadmap

Solid-state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high-resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high-resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations.

Solid-State NMR: Methods for Biological Solids

In the last two decades, solid-state nuclear magnetic resonance (ssNMR) spectroscopy has transformed from a spectroscopic technique investigating small molecules and industrial polymers to a potent tool decrypting structure and underlying dynamics of complex biological systems, such as membrane proteins, fibrils, and assemblies, in near-physiological environments and temperatures. This transformation can be ascribed to improvements in hardware design, sample preparation, pulsed methods, isotope labeling strategies, resolution, and sensitivity. The fundamental engagement between nuclear spins and radio-frequency pulses in the presence of a strong static magnetic field is identical between solution and ssNMR, but the experimental procedures vastly differ because of the absence of molecular tumbling in solids. This review discusses routinely employed state-of-the-art static and MAS pulsed NMR methods relevant for biological samples with rotational correlation times exceeding 100's of nanoseconds. Recent developments in signal filtering approaches, proton methodologies, and multiple acquisition techniques to boost sensitivity and speed up data acquisition at fast MAS are also discussed. Several examples of protein structures (globular, membrane, fibrils, and assemblies) solved with ssNMR spectroscopy have been considered. We also discuss integrated approaches to structurally characterize challenging biological systems and some newly emanating subdisciplines in ssNMR spectroscopy.

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

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New tools support efficient analysis of solution and solid-state NMR spectra of proteins.

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POKY integrates a powerful suite of software packages for automated assignments.

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The Versatile Assigner module validates assignments through probabilistic analysis.

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The operation of these tools is supported by on-line guidance.

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The performance of these tools is evaluated in reference to competing software.

NMR spectroscopy provides structural and functional information about biomolecules and their complexes. The complexity of these systems can make the NMR data difficult to interpret, particularly for newer users of NMR technology, who may have limited understanding of the tools available and how they are used. To alleviate this problem, we have created software based on standardized workflows for both solution and solid-state NMR spectroscopy of proteins. These tools assist with manual and automated peak picking and with chemical shift assignment and validation. They provide users with an optimized path through spectral analysis that can help them perform the necessary tasks more efficiently.

Dynamic structural order of a low-complexity domain facilitates assembly of intermediate filaments

The main point of our manuscript is focused on the structure of the low-complexity (LC) domain of the Tm1-I/C intermediate filament protein in the context of assembled intermediate filaments. We found that the LC tail domain of Tm1-I/C exists in precisely the same cross-β conformation within its proper biologic assembly as it does in labile, amyloid-like polymers made from the tail domain alone. This science represents a conceptually distinct advance that may form the cornerstone understanding of how the thousands of LC domains expressed in eukaryotic cells operate in a mechanistic sense, and stands in conflict with previous research claiming that LC domains function in the absence of molecular structure.

The coiled-coil domains of intermediate filament (IF) proteins are flanked by regions of low sequence complexity. Whereas IF coiled-coil domains assume dimeric and tetrameric conformations on their own, maturation of eight tetramers into cylindrical IFs is dependent on either “head” or “tail” domains of low sequence complexity. Here we confirm that the tail domain required for assembly of Drosophila Tm1-I/C IFs functions by forming labile cross-β interactions. These interactions are seen in polymers made from the tail domain alone, as well as in assembled IFs formed by the intact Tm1-I/C protein. The ability to visualize such interactions in situ within the context of a discrete cellular assembly lends support to the concept that equivalent interactions may be used in organizing other dynamic aspects of cell morphology.

Fused in Sarcoma: Properties, Self-Assembly and Correlation with Neurodegenerative Diseases

Fused in sarcoma (FUS) is a DNA/RNA binding protein that is involved in RNA metabolism and DNA repair. Numerous reports have demonstrated by pathological and genetic analysis that FUS is associated with a variety of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and polyglutamine diseases. Traditionally, the fibrillar aggregation of FUS was considered to be the cause of those diseases, especially via its prion-like domains (PrLDs), which are rich in glutamine and asparagine residues. Lately, a nonfibrillar self-assembling phenomenon, liquid–liquid phase separation (LLPS), was observed in FUS, and studies of its functions, mechanism, and mutual transformation with pathogenic amyloid have been emerging. This review summarizes recent studies on FUS self-assembling, including both aggregation and LLPS as well as their relationship with the pathology of ALS, FTLD, and other neurodegenerative diseases.

