Automated error-tolerant macromolecular structure determination from multidimensional nuclear Overhauser enhancement spectra and chemical shift assignments: improved robustness and performance of the PASD algorithm

Automated fibril structure calculations in Xplor-NIH

Amyloid fibrils are protein assemblies that are pathologically linked to neurodegenerative diseases. Fibril structures can aid development of highly specific ligands for diagnostic imaging and therapeutics. Solid-state NMR (SSNMR) is a viable approach to solving fibril structures; however, most SSNMR protocols require manual analysis of extensive spectral data, presenting a major bottleneck to determining structures. Standard automation; routines fall short for symmetric multimeric assemblies like amyloids due to high cross peak degeneracy and the need to account for multiple protein subunits. Here, we employ the probabilistic assignment for structure determination protocol in conjunction with strict; symmetry in Xplor-NIH structure determination software, demonstrating the methodology using data from a previous structure of an α-synuclein (Asyn) fibril implicated in Parkinson disease. The automated protocol generated a structure of comparable, if not superior, quality in a few days of computational time, reducing the manual effort required; to solve amyloid structures by SSNMR.

Xplor-NIH: Better parameters and protocols for NMR protein structure determination

The present work describes an update to the protein covalent geometry and atomic radii parameters in the Xplor-NIH biomolecular structure determination package. In combination with an improved treatment of selected non-bonded interactions between atoms three bonds apart, such as those involving methyl hydrogens, and a previously developed term that affects the system's gyration volume, the new parameters are tested using structure calculations on 30 proteins with restraints derived from nuclear magnetic resonance data. Using modern structure validation criteria, including several formally adopted by the Protein Data Bank, and a clear measure of structural accuracy, the results show superior performance relative to previous Xplor-NIH implementations. Additionally, the Xplor-NIH structures compare favorably against originally determined NMR models.

Structure of alpha-synuclein fibrils derived from human Lewy body dementia tissue

The defining feature of Parkinson disease (PD) and Lewy body dementia (LBD) is the accumulation of alpha-synuclein (Asyn) fibrils in Lewy bodies and Lewy neurites. Here we develop and validate a method to amplify Asyn fibrils extracted from LBD postmortem tissue samples and use solid state nuclear magnetic resonance (SSNMR) studies to determine atomic resolution structure. Amplified LBD Asyn fibrils comprise a mixture of single protofilament and two protofilament fibrils with very low twist. The protofilament fold is highly similar to the fold determined by a recent cryo-electron microscopy study for a minority population of twisted single protofilament fibrils extracted from LBD tissue. These results expand the structural characterization of LBD Asyn fibrils and approaches for studying disease mechanisms, imaging agents and therapeutics targeting Asyn.

Tuning sterol extraction kinetics yields a renal-sparing polyene antifungal

Decades of previous efforts to develop renal-sparing polyene antifungals were misguided by the classic membrane permeabilization model1. Recently, the clinically vital but also highly renal-toxic small-molecule natural product amphotericin B was instead found to kill fungi primarily by forming extramembraneous sponge-like aggregates that extract ergosterol from lipid bilayers2-6. Here we show that rapid and selective extraction of fungal ergosterol can yield potent and renal-sparing polyene antifungals. Cholesterol extraction was found to drive the toxicity of amphotericin B to human renal cells. Our examination of high-resolution structures of amphotericin B sponges in sterol-free and sterol-bound states guided us to a promising structural derivative that does not bind cholesterol and is thus renal sparing. This derivative was also less potent because it extracts ergosterol more slowly. Selective acceleration of ergosterol extraction with a second structural modification yielded a new polyene, AM-2-19, that is renal sparing in mice and primary human renal cells, potent against hundreds of pathogenic fungal strains, resistance evasive following serial passage in vitro and highly efficacious in animal models of invasive fungal infections. Thus, rational tuning of the dynamics of interactions between small molecules may lead to better treatments for fungal infections that still kill millions of people annually7,8 and potentially other resistance-evasive antimicrobials, including those that have recently been shown to operate through supramolecular structures that target specific lipids9.

