NMR structure determination for larger proteins using backbone-only data

Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid

Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of biomacromolecules. Consequently, we are approaching an era in which vigorous applications of these methods are more widespread in research, including functional elucidation of biomacromolecules and drug discovery. However, multidimensional ssNMR methods are not as advanced as solution NMR methods, especially for automated data analysis. This article describes how a newly developed Graph_Robot module, implemented in MagRO-NMRViewJ, evolved from integrated tools for NMR data analysis named Kujira (developed by Kobayashi et al. [1]). These packaged tools systematically utilize flexible, sophisticated, yet simple libraries that facilitate only for solution-NMR data analysis, offering an intuitive interface accessible even to novice users. In this study, semi-automated assignments of backbone and side chain signals of ssNMR datasets for uniformly 13C/15N labeled aquaporin Z and 42-residue amyloid-β fibril were examined as examples to demonstrate how Graph_Robot can expedite the visual inspection and handling of multidimensional ssNMR spectral data. In addition, the functionality of the Graph_Robot system enables a computer to interpret the behavior of magnetization transfer based on a finite automaton model.

Quantitative Characterization of Chain-Flipping of Acyl Carrier Protein of Escherichia coli Using Chemical Exchange NMR

The acyl carrier protein of Escherichia coli, termed AcpP, is a prototypical example of type II fatty acid synthase systems found in many bacteria. It serves as a central hub by accepting diverse acyl moieties (4-18 carbons) and shuttling them between its multiple enzymatic partners to generate fatty acids. Prior structures of acyl-AcpPs established that thioester-linked acyl cargos are sequestered within AcpP's hydrophobic lumen. In contrast, structures of enzyme-bound acyl-AcpPs showed translocation of AcpP-tethered acyl chains into the active sites of enzymes. The mechanistic underpinnings of this conformational interplay, termed chain-flipping, are unclear. Here, using heteronuclear NMR spectroscopy, we reveal that AcpP-tethered acyl chains (6-10 carbons) spontaneously adopt lowly populated solvent-exposed conformations. To this end, we devised a new strategy to replace AcpP's thioester linkages with 15N-labeled amide bonds, which facilitated direct "visualization" of these excited states using NMR chemical exchange saturation transfer and relaxation dispersion measurements. Global fitting of the corresponding data yielded kinetic rate constants of the underlying equilibrium and populations and lifetimes of solvent-exposed states. The latter were influenced by acyl chain composition and ranged from milliseconds to submilliseconds for chains containing six, eight, and ten carbons, owing to their variable interactions with AcpP's hydrophobic core. Although transient, the exposure of AcpP-tethered acyl chains to the solvent may allow relevant enzymes to gain access to its active thioester, and the enzyme-induced selection of this conformation will culminate in the production of fatty acids.

Key arginine residues in R2D2 dsRBD1 and dsRBD2 lead the siRNA recognition in Drosophila melanogaster RNAi pathway

In Drosophila melanogaster, Dcr-2:R2D2 heterodimer binds to the 21 nucleotide siRNA duplex to form the R2D2/Dcr-2 Initiator (RDI) complex, which is critical for the initiation of siRNA-induced silencing complex (RISC) assembly. During RDI complex formation, R2D2, a protein that contains three dsRNA binding domains (dsRBD), senses two aspects of the siRNA: thermodynamically more stable end (asymmetry sensing) and the 5'-phosphate (5'-P) recognition. Despite several detailed studies to date, the molecular determinants arising from R2D2 for performing these two tasks remain elusive. In this study, we have performed structural, biophysical, and biochemical characterization of R2D2 dsRBDs. We found that the solution NMR-derived structure of R2D2 dsRBD1 yielded a canonical α1-β1-β2-β3-α2 fold, wherein two arginine salt bridges provide additional stability to the R2D2 dsRBD1. Furthermore, we show that R2D2 dsRBD1 interacts with thermodynamically asymmetric siRNA duplex independent of its 5'-phosphorylation state, whereas R2D2 dsRBD2 prefers to interact with 5'-P siRNA duplex. The mutation of key arginine residues, R53 and R101, in concatenated dsRBDs of R2D2 results in a significant loss of siRNA duplex recognition. Our study deciphers the active roles of R2D2 dsRBDs by showing that dsRBD1 initiates siRNA recognition, whereas dsRBD2 senses 5'-phosphate as an authentic mark on functional siRNA.