Structural characterization of the D290V mutation site in hnRNPA2 low-complexity-domain polymers

Human genetic studies have given evidence of familial, disease-causing mutations in the analogous amino acid residue shared by three related RNA binding proteins causative of three neurological diseases. Alteration of aspartic acid residue 290 of hnRNPA2 to valine is believed to predispose patients to multisystem proteinopathy. Mutation of aspartic acid 262 of hnRNPA1 to either valine or asparagine has been linked to either amyotrophic lateral sclerosis or multisystem proteinopathy. Mutation of aspartic acid 378 of hnRNPDL to either asparagine or histidine has been associated with limb girdle muscular dystrophy. All three of these aspartic acid residues map to evolutionarily conserved regions of low-complexity (LC) sequence that may function in states of either intrinsic disorder or labile self-association. Here, we present a combination of solid-state NMR spectroscopy with segmental isotope labeling and electron microscopy on the LC domain of the hnRNPA2 protein. We show that, for both the wild-type protein and the aspartic acid 290-to-valine mutant, labile polymers are formed in which the LC domain associates into an in-register cross-β conformation. Aspartic acid 290 is shown to be charged at physiological pH and immobilized within the polymer core. Polymers of the aspartic acid 290-to-valine mutant are thermodynamically more stable than wild-type polymers. These observations give evidence that removal of destabilizing electrostatic interactions may be responsible for the increased propensity of the mutated LC domains to self-associate in disease-causing conformations.

Automated assignment of NMR spectra of macroscopically oriented proteins using simulated annealing

An automated technique for the sequential assignment of NMR backbone resonances of oriented protein samples has been developed and tested based on ¹⁵N-¹⁵N homonuclear exchange and spin-exchanged separated local-field spectra. By treating the experimental spectral intensity as a pseudopotential, the Monte-Carlo Simulated Annealing algorithm has been employed to seek lowest-energy assignment solutions over a large sampling space where direct enumeration would be unfeasible. The determined sequential assignments have been scored based on the positions of the crosspeaks resulting from the possible orders for the main peaks. This approach is versatile in terms of the parameters that can be specified to achieve the best-fit result. At a minimum the algorithm requires a continuous segment of the main-peak chemical shifts obtained from a uniformly labeled sample and a spin-exchanged experimental spectrum represented as a 2D matrix array. With selective labeling experiments, groups of chemical shifts corresponding to specific locations in the protein backbone can be fixed, thereby decreasing the sampling space. The output from the program consists of a list of top-score main peak assignments, which can be subjected to further scoring criteria until a consensus solution is found. The algorithm has first been tested on a synthetic spectrum with randomly generated chemical shifts and dipolar couplings for the main peaks. The original assignments have been successfully recovered for as many as 100 main peaks when residue-type information was used even in the presence of substantial spectral peak overlap. The algorithm was then applied to assigning two sets of experimental spectra to recover and confirm the previously established assignments in an automated fashion. For the 20-residue transmembrane domain of Pf1 coat protein reconstituted in magnetically aligned bicelles, the original assignment by Park et al. (2010) was recovered by the automated algorithm with additional input from 5 selectively labeled amino acid spectra. The second case considered was the 46 residue Pf1 bacteriophage from Thiriot et al. (2005) and Knox et al. (2010), of which 38 residues were fit. Automated fitting resulted in several possible assignments but not exactly the original assignment. By using a post-fitting filtering procedure based on the number of missed cross peaks and Pf1 helical structure, a consensus spectroscopic assignment is proposed covering 84% of the original assignment. While the automated assignment works best in spectra with well-resolved crosspeaks, it also tolerates substantial spectral crowding to yield reasonable assignments in the cases where ambiguity and degeneracy of possible assignment solutions are inevitable.