Structure of alpha-synuclein fibrils derived from human Lewy body dementia tissue

The defining feature of Parkinson disease (PD) and Lewy body dementia (LBD) is the accumulation of alpha-synuclein (Asyn) fibrils in Lewy bodies and Lewy neurites. We developed and validated a novel method to amplify Asyn fibrils extracted from LBD postmortem tissue samples and used solid state nuclear magnetic resonance (SSNMR) studies to determine atomic resolution structure. Amplified LBD Asyn fibrils comprise two protofilaments with pseudo-21 helical screw symmetry, very low twist and an interface formed by antiparallel beta strands of residues 85-93. The fold is highly similar to the fold determined by a recent cryo-electron microscopy study for a minority population of twisted single protofilament fibrils extracted from LBD tissue. These results expand the structural landscape of LBD Asyn fibrils and inform further studies of disease mechanisms, imaging agents and therapeutics targeting Asyn.

Assessment of the conformational profile of bombesin by computational methods

In the present work, the results of a computational study aimed at assessing the conformational profile of bombesin are reported. The conformational space of the peptide was sampled by means of a 4 μs accelerated molecular dynamics simulation in water, using an explicit solvent model. The results were analyzed using Principal Component Analysis to get essential information on peptide fluctuations, along with cluster analysis to characterize different conformations in the sample. Analysis of the results suggests that the peptide adopts helical structures at the C-terminus that tend to unwind at the end of the peptide chain, since there are many structures exhibiting only two turns of a helix at the central segment of the peptide. In addition, the peptide also adopts hairpin turn structures at the N-terminus. Results of the simulation were confronted with available NMR results in a 2,2,2-trifluoroethanol/water (30% v/v) solution. Distances deduced form NOEs experiments only provide support to the presence of helical conformations that represent the most populated structures in the simulation. The absence of other conformations in the NMR experiments can be explained to be due to the α-helix enhancing nature of the solvent used in the experiments.

Xplor-NIH for molecular structure determination from NMR and other data sources

Xplor-NIH is a popular software package for biomolecular structure determination from nuclear magnetic resonance (NMR) and other data sources. Here, some of Xplor-NIH's most useful data-associated energy terms are reviewed, including newer alternative options for using residual dipolar coupling data in structure calculations. Further, we discuss new developments in the implementation of strict symmetry for the calculation of symmetric homo-oligomers, and in the representation of the system as an ensemble of structures to account for motional effects. Finally, the different available force fields are presented, among other Xplor-NIH capabilities.

Creatine CEST MRI for Differentiating Gliomas with Different Degrees of Aggressiveness

PURPOSE

Creatine (Cr) is a major metabolite in the bioenergetic system. Measurement of Cr using conventional MR spectroscopy (MRS) suffers from low spatial resolution and relatively long acquisition times. Creatine chemical exchange saturation transfer (CrCEST) magnetic resonance imaging (MRI) is an emerging molecular imaging method for tissue Cr measurements. Our previous study showed that the CrCEST contrast, obtained through multicomponent Z-spectral fitting, was lower in tumors compared to normal brain, which further reduced with tumor progression. The current study was aimed to investigate if CrCEST MRI can also be useful for differentiating gliomas with different degrees of aggressiveness.

PROCEDURES

Intracranial 9L gliosarcoma and F98 glioma bearing rats with matched tumor size were scanned with a 9.4 T MRI scanner at two time points. CEST Z-spectra were collected using a customized sequence with a frequency-selective rectangular saturation pulse (B1 = 50 Hz, duration = 3 s) followed by a single-shot readout. Z spectral data were fitted pixel-wise with five Lorentzian functions, and maps of CrCEST peak amplitude, linewidth, and integral were produced. For comparison, single-voxel proton MR spectroscopy (1H-MRS) was performed to quantify and compare the total Cr concentration in the tumor.

RESULTS

CrCEST contrasts decreased with tumor progression from weeks 3 to 4 in both 9L and F98 phenotypes. More importantly, F98 tumors had significantly lower CrCEST integral compared to 9L tumors. On the other hand, integrals of other Z-spectral components were unable to differentiate both tumor progression and phenotype with limited sample size.

CONCLUSIONS

Given that F98 is a more aggressive tumor than 9L, this study suggests that CrCEST MRI may help differentiate gliomas with different aggressiveness.

NMR-based automated protein structure determination

NMR spectra analysis for protein structure determination can now in many cases be performed by automated computational methods. This overview of the computational methods for NMR protein structure analysis presents recent automated methods for signal identification in multidimensional NMR spectra, sequence-specific resonance assignment, collection of conformational restraints, and structure calculation, as implemented in the CYANA software package. These algorithms are sufficiently reliable and integrated into one software package to enable the fully automated structure determination of proteins starting from NMR spectra without manual interventions or corrections at intermediate steps, with an accuracy of 1-2 Å backbone RMSD in comparison with manually solved reference structures.