N4BP1 functions as a dimerization-dependent linear ubiquitin reader which regulates TNF signalling

Signalling through TNFR1 modulates proinflammatory gene transcription and programmed cell death, and its impairment causes autoimmune diseases and cancer. NEDD4-binding protein 1 (N4BP1) is a critical suppressor of proinflammatory cytokine production that acts as a regulator of innate immune signalling and inflammation. However, our current understanding about the molecular properties that enable N4BP1 to exert its suppressive potential remain limited. Here, we show that N4BP1 is a novel linear ubiquitin reader that negatively regulates NFκB signalling by its unique dimerization-dependent ubiquitin-binding module that we named LUBIN. Dimeric N4BP1 strategically positions two non-selective ubiquitin-binding domains to ensure preferential recognition of linear ubiquitin. Under proinflammatory conditions, N4BP1 is recruited to the nascent TNFR1 signalling complex, where it regulates duration of proinflammatory signalling in LUBIN-dependent manner. N4BP1 deficiency accelerates TNFα-induced cell death by increasing complex II assembly. Under proapoptotic conditions, caspase-8 mediates proteolytic processing of N4BP1, resulting in rapid degradation of N4BP1 by the 26 S proteasome, and acceleration of apoptosis. In summary, our findings demonstrate that N4BP1 dimerization creates a novel type of ubiquitin reader that selectively recognises linear ubiquitin which enables the timely and coordinated regulation of TNFR1-mediated inflammation and cell death.

Computational modeling multiple conformational states of proteins with residual dipolar coupling data

Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Nuclear Magnetic Shielding Constants with the Polarizable Density Embedding Model

We extend the polarizable density embedding (PDE) model to support the calculation of nuclear magnetic resonance (NMR) shielding constants using gauge-including atomic orbitals (GIAOs) within a density functional theory (DFT) framework. The PDE model divides the total system into fragments, describing some by quantum mechanics (QM) and the others through an embedding model. The PDE model uses anisotropic polarizabilities, inter-fragment two-electron Coulomb integrals, and a non-local repulsion operator to emulate the QM effects. The terms involving Coulomb integrals are straightforwardly extended with GIAOs. In contrast, we consider two approaches to handle the gauge dependency of the non-local operator, employing either simple symmetrization or a gauge transformation. We find the latter approach to be most stable with respect to increasing the basis set size of the QM region. We examine the accuracy of the PDE model for calculating NMR shielding constants on several solutes in a water solution. The performance is compared with the classical polarizable embedding (PE) model in addition to supermolecular reference calculations. Based on these systems, we address the basis set convergence characteristics and the QM region size requirements. Furthermore, we investigate the performance of the PDE model for a system with significant electron spill-out. In many cases, we find that the PDE model outperforms the PE model, especially regarding the accuracy of nuclear shielding constants when using small QM region sizes and in systems with significant electron spill-out.

Agl24 is an ancient archaeal homolog of the eukaryotic N-glycan chitobiose synthesis enzymes

Protein N-glycosylation is a post-translational modification found in organisms of all domains of life. The crenarchaeal N-glycosylation begins with the synthesis of a lipid-linked chitobiose core structure, identical to that in Eukaryotes, although the enzyme catalyzing this reaction remains unknown. Here, we report the identification of a thermostable archaeal β-1,4-N-acetylglucosaminyltransferase, named archaeal glycosylation enzyme 24 (Agl24), responsible for the synthesis of the N-glycan chitobiose core. Biochemical characterization confirmed its function as an inverting β-D-GlcNAc-(1→4)-α-D-GlcNAc-diphosphodolichol glycosyltransferase. Substitution of a conserved histidine residue, found also in the eukaryotic and bacterial homologs, demonstrated its functional importance for Agl24. Furthermore, bioinformatics and structural modeling revealed similarities of Agl24 to the eukaryotic Alg14/13 and a distant relation to the bacterial MurG, which are catalyzing the same or a similar reaction, respectively. Phylogenetic analysis of Alg14/13 homologs indicates that they are ancient in Eukaryotes, either as a lateral transfer or inherited through eukaryogenesis.

Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

Structural Analysis of the Effect of Asn107Ser Mutation on Alg13 Activity and Alg13-Alg14 Complex Formation and Expanding the Phenotypic Variability of ALG13-CDG

Congenital Disorders of Glycosylation (CDG) are multisystemic metabolic disorders showing highly heterogeneous clinical presentation, molecular etiology, and laboratory results. Here, we present different transferrin isoform patterns (obtained by isoelectric focusing) from three female patients harboring the ALG13 c.320A>G mutation. Contrary to other known variants of type I CDGs, where transferrin isoelectric focusing revealed notably increased asialo- and disialotransferrin fractions, a normal glycosylation pattern was observed in the probands. To verify this data and give novel insight into this variant, we modeled the human Alg13 protein and analyzed the dynamics of the apo structure and the complex with the UDP-GlcNAc substrate. We also modeled the Alg13-Alg14 heterodimer and ran multiple simulations of the complex in the presence of the substrate. Finally, we proposed a plausible complex formation mechanism.