Solid-state MAS NMR resonance assignment methods for proteins

The prerequisite to structural or functional studies of proteins by NMR is generally the assignment of resonances. Since the first assignment of proteins by solid-state MAS NMR was conducted almost two decades ago, a wide variety of different pulse sequences and methods have been proposed and continue to be developed. Traditionally, a variety of 2D and 3D ¹³C-detected experiments have been used for the assignment of backbone and side-chain ¹³C and ¹⁵N resonances. These methods have found widespread use across the field. But as the hardware has changed and higher spinning frequencies and magnetic fields are becoming available, the ability to use direct proton detection is opening up a new set of assignment methods based on triple-resonance experiments. This review describes solid-state MAS NMR assignment methods using carbon detection and proton detection at different deuteration levels. The use of different isotopic labelling schemes as an aid to assignment in difficult cases is discussed as well as the increasing number of software packages that support manual and automated resonance assignment.

Structure of FUS Protein Fibrils and Its Relevance to Self-Assembly and Phase Separation of Low-Complexity Domains

Polymerization and phase separation of proteins containing low-complexity (LC) domains are important factors in gene expression, mRNA processing and trafficking, and localization of translation. We have used solid-state nuclear magnetic resonance methods to characterize the molecular structure of self-assembling fibrils formed by the LC domain of the fused in sarcoma (FUS) RNA-binding protein. From the 214-residue LC domain of FUS (FUS-LC), a segment of only 57 residues forms the fibril core, while other segments remain dynamically disordered. Unlike pathogenic amyloid fibrils, FUS-LC fibrils lack hydrophobic interactions within the core and are not polymorphic at the molecular structural level. Phosphorylation of core-forming residues by DNA-dependent protein kinase blocks binding of soluble FUS-LC to FUS-LC hydrogels and dissolves phase-separated, liquid-like FUS-LC droplets. These studies offer a structural basis for understanding LC domain self-assembly, phase separation, and regulation by post-translational modification.

Backbone assignment of perdeuterated proteins by solid-state NMR using proton detection and ultrafast magic-angle spinning

Solid-state NMR (ssNMR) is a technique that allows the study of protein structure and dynamics at atomic detail. In contrast to X-ray crystallography and cryo-electron microscopy, proteins can be studied under physiological conditions-for example, in a lipid bilayer and at room temperature (0-35 °C). However, ssNMR requires considerable amounts (milligram quantities) of isotopically labeled samples. In recent years, ¹H-detection of perdeuterated protein samples has been proposed as a method of alleviating the sensitivity issue. Such methods are, however, substantially more demanding to the spectroscopist, as compared with traditional ¹³C-detected approaches. As a guide, this protocol describes a procedure for the chemical shift assignment of the backbone atoms of proteins in the solid state by ¹H-detected ssNMR. It requires a perdeuterated, uniformly ¹³C- and ¹⁵N-labeled protein sample with subsequent proton back-exchange to the labile sites. The sample needs to be spun at a minimum of 40 kHz in the NMR spectrometer. With a minimal set of five 3D NMR spectra, the protein backbone and some of the side-chain atoms can be completely assigned. These spectra correlate resonances within one amino acid residue and between neighboring residues; taken together, these correlations allow for complete chemical shift assignment via a 'backbone walk'. This results in a backbone chemical shift table, which is the basis for further analysis of the protein structure and/or dynamics by ssNMR. Depending on the spectral quality and complexity of the protein, data acquisition and analysis are possible within 2 months.

INFOS: spectrum fitting software for NMR analysis

Software for fitting of NMR spectra in MATLAB is presented. Spectra are fitted in the frequency domain, using Fourier transformed lineshapes, which are derived using the experimental acquisition and processing parameters. This yields more accurate fits compared to common fitting methods that use Lorentzian or Gaussian functions. Furthermore, a very time-efficient algorithm for calculating and fitting spectra has been developed. The software also performs initial peak picking, followed by subsequent fitting and refinement of the peak list, by iteratively adding and removing peaks to improve the overall fit. Estimation of error on fitting parameters is performed using a Monte-Carlo approach. Many fitting options allow the software to be flexible enough for a wide array of applications, while still being straightforward to set up with minimal user input.