The membrane localization domains of two distinct bacterial toxins form a 4-helix-bundle in solution

Membrane localization domain (MLD) was first proposed for a 4-helix-bundle motif in the crystal structure of the C1 domain of Pasteurella multocida toxin (PMT). This structure motif is also found in the crystal structures of several clostridial glycosylating toxins (TcdA, TcdB, TcsL, and TcnA). The Ras/Rap1-specific endopeptidase (RRSP) module of the multifunctional autoprocessing repeats-in-toxins (MARTX) toxin produced by Vibrio vulnificus has sequence homology to the C1-C2 domains of PMT, including a putative MLD. We have determined the solution structure for the MLDs in PMT and in RRSP using solution state NMR. We conclude that the MLDs in these two toxins assume a 4-helix-bundle structure in solution.

NMRFAM-SDF: a protein structure determination framework

The computationally demanding nature of automated NMR structure determination necessitates a delicate balancing of factors that include the time complexity of data collection, the computational complexity of chemical shift assignments, and selection of proper optimization steps. During the past two decades the computational and algorithmic aspects of several discrete steps of the process have been addressed. Although no single comprehensive solution has emerged, the incorporation of a validation protocol has gained recognition as a necessary step for a robust automated approach. The need for validation becomes even more pronounced in cases of proteins with higher structural complexity, where potentially larger errors generated at each step can propagate and accumulate in the process of structure calculation, thereby significantly degrading the efficacy of any software framework. This paper introduces a complete framework for protein structure determination with NMR--from data acquisition to the structure determination. The aim is twofold: to simplify the structure determination process for non-NMR experts whenever feasible, while maintaining flexibility by providing a set of modules that validate each step, and to enable the assessment of error propagations. This framework, called NMRFAM-SDF (NMRFAM-Structure Determination Framework), and its various components are available for download from the NMRFAM website (http://nmrfam.wisc.edu/software.htm).

CEST signal at 2ppm (CEST@2ppm) from Z-spectral fitting correlates with creatine distribution in brain tumor

In general, multiple components such as water direct saturation, magnetization transfer (MT), chemical exchange saturation transfer (CEST) and aliphatic nuclear Overhauser effect (NOE) contribute to the Z-spectrum. The conventional CEST quantification method based on asymmetrical analysis may lead to quantification errors due to the semi-solid MT asymmetry and the aliphatic NOE located on a single side of the Z-spectrum. Fitting individual contributors to the Z-spectrum may improve the quantification of each component. In this study, we aim to characterize the multiple exchangeable components from an intracranial tumor model using a simplified Z-spectral fitting method. In this method, the Z-spectrum acquired at low saturation RF amplitude (50 Hz) was modeled as the summation of five Lorentzian functions that correspond to NOE, MT effect, bulk water, amide proton transfer (APT) effect and a CEST peak located at +2 ppm, called CEST@2ppm. With the pixel-wise fitting, the regional variations of these five components in the brain tumor and the normal brain tissue were quantified and summarized. Increased APT effect, decreased NOE and reduced CEST@2ppm were observed in the brain tumor compared with the normal brain tissue. Additionally, CEST@2ppm decreased with tumor progression. CEST@2ppm was found to correlate with the creatine concentration quantified with proton MRS. Based on the correlation curve, the creatine contribution to CEST@2ppm was quantified. The CEST@2ppm signal could be a novel imaging surrogate for in vivo creatine, the important bioenergetics marker. Given its noninvasive nature, this CEST MRI method may have broad applications in cancer bioenergetics.