Recent Advances in Spectroscopic Techniques for the Analysis of Microplastics in Food

Microplastic pollution has become a worldwide concern in aquatic and terrestrial environments. Microplastics could also enter the food chain, causing potential harm to human health. To facilitate the risk assessment of microplastics to humans, it is critically important to have a reliable analytical technique to detect, quantify, and identify microplastics of various materials, sizes, and shapes from environmental, agricultural, and food matrices. Spectroscopic techniques, mainly vibrational spectroscopy (Raman and infrared), are commonly used techniques for microplastic analysis. This review focuses on recent advances of these spectroscopic techniques for the analysis of microplastics in food. The fundamental, recent technical advances of the spectroscopic techniques and their advantages and limitations were summarized. The food sample pretreatment methods and recent applications for detecting and quantifying microplastics in different types of food were reviewed. In addition, the current technical challenges and future research directions were discussed. It is anticipated that the advances in instrument development and methodology innovation will enable spectroscopic techniques to solve critical analytical challenges in microplastic analysis in food, which will facilitate the reliable risk assessment.

Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT

Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of internal dynamics. In this simulation, DHFR was segmented into 7 regions where 4 of the fragments were fixed with respect to each other, two regions underwent rigid-body dynamics, and one region experienced uncorrelated and melting event. The two dynamical and rigid-body segments experienced an average orientational modification of 7° and 12° respectively. Observable RDC data for backbone C′-N, N-HN, and C′-HN were generated from 102 uniformly sampled frames that described the molecular trajectory. The structure calculation of DHFR with REDCRAFT by using traditional Ramachandran restraint produced a structure with 29 Å of structural difference measured over the backbone atoms (bb-rmsd) over the entire length of the protein and an average bb-rmsd of more than 4.7 Å over each of the dynamical fragments. The same exercise repeated with context-specific dihedral restraints generated by PDBMine produced a structure with bb-rmsd of 21 Å over the entire length of the protein but with bb-rmsd of less than 3 Å over each of the fragments. Finally, utilization of the Dynamic Profile generated by REDCRAFT allowed for the identification of different dynamical regions of the protein and the recovery of individual fragments with bb-rmsd of less than 1 Å. Following the recovery of the fragments, our assembly procedure of domains (larger segments consisting of multiple fragments with a common dynamical profile) correctly assembled the four fragments that are rigid with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure could be defined. In conclusion, our approach was successful in identifying the dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible.

Bipartite binding and partial inhibition links DEPTOR and mTOR in a mutually antagonistic embrace

The mTORC1 kinase complex regulates cell growth, proliferation, and survival. Because mis-regulation of DEPTOR, an endogenous mTORC1 inhibitor, is associated with some cancers, we reconstituted mTORC1 with DEPTOR to understand its function. We find that DEPTOR is a unique partial mTORC1 inhibitor that may have evolved to preserve feedback inhibition of PI3K. Counterintuitively, mTORC1 activated by RHEB or oncogenic mutation is much more potently inhibited by DEPTOR. Although DEPTOR partially inhibits mTORC1, mTORC1 prevents this inhibition by phosphorylating DEPTOR, a mutual antagonism that requires no exogenous factors. Structural analyses of the mTORC1/DEPTOR complex showed DEPTOR's PDZ domain interacting with the mTOR FAT region, and the unstructured linker preceding the PDZ binding to the mTOR FRB domain. The linker and PDZ form the minimal inhibitory unit, but the N-terminal tandem DEP domains also significantly contribute to inhibition.

An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling

Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.

A review of fruit juice authenticity assessments: Targeted and untargeted analyses

Fruit juices are becoming more and more popular in the whole world. However, the increasing fruit juice fraud cases are undermining the healthy development of fruit juice industry. Fruit juice authenticity represents an important food quality and safety parameter. Many techniques have been applied in fruit juices authenticity assessment. The purpose of this review is to provide a research overview of the targeted and untargeted analyses of fruit authentication, and a method selection guide for fruit juice authenticity assessment. Targeted markers, such as stable isotopes, phenolics, carbohydrates, organic acids, volatile components, DNAs, amino acids and proteins, as well as carotenoids, will be discussed. And untargeted techniques, including liquid/gas chromatography-mass spectrometer, nuclear magnetic resonance, infrared spectroscopy, inductively-coupled plasma-mass spectrometry/optical emission spectrometer, fluorescence spectra, electronic sensors and others, will be reviewed. The emerging untargeted for novel targeted marker analysis will be also summarized.

Structure of membrane diacylglycerol kinase in lipid bilayers

Diacylglycerol kinase (DgkA) is a small integral membrane protein, responsible for the ATP-dependent phosphorylation of diacylglycerol to phosphatidic acid. Its structures reported in previous studies, determined in detergent micelles by solution NMR and in monoolein cubic phase by X-ray crystallography, differ significantly. These differences point to the need to validate these detergent-based structures in phospholipid bilayers. Here, we present a well-defined homo-trimeric structure of DgkA in phospholipid bilayers determined by magic angle spinning solid-state NMR (ssNMR) spectroscopy, using an approach combining intra-, inter-molecular paramagnetic relaxation enhancement (PRE)-derived distance restraints and CS-Rosetta calculations. The DgkA structure determined in lipid bilayers is different from the solution NMR structure. In addition, although ssNMR structure of DgkA shows a global folding similar to that determined by X-ray, these two structures differ in monomeric symmetry and dynamics. A comparative analysis of DgkA structures determined in three different detergent/lipid environments provides a meaningful demonstration of the influence of membrane mimetic environments on the structure and dynamics of membrane proteins.