Conformationally selective multidimensional chemical shift ranges in proteins from a PACSY database purged using intrinsic quality criteria

We have determined refined multidimensional chemical shift ranges for intra-residue correlations ((13)C-(13)C, (15)N-(13)C, etc.) in proteins, which can be used to gain type-assignment and/or secondary-structure information from experimental NMR spectra. The chemical-shift ranges are the result of a statistical analysis of the PACSY database of >3000 proteins with 3D structures (1,200,207 (13)C chemical shifts and >3 million chemical shifts in total); these data were originally derived from the Biological Magnetic Resonance Data Bank. Using relatively simple non-parametric statistics to find peak maxima in the distributions of helix, sheet, coil and turn chemical shifts, and without the use of limited "hand-picked" data sets, we show that ~94% of the (13)C NMR data and almost all (15)N data are quite accurately referenced and assigned, with smaller standard deviations (0.2 and 0.8 ppm, respectively) than recognized previously. On the other hand, approximately 6% of the (13)C chemical shift data in the PACSY database are shown to be clearly misreferenced, mostly by ca. -2.4 ppm. The removal of the misreferenced data and other outliers by this purging by intrinsic quality criteria (PIQC) allows for reliable identification of secondary maxima in the two-dimensional chemical-shift distributions already pre-separated by secondary structure. We demonstrate that some of these correspond to specific regions in the Ramachandran plot, including left-handed helix dihedral angles, reflect unusual hydrogen bonding, or are due to the influence of a following proline residue. With appropriate smoothing, significantly more tightly defined chemical shift ranges are obtained for each amino acid type in the different secondary structures. These chemical shift ranges, which may be defined at any statistical threshold, can be used for amino-acid type assignment and secondary-structure analysis of chemical shifts from intra-residue cross peaks by inspection or by using a provided command-line Python script (PLUQin), which should be useful in protein structure determination. The refined chemical shift distributions are utilized in a simple quality test (SQAT) that should be applied to new protein NMR data before deposition in a databank, and they could benefit many other chemical-shift based tools.

Probing the influential factors of NMR T1-T2 spectra in the characterization of the kerogen by numerical simulation

The low field nuclear magnetic resonance (NMR) spectroscopy has been widely used to characterize the longitudinal and transversal relaxation (T1-T2) spectrum of unconventional resources such as shale gas and tight oil containing significant proportions of kerogen and bitumen. However, it requires exquisite design of the acquisition model and the inversion algorithm due to the fast relaxation nature of the kerogen and bitumen. A new direct two dimensional (2D) inversion algorithm combined the iterative truncated singular value decomposition (TSVD) and the Akaiake Information Criterion (AIC) is presented to perform the data inversion efficiently. The fluid component decomposition (FCD) is applied to construct the forward T1-T2 model of the kerogen, and numerical simulations are conducted to investigate factors which may influence inversion results including echo spacing, recovery time series, signal to noise ratio (SNR), and the maximal iteration time. Results show that the T2 component is heavily impaired by the echo spacing, whereas the T1 component is influenced by the recovery time series but with limited effects. The inversion precision is greatly affected by the quality of the data. The inversed spectrum deviates from the model seriously when the SNR of the artificial noise is lower than 50, and the T2 component is more sensitive to the noise than the T1 component. What's more, the maximal iteration time can also affect the inversion result, especially when the maximal iteration time is smaller than 500. Proper acquisition and inversion parameters for the characterization of the kerogen are obtained considering the precision and the computational cost.