Prefusion structure of syntaxin-1A suggests pathway for folding into neuronal trans-SNARE complex fusion intermediate

The assembly of the three neuronal soluble N-ethylmaleimide-sensitive factor attachment protein (SNAP) receptor (SNARE) proteins synaptobrevin 2, syntaxin-1A, and SNAP-25 is the key step that leads to exocytotic fusion of synaptic vesicles. In the fully assembled SNARE complex, these three proteins form a coiled-coil four-helix bundle structure by interaction of their respective SNARE motifs. Although biochemical and mutational analyses strongly suggest that the heptad-repeat SNARE motifs zipper into the final structure, little is known about the prefusion state of individual membrane-bound SNAREs and how they change conformation from the unzippered prefusion to the zippered postfusion state in a membrane environment. We have solved the solution NMR structure of micelle-bound syntaxin-1A in its prefusion conformation. In addition to the transmembrane helix, the SNARE motif consists of two well-ordered, membrane-bound helices separated by the "0-layer" residue Gln226. This unexpected structural order of the N- and C-terminal halves of the uncomplexed SNARE motif suggests the formation of partially zippered SNARE complex intermediates, with the 0-layer serving as a proofreading site for correct SNARE assembly. Interferometric fluorescence measurements in lipid bilayers confirm that the open SNARE motif helices of syntaxin interact with lipid bilayers and that association with the other target-membrane SNARE SNAP-25 lifts the SNARE motif off the membrane as a critical prerequisite for SNARE complex assembly and membrane fusion.

Comprehensive automation for NMR structure determination of proteins

This chapter gives an overview of automated protein structure determination by nuclear magnetic resonance (NMR) with the UNIO protocol that enables high to full automation of all NMR data analysis steps involved. Four established algorithms, namely, the MATCH algorithm for sequence-specific resonance assignment, the ASCAN algorithm for side-chain resonance assignment, the CANDID algorithm for NOE assignment, and the ATNOS algorithm for signal identification in NMR spectra, are assembled into three principal UNIO NMR data analysis components (MATCH, ATNOS/ASCAN, and ATNOS/CANDID) that are accessed thanks to a particularly intuitive and flexible, yet powerful graphical user interface (GUI). UNIO is designed to work independently or in association with other NMR software. The principal data analysis components for sequence-specific backbone, side-chain and NOE assignment may be run separately or out of sequence. User-intervention at individual stages is encouraged and facilitated by graphical tools included for the preparation, analysis, validation, and subsequent presentation of the NMR structure.

Protein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY data

One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called NASCA: (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), NASCA: extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that NASCA: assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by NASCA: have backbone RMSD 0.8-1.5 Å from the reference structures determined by traditional NMR approaches.

Binding site identification and structure determination of protein-ligand complexes by NMR a semiautomated approach

Over the last 15 years, the role of NMR spectroscopy in the lead identification and optimization stages of pharmaceutical drug discovery has steadily increased. NMR occupies a unique niche in the biophysical analysis of drug-like compounds because of its ability to identify binding sites, affinities, and ligand poses at the level of individual amino acids without necessarily solving the structure of the protein-ligand complex. However, it can also provide structures of flexible proteins and low-affinity (K(d)>10(-6)M) complexes, which often fail to crystallize. This chapter emphasizes a throughput-focused protocol that aims to identify practical aspects of binding site characterization, automated and semiautomated NMR assignment methods, and structure determination of protein-ligand complexes by NMR.

Solution structure of the 128 kDa enzyme I dimer from Escherichia coli and its 146 kDa complex with HPr using residual dipolar couplings and small- and wide-angle X-ray scattering

The solution structures of free Enzyme I (EI, ∼128 kDa, 575 × 2 residues), the first enzyme in the bacterial phosphotransferase system, and its complex with HPr (∼146 kDa) have been solved using novel methodology that makes use of prior structural knowledge (namely, the structures of the dimeric EIC domain and the isolated EIN domain both free and complexed to HPr), combined with residual dipolar coupling (RDC), small- (SAXS) and wide- (WAXS) angle X-ray scattering and small-angle neutron scattering (SANS) data. The calculational strategy employs conjoined rigid body/torsion/Cartesian simulated annealing, and incorporates improvements in calculating and refining against SAXS/WAXS data that take into account complex molecular shapes in the description of the solvent layer resulting in a better representation of the SAXS/WAXS data. The RDC data orient the symmetrically related EIN domains relative to the C(2) symmetry axis of the EIC dimer, while translational, shape, and size information is provided by SAXS/WAXS. The resulting structures are independently validated by SANS. Comparison of the structures of the free EI and the EI-HPr complex with that of the crystal structure of a trapped phosphorylated EI intermediate reveals large (∼70-90°) hinge body rotations of the two subdomains comprising the EIN domain, as well as of the EIN domain relative to the dimeric EIC domain. These large-scale interdomain motions shed light on the structural transitions that accompany the catalytic cycle of EI.

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.