Jianping Li et al. present the homo-trimeric structure of the small integral membrane protein diacylglycerol kinase (DgkA) in phospholipid bilayers determined by magic angle spinning solid-state NMR spectroscopy. They compare the structure with structures solved by solution NMR and X-ray crystallography and provide insights into the influence of membrane mimetic environments on membrane proteins.

Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR

Determination of structure of RNA via NMR is complicated in large part by the lack of a precise parameterization linking the observed chemical shifts to the underlying geometric parameters. In contrast to proteins, where numerous high-resolution crystal structures serve as coordinate templates for this mapping, such models are rarely available for smaller oligonucleotides accessible via NMR, or they exhibit crystal packing and counter-ion binding artifacts that prevent their use for the chemical shifts analysis. On the other hand, NMR-determined structures of RNA often are not solved at the density of restraints required to precisely define the variable degrees of freedom. In this study we sidestep the problems of direct parameterization of the RNA chemical shifts/structure relationship and examine the effects of imposing local fragmental coordinate similarity restraints based on similarities of the experimental secondary ribose ¹³C/¹H chemical shifts instead. The effect of such chemical shift similarity (CSS) restraints on the structural accuracy is assessed via residual dipolar coupling (RDC)-based cross-validation. Improvements in the coordinate accuracy are observed for all of the six RNA constructs considered here as test cases, which argues for routine inclusion of these terms during NMR-based oligonucleotide structure determination. Such accuracy improvements are expected to facilitate derivation of the chemical shift/structure relationships for RNA.

Neutral Peptides in the Gas Phase: Conformation and Aggregation Issues

Combined IR and UV laser spectroscopic techniques in molecular beams merged with theoretical approaches have proven to be an ideal tool to elucidate intrinsic structural properties on a molecular level. It offers the possibility to analyze structural changes, in a controlled molecular environment, when successively adding aggregation partners. By this, it further makes these techniques a valuable starting point for a bottom-up approach in understanding the forces shaping larger molecular systems. This bottom-up approach was successfully applied to neutral amino acids starting around the 1990s. Ever since, experimental and theoretical methods developed further, and investigations could be extended to larger peptide systems. Against this background, the review gives an introduction to secondary structures and experimental methods as well as a summary on theoretical approaches. Vibrational frequencies being characteristic probes of molecular structure and interactions are especially addressed. Archetypal biologically relevant secondary structures investigated by molecular beam spectroscopy are described, and the influences of specific peptide residues on conformational preferences as well as the competition between secondary structures are discussed. Important influences like microsolvation or aggregation behavior are presented. Beyond the linear α-peptides, the main results of structural analysis on cyclic systems as well as on β- and γ-peptides are summarized. Overall, this contribution addresses current aspects of molecular beam spectroscopy on peptides and related species and provides molecular level insights into manifold issues of chemical and biochemical relevance.

PRE-driven protein NMR structures: an alternative approach in highly paramagnetic systems

Metalloproteins play key roles across biology, and knowledge of their structure is essential to understand their physiological role. For those metalloproteins containing paramagnetic states, the enhanced relaxation caused by the unpaired electrons often makes signal detection unfeasible near the metal center, precluding adequate structural characterization right where it is more biochemically relevant. Here, we report a protein structure determination by NMR where two different sets of restraints, one containing Nuclear Overhauser Enhancements (NOEs) and another containing Paramagnetic Relaxation Enhancements (PREs), are used separately and eventually together. The protein PioC from Rhodopseudomonas palustris TIE-1 is a High Potential Iron-Sulfur Protein (HiPIP) where the [4Fe-4S] cluster is paramagnetic in both oxidation states at room temperature providing the source of PREs used as alternative distance restraints. Comparison of the family of structures obtained using NOEs only, PREs only, and the combination of both reveals that the pairwise root-mean-square deviation (RMSD) between them is similar and comparable with the precision within each family. This demonstrates that, under favorable conditions in terms of protein size and paramagnetic effects, PREs can efficiently complement and eventually replace NOEs for the structural characterization of small paramagnetic metalloproteins and de novo-designed metalloproteins by NMR. DATABASES: The 20 conformers with the lowest target function constituting the final family obtained using the full set of NMR restraints were deposited to the Protein Data Bank (PDB ID: 6XYV). The 20 conformers with the lowest target function obtained using NOEs only (PDB ID: 7A58) and PREs only (PDB ID: 7A4L) were also deposited to the Protein Data Bank. The chemical shift assignments were deposited to the BMRB (code 34487).