Aromatic spectral editing techniques for magic-angle-spinning solid-state NMR spectroscopy of uniformly (13)C-labeled proteins

The four aromatic amino acids in proteins, namely histidine, phenylalanine, tyrosine, and tryptophan, have strongly overlapping (13)C chemical shift ranges between 100 and 160ppm, and have so far been largely neglected in solid-state NMR determination of protein structures. Yet aromatic residues play important roles in biology through π-π and cation-π interactions. To better resolve and assign aromatic residues' (13)C signals in magic-angle-spinning (MAS) solid-state NMR spectra, we introduce two spectral editing techniques. The first method uses gated (1)H decoupling in a proton-driven spin-diffusion (PDSD) experiment to remove all protonated (13)C signals and retain only non-protonated carbon signals in the aromatic region of the (13)C spectra. The second technique uses chemical shift filters and (1)H-(13)C dipolar dephasing to selectively detect the Cα, Cβ and CO cross peaks of aromatic residues while suppressing the signals of all aliphatic residues. We demonstrate these two techniques on amino acids, a model peptide, and the microcrystalline protein GB1, and show that they significantly simplify the 2D NMR spectra and both reveal and permit the ready assignment of the aromatic residues' signals.

Automated structure determination from NMR spectra

Three-dimensional structures of proteins in solution can be calculated on the basis of conformational restraints derived from NMR measurements. This chapter gives an overview of the computational methods for NMR protein structure analysis highlighting recent automated methods for the assignment of NMR spectra, the collection of conformational restraints, and the structure calculation.

Membrane proteins in their native habitat as seen by solid-state NMR spectroscopy

Membrane proteins play many critical roles in cells, mediating flow of material and information across cell membranes. They have evolved to perform these functions in the environment of a cell membrane, whose physicochemical properties are often different from those of common cell membrane mimetics used for structure determination. As a result, membrane proteins are difficult to study by traditional methods of structural biology, and they are significantly underrepresented in the protein structure databank. Solid-state Nuclear Magnetic Resonance (SSNMR) has long been considered as an attractive alternative because it allows for studies of membrane proteins in both native-like membranes composed of synthetic lipids and in cell membranes. Over the past decade, SSNMR has been rapidly developing into a major structural method, and a growing number of membrane protein structures obtained by this technique highlights its potential. Here we discuss membrane protein sample requirements, review recent progress in SSNMR methodologies, and describe recent advances in characterizing membrane proteins in the environment of a cellular membrane.

Magic angle spinning NMR of viruses

Viruses, relatively simple pathogens, are able to replicate in many living organisms and to adapt to various environments. Conventional atomic-resolution structural biology techniques, X-ray crystallography and solution NMR spectroscopy provided abundant information on the structures of individual proteins and nucleic acids comprising viruses; however, viral assemblies are not amenable to analysis by these techniques because of their large size, insolubility, and inherent lack of long-range order. In this article, we review the recent advances in magic angle spinning NMR spectroscopy that enabled atomic-resolution analysis of structure and dynamics of large viral systems and give examples of several exciting case studies.

On the problem of resonance assignments in solid state NMR of uniformly ¹⁵N,¹³C-labeled proteins

Determination of accurate resonance assignments from multidimensional chemical shift correlation spectra is one of the major problems in biomolecular solid state NMR, particularly for relative large proteins with less-than-ideal NMR linewidths. This article investigates the difficulty of resonance assignment, using a computational Monte Carlo/simulated annealing (MCSA) algorithm to search for assignments from artificial three-dimensional spectra that are constructed from the reported isotropic (15)N and (13)C chemical shifts of two proteins whose structures have been determined by solution NMR methods. The results demonstrate how assignment simulations can provide new insights into factors that affect the assignment process, which can then help guide the design of experimental strategies. Specifically, simulations are performed for the catalytic domain of SrtC (147 residues, primarily β-sheet secondary structure) and the N-terminal domain of MLKL (166 residues, primarily α-helical secondary structure). Assuming unambiguous residue-type assignments and four ideal three-dimensional data sets (NCACX, NCOCX, CONCA, and CANCA), uncertainties in chemical shifts must be less than 0.4 ppm for assignments for SrtC to be unique, and less than 0.2 ppm for MLKL. Eliminating CANCA data has no significant effect, but additionally eliminating CONCA data leads to more stringent requirements for chemical shift precision. Introducing moderate ambiguities in residue-type assignments does not have a significant effect.