Solution structure of the inhibitor of cysteine proteases 1 from Entamoeba histolytica reveals a possible auto regulatory mechanism

The genome of Entamoeba histolytica encodes approximately 50 Cysteine Proteases (CPs) whose activity is regulated by two Inhibitors of Cysteine Proteases (ICPs), EhICP1 and EhICP2. The main difference between both EhICPs is the acquisition of a 17 N-terminal targeting signal in EhICP2 and three exposed cysteine residues in EhICP1. The three exposed cysteines in EhICP1 potentiate the formation of cross-linking species that drive heterogeneity. Here we solved the NMR structure of EhICP1 using a mutant protein without accessible cysteines. Our structural data shows that EhICP1 adopts an immunoglobulin fold composed of seven β-strands, and three solvent exposed loops that resemble the structures of EhICP2 and chagasin. EhICP1 and EhICP2 are able to inhibit the archetypical cysteine protease papain by intercalating their BC loops into the protease active site independently of the character of the residue (serine or threonine) responsible to interact with the active site of papain. EhICP1 and EhICP2 present signals of functional divergence as they clustered in different clades. Two of the three exposed cysteines in EhICP1 are located at the DE loop that intercalates into the CP substrate-binding cleft. We propose that the solvent exposed cysteines of EhICP1 play a role in regulating its inhibitory activity and that in oxidative conditions, the cysteines of EhICP1 react to form intra and intermolecular disulfide bonds that render an inactive inhibitor. EhICP2 is not subject to redox regulation, as this inhibitor does not contain a single cysteine residue. This proposed redox regulation may be related to the differential cellular localization between EhICP1 and EhICP2.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Determining methyl sidechain conformations in a CS-ROSETTA model using methyl 1H-13C residual dipolar couplings

Modelling of protein structures based on backbone chemical shifts, using programs such as CS-ROSETTA, is becoming increasingly popular, especially for systems where few restraints are available or where homologous structures are already known. While the reliability of CS-ROSETTA calculations can be improved by incorporation of some additional backbone NMR data such as those afforded by residual dipolar couplings or minimal NOE data sets involving backbone amide protons, the sidechain conformations are largely modelled by statistical energy terms. Here, we present a simple method based on methyl residual dipolar couplings that can be used to determine the rotameric state of the threefold symmetry axis of methyl groups that occupy a single rotamer, determine rotameric distributions, and identify regions of high flexibility. The method is demonstrated for methyl side chains of a deletion variant of the human chaperone DNAJB6b.

Paramagpy: software for fitting magnetic susceptibility tensors using paramagnetic effects measured in NMR spectra

Paramagnetic metal ions with fast-relaxing electrons generate pseudocontact shifts (PCSs), residual dipolar couplings (RDCs), paramagnetic relaxation enhancements (PREs) and cross-correlated relaxation (CCR) in the nuclear magnetic resonance (NMR) spectra of the molecules they bind to. These effects offer long-range structural information in molecules equipped with binding sites for such metal ions. Here we present the new open-source software Paramagpy, which has been written in Python 3 with a graphic user interface. Paramagpy combines the functionalities of different currently available programs to support the fitting of magnetic susceptibility tensors using PCS, RDC, PRE and CCR data and molecular coordinates in Protein Data Bank (PDB) format, including a convenient graphical user interface. Paramagpy uses efficient fitting algorithms to avoid local minima and supports corrections to back-calculated PCS and PRE data arising from cross-correlation effects with chemical shift tensors. The source code is available from 10.5281/zenodo.3594568 .

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵ N-¹ H residual dipolar coupling data, typical of that obtained for ¹⁵ N,¹³ C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Unraveling the structure and dynamics of the human DNAJB6b chaperone by NMR reveals insights into Hsp40-mediated proteostasis

J-domain chaperones are involved in the efficient handover of misfolded/partially folded proteins to Hsp70 but also function independently to protect against cell death. Due to their high flexibility, the mechanism by which they regulate the Hsp70 cycle and how specific substrate recognition is performed remains unknown. Here we focus on DNAJB6b, which has been implicated in various human diseases and represents a key player in protection against neurodegeneration and protein aggregation. Using a variant that exists mainly in a monomeric form, we report the solution structure of an Hsp40 containing not only the J and C-terminal substrate binding (CTD) domains but also the functionally important linkers. The structure reveals a highly dynamic protein in which part of the linker region masks the Hsp70 binding site. Transient interdomain interactions via regions crucial for Hsp70 binding create a closed, autoinhibited state and help retain the monomeric form of the protein. Detailed NMR analysis shows that the CTD (but not the J domain) self-associates to form an oligomer comprising ∼35 monomeric units, revealing an intricate balance between intramolecular and intermolecular interactions. The results shed light on the mechanism of autoregulation of the Hsp70 cycle via conserved parts of the linker region and reveal the mechanism of DNAJB6b oligomerization and potentially antiaggregation.

Emerging solution NMR methods to illuminate the structural and dynamic properties of proteins

The first recognition of protein breathing was more than 50 years ago. Today, we are able to detect the multitude of interaction modes, structural polymorphisms, and binding-induced changes in protein structure that direct function. Solution-state NMR spectroscopy has proved to be a powerful technique, not only to obtain high-resolution structures of proteins, but also to provide unique insights into the functional dynamics of proteins. Here, we summarize recent technical landmarks in solution NMR that have enabled characterization of key biological macromolecular systems. These methods have been fundamental to atomic resolution structure determination and quantitative analysis of dynamics over a wide range of time scales by NMR. The ability of NMR to detect lowly populated protein conformations and transiently formed complexes plays a critical role in its ability to elucidate functionally important structural features of proteins and their dynamics.

Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.

Three-Dimensional Protein Structure Determination Using Pseudocontact Shifts of Backbone Amide Protons Generated by Double-Histidine Co2+-Binding Motifs at Multiple Sites

Pseudocontact shifts (PCSs) generated by paramagnetic metal ions contribute highly informative long-range structure restraints that can be measured in solution and are ideally suited to guide structure prediction algorithms in determining global protein folds. We recently demonstrated that PCSs, which are relatively small but of high quality, can be generated by a double-histidine (dHis) motif in an α-helix, which provides a well-defined binding site for a single Co²⁺ ion. Here we show that PCSs of backbone amide protons generated by dHis-Co²⁺ motifs positioned in four different α-helices of a protein deliver excellent restraints to determine the three-dimensional (3D) structure of a protein in a way akin to the global positioning system (GPS). We demonstrate the approach with GPS-Rosetta calculations of the 3D structure of the C-terminal domain of the chaperone ERp29 (ERp29-C). Despite the relatively small size of the PCSs generated by the dHis-Co²⁺ motifs, the structure calculations converged readily. Generating PCSs by the dHis-Co²⁺ motif thus presents an excellent alternative to the use of lanthanide tags.

Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13

Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.

Tau local structure shields an amyloid-forming motif and controls aggregation propensity

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within tau's repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. We use cross-linking mass spectrometry, recombinant protein and synthetic peptide systems, in silico modeling, and cell models to conclude that the aggregation-prone 306VQIVYK311 motif forms metastable compact structures with its upstream sequence that modulates aggregation propensity. We report that disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.

NMR structure determination of Ixolaris and factor X(a) interaction reveals a noncanonical mechanism of Kunitz inhibition

Ixolaris is a potent tick salivary anticoagulant that binds coagulation factor Xa (FXa) and zymogen FX, with formation of a quaternary tissue factor (TF)/FVIIa/ FX(a)/Ixolaris inhibitory complex. Ixolaris blocks TF-induced coagulation and PAR2 signaling and prevents thrombosis, tumor growth, and immune activation. We present a high-resolution structure and dynamics of Ixolaris and describe the structural basis for recognition of FX. Ixolaris consists of 2 Kunitz domains (K1 and K2) in which K2 is strikingly dynamic and encompasses several residues involved in FX binding. This indicates that the backbone plasticity of K2 is critical for Ixolaris biological activity. Notably, a nuclear magnetic resonance-derived model reveals a mechanism for an electrostatically guided, high-affinity interaction between Ixolaris and FX heparin-binding (pro)exosite, resulting in an allosteric switch in the catalytic site. This is the first report revealing the structure-function relationship of an anticoagulant targeting a zymogen serving as a scaffold for TF inhibition.

A novel mechanism for ATP to enhance the functional oligomerization of TDP-43 by specific binding

Pathological TDP-43 aggregation has been found in ∼98% ALS and other neurodegenerative diseases including Alzheimer's. TDP-43 N-terminal domain (NTD) was recently shown to form a tubular super-helical filament by oligomerization in vivo, which functions to prevent its pathological aggregation. ATP, the universal energy currency with very high concentrations in all living cells, was recently decoded to act as a biological hydrotrope to maintain protein homeostasis. Here by NMR spectroscopy, we reveal for the first time that at physiological concentrations ATP binds the TDP-43 NTD to enhance its oligomerization. Most strikingly, this binding is specifically coupled with oligomerization because three mutants with the capacity of oligomerization eliminated lose the ability to bind ATP. Our study thus provides a novel mechanism for ATP to prevent pathological aggregation by specific binding; and further implies that ATP might have many previously-unknown functions in cells by binding to proteins other than the classic ATP-dependent proteins/enzymes.

Toward Structure Determination of Disulfide-Rich Peptides Using Chemical Shift-Based Methods

Disulfide-rich peptides are a class of molecules for which NMR spectroscopy has been the primary tool for structural characterization. Here, we explore whether the process can be achieved by using structural information encoded in chemical shifts. We examine (i) a representative set of five cyclic disulfide-rich peptides that have high-resolution NMR and X-ray structures and (ii) a larger set of 100 disulfide-rich peptides from the PDB. Accuracy of the calculated structures was dependent on the methods used for searching through conformational space and for identifying native conformations. Although Hα chemical shifts could be predicted reasonably well using SHIFTX, agreement between predicted and experimental chemical shifts was sufficient for identifying native conformations for only some peptides in the representative set. Combining chemical shift data with the secondary structure information and potential energy calculations improved the ability to identify native conformations. Additional use of sparse distance restraints or homology information to restrict the search space also improved the resolution of the calculated structures. This study demonstrates that abbreviated methods have potential for elucidation of peptide structures to high resolution and further optimization of these methods, e.g., improvement in chemical shift prediction accuracy, will likely help transition these methods into the mainstream of disulfide-rich peptide structural biology.