MAS NMR of HIV-1 protein assemblies

The negative global impact of the AIDS pandemic is well known. In this perspective article, the utility of magic angle spinning (MAS) NMR spectroscopy to answer pressing questions related to the structure and dynamics of HIV-1 protein assemblies is examined. In recent years, MAS NMR has undergone major technological developments enabling studies of large viral assemblies. We discuss some of these evolving methods and technologies and provide a perspective on the current state of MAS NMR as applied to the investigations into structure and dynamics of HIV-1 assemblies of CA capsid protein and of Gag maturation intermediates.

Towards automatic protein backbone assignment using proton-detected 4D solid-state NMR data

We introduce an efficient approach for sequential protein backbone assignment based on two complementary proton-detected 4D solid-state NMR experiments that correlate Hi(N)/Ni with CAi/COi or CAi-1/COi-1. The resulting 4D spectra exhibit excellent sensitivity and resolution and are amenable to (semi-)automatic assignment approaches. This strategy allows to obtain sequential connections with high confidence as problems related to peak overlap and multiple assignment possibilities are avoided. Non-uniform sampling schemes were implemented to allow for the acquisition of 4D spectra within a few days. Rather moderate hardware requirements enable the successful demonstration of the method on deuterated type III secretion needles using a 600 MHz spectrometer at a spinning rate of 25 kHz.

VirtualSpectrum, a tool for simulating peak list for multi-dimensional NMR spectra

NMR spectroscopy is a widely used technique for characterizing the structure and dynamics of macromolecules. Often large amounts of NMR data are required to characterize the structure of proteins. To save valuable time and resources on data acquisition, simulated data is useful in the developmental phase, for data analysis, and for comparison with experimental data. However, existing tools for this purpose can be difficult to use, are sometimes specialized for certain types of molecules or spectra, or produce too idealized data. Here we present a fast, flexible and robust tool, VirtualSpectrum, for generating peak lists for most multi-dimensional NMR experiments for both liquid and solid state NMR. It is possible to tune the quality of the generated peak lists to include sources of artifacts from peak overlap, noise and missing signals. VirtualSpectrum uses an analytic expression to represent the spectrum and derive the peak positions, seamlessly handling overlap between signals. We demonstrate our tool by comparing simulated and experimental spectra for different multi-dimensional NMR spectra and analyzing systematically three cases where overlap between peaks is particularly relevant; solid state NMR data, liquid state NMR homonuclear (1)H and (15)N-edited spectra, and 2D/3D heteronuclear correlation spectra of unstructured proteins. We analyze the impact of protein size and secondary structure on peak overlap and on the accuracy of structure determination based on data of different qualities simulated by VirtualSpectrum.

Rapid proton-detected NMR assignment for proteins with fast magic angle spinning

Using a set of six ¹H-detected triple-resonance NMR experiments, we establish a method for sequence-specific backbone resonance assignment of magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectra of 5–30 kDa proteins. The approach relies on perdeuteration, amide ²H/¹H exchange, high magnetic fields, and high-spinning frequencies (ω_r/2π ≥ 60 kHz) and yields high-quality NMR data, enabling the use of automated analysis. The method is validated with five examples of proteins in different condensed states, including two microcrystalline proteins, a sedimented virus capsid, and two membrane-embedded systems. In comparison to contemporary ¹³C/¹⁵N-based methods, this approach facilitates and accelerates the MAS NMR assignment process, shortening the spectral acquisition times and enabling the use of unsupervised state-of-the-art computational data analysis protocols originally developed for solution NMR.