Fast NMR method to probe solvent accessibility and disordered regions in proteins

Understanding protein structure and dynamics, which govern key cellular processes, is crucial for basic and applied research. Intrinsically disordered protein (IDP) regions display multifunctionality via alternative transient conformations, being key players in disease mechanisms. IDP regions are abundant, namely in small viruses, allowing a large number of functions out of a small proteome. The relation between protein function and structure is thus now seen from a different perspective: as IDP regions enable transient structural arrangements, each conformer can play different roles within the cell. However, as IDP regions are hard and time-consuming to study via classical techniques (optimized for globular proteins with unique conformations), new methods are required. Here, employing the dengue virus (DENV) capsid (C) protein and the immunoglobulin-binding domain of streptococcal protein G, we describe a straightforward NMR method to differentiate the solvent accessibility of single amino acid N-H groups in structured and IDP regions. We also gain insights into DENV C flexible fold region biological activity. The method, based on minimal pH changes, uses the well-established ¹H-¹⁵N HSQC pulse sequence and is easily implementable in current protein NMR routines. The data generated are simple to interpret, with this rapid approach being an useful first-choice IDPs characterization method.

Combining Evolutionary Covariance and NMR Data for Protein Structure Determination

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Do Osmolytes Impact the Structure and Dynamics of Myoglobin?

Osmolytes are small organic compounds that can affect the stability of proteins in living cells. The mechanism of osmolytes' protective effects on protein structure and dynamics has not been fully explained, but in general, two possibilities have been suggested and examined: a direct interaction of osmolytes with proteins (water replacement hypothesis), and an indirect interaction (vitrification hypothesis). Here, to investigate these two possible mechanisms, we studied myoglobin-osmolyte systems using FTIR, UV-vis, CD, and femtosecond IR pump-probe spectroscopy. Interestingly, noticeable changes are observed in both the lifetime of the CO stretch of CO-bound myoglobin and the spectra of UV-vis, CD, and FTIR upon addition of the osmolytes. In addition, the temperature-dependent CD studies reveal that the protein's thermal stability depends on molecular structure, hydrogen-bonding ability, and size of osmolytes. We anticipate that the present experimental results provide important clues about the complicated and intricate mechanism of osmolyte effects on protein structure and dynamics in a crowded cellular environment.

CS-ROSETTA

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

Synthesis, antimicrobial activity and conformational analysis of the class IIa bacteriocin pediocin PA-1 and analogs thereof

The antimicrobial peptide pediocin PA-1 is a class IIa bacteriocin that inhibits several clinically relevant pathogens including Listeria spp. Here we report the synthesis and characterization of whole pediocin PA-1 and novel analogs thereof using a combination of solid- and solution-phase strategies to overcome difficulties due to instability and undesired reactions. Pediocin PA-1 thus synthesized was a potent inhibitor of Listeria monocytogenes (MIC = 6.8 nM), similar to the bacteriocin produced naturally by Pediococcus acidilactici. Of particular interest is that linear analogs lacking both of the disulfide bridges characterizing pediocin PA-1 were as potent. One linear analog was also a strong inhibitor of Clostridium perfringens, another important food-borne pathogen. These results are discussed in light of conformational information derived from circular dichroism, solution NMR spectroscopy and structure-activity relationship studies.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra

Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6-10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.

New Structural Insights into Formation of the Key Actin Regulating WIP-WASp Complex Determined by NMR and Molecular Imaging

Wiskott-Aldrich syndrome protein (WASp) is exclusively expressed in hematopoietic cells and responsible for actin-dependent processes, including cellular activation, migration, and invasiveness. The C-terminal domain of WASp-Interacting Protein (WIP) binds to WASp and regulates its activity by shielding it from degradation in a phosphorylation dependent manner as we previously demonstrated. Mutations in the WAS-encoding gene lead to the primary immunodeficiencies Wiskott-Aldrich syndrome (WAS) and X-linked thrombocytopenia (XLT). Here, we shed a first structural light upon this function of WIP using nuclear magnetic resonance (NMR) and in vivo molecular imaging. Coexpression of fragments WASp(20-158) and WIP(442-492) allowed the purification and structural characterization of a natively folded complex, determined to form a characteristic pleckstrin homology domain with a mixed α/β-fold and central two-winged β-sheet. The WIP-derived peptide, unstructured in its free form, wraps around and interacts with WASp through short structural elements. Förster resonance energy transfer (FRET) and biochemical experiments demonstrated that, of these elements, WIP residues 454-456 are the major contributor to WASp affinity, and the previously overlooked residues 449-451 were found to have the largest effect upon WASp ubiquitylation and, presumably, degradation. Results obtained from this complementary combination of technologies link WIP-WASp affinity to protection from degradation. Our findings about the nature of WIP·WASp complex formation are relevant for ongoing efforts to understand hematopoietic cell behavior, paving the way for new therapeutic approaches to WAS and XLT.

PvaxDB: a comprehensive structural repository of Plasmodium vivax proteome

The severity of malaria caused by Plasmodium vivax worldwide and its resistance against the available general antimalarial drugs has created an urgent need for a comprehensive insight into its biology and biochemistry for developing some novel potential vaccines and therapeutics. P.vivax comprises 5392 proteins mostly predicted, out of which 4211 are soluble proteins and 2205 of these belong to blood and liver stages of malarial cycle. Presently available public resources report functional annotation (gene ontology) of only 28% (627 proteins) of the enzymatic soluble proteins and experimental structures are determined for only 42 proteins P. vivax proteome. In this milieu of severe paucity of structural and functional data, we have generated structures of 2205 soluble proteins, validated them thoroughly, identified their binding pockets (including active sites) and annotated their function increasing the coverage from the existing 28% to 100%. We have pooled all this information together and created a database christened as PvaxDB, which furnishes extensive sequence, structure, ligand binding site and functional information. We believe PvaxDB could be helpful in identifying novel protein drug targets, expediting development of new drugs to combat malaria. This is also the first attempt to create a reliable comprehensive computational structural repository of all the soluble proteins of P. vivax.

Database URL: http://www.scfbio-iitd.res.in/PvaxDB

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Advances in the Understanding of Protein-Protein Interactions in Drug Metabolizing Enzymes through the Use of Biophysical Techniques

In recent years, a growing appreciation has developed for the importance of protein-protein interactions to modulate the function of drug metabolizing enzymes. Accompanied with this appreciation, new methods and technologies have been designed for analyzing protein-protein interactions both in vitro and in vivo. These technologies have been applied to several classes of drug metabolizing enzymes, including: cytochrome P450's (CYPs), monoamine oxidases (MAOs), UDP-glucuronosyltransferases (UGTs), glutathione S-transferases (GSTs), and sulfotransferases (SULTs). In this review, we offer a brief description and assessment of the impact of many of these technologies to the study of protein-protein interactions in drug disposition. The still expanding list of these techniques and assays has the potential to revolutionize our understanding of how these enzymes carry out their important functions in vivo.

Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation

Bactofilins constitute a recently discovered class of bacterial proteins that form cytoskeletal filaments. They share a highly conserved domain (DUF583) of which the structure remains unknown, in part due to the large size and noncrystalline nature of the filaments. Here, we describe the atomic structure of a bactofilin domain from Caulobacter crescentus. To determine the structure, we developed an approach that combines a biophysical model for proteins with recently obtained solid-state NMR spectroscopy data and amino acid contacts predicted from a detailed analysis of the evolutionary history of bactofilins. Our structure reveals a triangular β-helical (solenoid) conformation with conserved residues forming the tightly packed core and polar residues lining the surface. The repetitive structure explains the presence of internal repeats as well as strongly conserved positions, and is reminiscent of other fibrillar proteins. Our work provides a structural basis for future studies of bactofilin biology and for designing molecules that target them, as well as a starting point for determining the organization of the entire bactofilin filament. Finally, our approach presents new avenues for determining structures that are difficult to obtain by traditional means.

Recent developments in solution nuclear magnetic resonance (NMR)-based molecular biology

Visualizing post-translational modifications, conformations, and interaction surfaces of protein structures at atomic resolution underpins the development of novel therapeutics to combat disease. As computational resources expand, in silico calculations coupled with experimentally derived structures and functional assays have led to an explosion in structure-based drug design (SBDD) with several compounds in clinical trials. It is increasingly clear that "hidden" transition-state structures along activation trajectories can be harnessed to develop novel classes of allosteric inhibitors. The goal of this mini-review is to empower the clinical researcher with a general knowledge of the strengths and weaknesses of nuclear magnetic resonance (NMR) spectroscopy in molecular medicine. Although NMR can determine protein structures at atomic resolution, its unrivaled strength lies in sensing subtle changes in a nuclei's chemical environment as a result of intrinsic conformational dynamics, solution conditions, and binding interactions. These can be recorded at atomic resolution, without explicit structure determination, and then incorporated with static structures or molecular dynamics simulations to produce a complete biological picture.

Molecular modeling of biomolecules by paramagnetic NMR and computational hybrid methods

The 3D atomic structures of biomolecules and their complexes are key to our understanding of biomolecular function, recognition, and mechanism. However, it is often difficult to obtain structures, particularly for systems that are complex, dynamic, disordered, or exist in environments like cell membranes. In such cases sparse data from a variety of paramagnetic NMR experiments offers one possible source of structural information. These restraints can be incorporated in computer modeling algorithms that can accurately translate the sparse experimental data into full 3D atomic structures. In this review, we discuss various types of paramagnetic NMR/computational hybrid modeling techniques that can be applied to successful modeling of not only the atomic structure of proteins but also their interacting partners. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.