Automated robust and accurate assignment of protein resonances for solid state NMR

The process of resonance assignment represents a time-consuming and potentially error-prone bottleneck in structural studies of proteins by solid-state NMR (ssNMR). Software for the automation of this process is therefore of high interest. Procedures developed through the last decades for solution-state NMR are not directly applicable for ssNMR due to the inherently lower data quality caused by lower sensitivity and broader lines, leading to overlap between peaks. Recently, the first efforts towards procedures specifically aimed for ssNMR have been realized (Schmidt et al. in J Biomol NMR 56(3):243-254, 2013). Here we present a robust automatic method, which can accurately assign protein resonances using peak lists from a small set of simple 2D and 3D ssNMR experiments, applicable in cases with low sensitivity. The method is demonstrated on three uniformly (13)C, (15)N labeled biomolecules with different challenges on the assignments. In particular, for the immunoglobulin binding domain B1 of streptococcal protein G automatic assignment shows 100% accuracy for the backbone resonances and 91.8% when including all side chain carbons. It is demonstrated, by using a procedure for generating artificial spectra with increasing line widths, that our method, GAMES_ASSIGN can handle a significant amount of overlapping peaks in the assignment. The impact of including different ssNMR experiments is evaluated as well.

Site-specific structural variations accompanying tubular assembly of the HIV-1 capsid protein

The 231-residue capsid (CA) protein of human immunodeficiency virus type 1 (HIV-1) spontaneously self-assembles into tubes with a hexagonal lattice that is believed to mimic the surface lattice of conical capsid cores within intact virions. We report the results of solid-state nuclear magnetic resonance (NMR) measurements on HIV-1 CA tubes that provide new information regarding changes in molecular structure that accompany CA self-assembly, local dynamics within CA tubes, and possible mechanisms for the generation of lattice curvature. This information is contained in site-specific assignments of signals in two- and three-dimensional solid-state NMR spectra, conformation-dependent (15)N and (13)C NMR chemical shifts, detection of highly dynamic residues under solution NMR conditions, measurements of local variations in transverse spin relaxation rates of amide (1)H nuclei, and quantitative measurements of site-specific (15)N-(15)N dipole-dipole couplings. Our data show that most of the CA sequence is conformationally ordered and relatively rigid in tubular assemblies and that structures of the N-terminal domain (NTD) and the C-terminal domain (CTD) observed in solution are largely retained. However, specific segments, including the N-terminal β-hairpin, the cyclophilin A binding loop, the inter-domain linker, segments involved in intermolecular NTD-CTD interactions, and the C-terminal tail, have substantial static or dynamical disorder in tubular assemblies. Other segments, including the 310-helical segment in CTD, undergo clear conformational changes. Structural variations associated with curvature of the CA lattice appear to be localized in the inter-domain linker and intermolecular NTD-CTD interface, while structural variations within NTD hexamers, around local 3-fold symmetry axes, and in CTD-CTD dimerization interfaces are less significant.

Molecular structure of β-amyloid fibrils in Alzheimer's disease brain tissue

In vitro, β-amyloid (Aβ) peptides form polymorphic fibrils, with molecular structures that depend on growth conditions, plus various oligomeric and protofibrillar aggregates. Here, we investigate structures of human brain-derived Aβ fibrils, using seeded fibril growth from brain extract and data from solid-state nuclear magnetic resonance and electron microscopy. Experiments on tissue from two Alzheimer's disease (AD) patients with distinct clinical histories showed a single predominant 40 residue Aβ (Aβ40) fibril structure in each patient; however, the structures were different from one another. A molecular structural model developed for Aβ40 fibrils from one patient reveals features that distinguish in-vivo- from in-vitro-produced fibrils. The data suggest that fibrils in the brain may spread from a single nucleation site, that structural variations may correlate with variations in AD, and that structure-specific amyloid imaging agents may be an important future goal.

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.

Automated solid-state NMR resonance assignment of protein microcrystals and amyloids

Solid-state NMR is an emerging structure determination technique for crystalline and non-crystalline protein assemblies, e.g., amyloids. Resonance assignment constitutes the first and often very time-consuming step to a structure. We present ssFLYA, a generally applicable algorithm for automatic assignment of protein solid-state NMR spectra. Application to microcrystals of ubiquitin and the Ure2 prion C-terminal domain, as well as amyloids of HET-s(218-289) and α-synuclein yielded 88-97 % correctness for the backbone and side-chain assignments that are classified as self-consistent by the algorithm, and 77-90 % correctness if also assignments classified as tentative by the algorithm are included.

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

We introduce a Python-based program that utilizes the large database of (13)C and (15)N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D (13)C-(13)C, (15)N-(13)C, or 3D (15)N-(13)C-(13)C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D (13)C-(13)C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40-60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO-Cα-Cβ or N-Cα-Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.

Rapid prediction of multi-dimensional NMR data sets

We present a computational environment for Fast Analysis of multidimensional NMR DAta Sets (FANDAS) that allows assembling multidimensional data sets from a variety of input parameters and facilitates comparing and modifying such "in silico" data sets during the various stages of the NMR data analysis. The input parameters can vary from (partial) NMR assignments directly obtained from experiments to values retrieved from in silico prediction programs. The resulting predicted data sets enable a rapid evaluation of sample labeling in light of spectral resolution and structural content, using standard NMR software such as Sparky. In addition, direct comparison to experimental data sets can be used to validate NMR assignments, distinguish different molecular components, refine structural models or other parameters derived from NMR data. The method is demonstrated in the context of solid-state NMR data obtained for the cyclic nucleotide binding domain of a bacterial cyclic nucleotide-gated channel and on membrane-embedded sensory rhodopsin II. FANDAS is freely available as web portal under WeNMR ( http://www.wenmr.eu/services/FANDAS ).

Restraints on backbone conformations in solid state NMR studies of uniformly labeled proteins from quantitative amide 15N-15N and carbonyl 13C-13C dipolar recoupling data

Recent structural studies of uniformly (15)N, (13)C-labeled proteins by solid state nuclear magnetic resonance (NMR) rely principally on two sources of structural restraints: (i) restraints on backbone conformation from isotropic (15)N and (13)C chemical shifts, based on empirical correlations between chemical shifts and backbone torsion angles; (ii) restraints on inter-residue proximities from qualitative measurements of internuclear dipole-dipole couplings, detected as the presence or absence of inter-residue crosspeaks in multidimensional spectra. We show that site-specific dipole-dipole couplings among (15)N-labeled backbone amide sites and among (13)C-labeled backbone carbonyl sites can be measured quantitatively in uniformly-labeled proteins, using dipolar recoupling techniques that we call (15)N-BARE and (13)C-BARE (BAckbone REcoupling), and that the resulting data represent a new source of restraints on backbone conformation. (15)N-BARE and (13)C-BARE data can be incorporated into structural modeling calculations as potential energy surfaces, which are derived from comparisons between experimental (15)N and (13)C signal decay curves, extracted from crosspeak intensities in series of two-dimensional spectra, with numerical simulations of the (15)N-BARE and (13)C-BARE measurements. We demonstrate this approach through experiments on microcrystalline, uniformly (15)N, (13)C-labeled protein GB1. Results for GB1 show that (15)N-BARE and (13)C-BARE restraints are complementary to restraints from chemical shifts and inter-residue crosspeaks, improving both the precision and the accuracy of calculated structures.

Segmental polymorphism in a functional amyloid

Although amyloid fibrils are generally considered to be causative or contributing agents in amyloid diseases, several amyloid fibrils are also believed to have biological functions. Among these are fibrils formed by Pmel17 within melanosomes, which act as a template for melanin deposition. We use solid-state NMR to show that the molecular structures of fibrils formed by the 130-residue pseudo-repeat domain Pmel17:RPT are polymorphic even within the biologically relevant pH range. Thus, biological function in amyloid fibrils does not necessarily imply a unique molecular structure. Solid-state NMR spectra of three Pmel17:RPT polymorphs show that in all cases, only a subset (~30%) of the full amino acid sequence contributes to the immobilized fibril core. Although the repetitive nature of the sequence and incomplete spectral resolution prevent the determination of unique chemical shift assignments from two- and three-dimensional solid-state NMR spectra, we use a Monte Carlo assignment algorithm to identify protein segments that are present in or absent from the fibril core. The results show that the identity of the core-forming segments varies from one polymorph to another, a phenomenon known as segmental polymorphism.