Homology modeling of larger proteins guided by chemical shifts

Solid-state NMR structure determination of a membrane protein in E. coli cellular inner membrane

Structure determination of membrane proteins in native cellular membranes is critical to precisely reveal their structures in physiological conditions. However, it remains challenging for solid-state nuclear magnetic resonance (ssNMR) due to the low sensitivity and high complexity of ssNMR spectra of cellular membranes. Here, we present the structure determination of aquaporin Z (AqpZ) by ssNMR in Escherichia coli inner membranes. To enhance the signal sensitivity of AqpZ, we optimized protein overexpression and removed outer membrane components. To suppress the interference of background proteins, we used a "dual-media" expression approach and antibiotic treatment. Using 1017 distance restraints obtained from two-dimensional 13C-13C spectra based on the complete chemical shift assignments, the 1.7-Å ssNMR structure of AqpZ is determined in E. coli inner membranes. This cellular ssNMR structure determination paves the way for analyzing the atomic structural details for membrane proteins in native cellular membranes.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

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.

Structural Insights into the Interaction between Bacillus subtilis SepF Assembly and FtsZ by Solid-State NMR Spectroscopy

In many species of Gram-positive bacteria, SepF participated in the membrane tethering of FtsZ Z-ring during bacteria division. However, atomic-level details of interaction between SepF and FtsZ in an assembled state are lacking. Here, by combining solid-state NMR (SSNMR) with biochemical analyses, the interaction of Bacillus subtilis SepF and the C-terminal domain (CTD) of FtsZ was investigated. We obtained near complete chemical shift assignments of SepF and determined the structural model of the SepF monomer. Interaction with FtsZ-CTD caused further packing of SepF rings, and SSNMR experiments revealed the affected residues locating at α1, α2, β3, and β4 of SepF. Solution NMR experiments of dimeric SepF constructed by point mutation strategy proved a prerequisite role of α-α interface formation in SepF for FtsZ binding. Overall, our results provide structural insights into the mechanisms of SepF-FtsZ interaction for better understanding the function of SepF in bacteria.

E2UbcH5B-derived peptide ligands target HECT E3-E2 binding site and block the Ub-dependent SARS-CoV-2 egression: A computational study

Homologous to E6AP carboxyl-terminus (HECT)-type E3 ligase performs ubiquitin (Ub)-proteasomal protein degradation via forming a complex with E2∼Ub. Enveloped viruses including SARS-CoV-2 escape from the infected cells by harnessing the E-class vacuolar protein-sorting (ESCRT) machinery and mimic the cellular system through PPAY motif-based linking to HECT Ub ligase activity. In the present study, we have characterized the binding pattern of E2^UbcH5B to HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2 through in silico analysis to isolate the E2^UbcH5B-specific peptide inhibitors that may target SARS-CoV-2 viral egression. Molecular dynamics analysis revealed more opening of E2^UbcH5B-binding pocket upon binding to HECT^NEDD4L, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2. We observed similar binding pattern for E2^UbcH5B and mentioned HECT domains as previously reported for HECT^NEDD4L where Trp762, Trp709, and Trp657 residues of HECT^NEDD4L, HECT^WWP1, and HECT^WWP2 are involved in making contacts with Ser94 residue of E2^UbcH5B. Similarly, corresponding to HECT^NEDD4L Tyr756 residue, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2-specific Phe703, Phe651, Phe1387, and Phe1353 residues execute interaction with E2^UbcH5B. Our analysis suggests that corresponding to Cys942 of HECT^NEDD4L, Cys890, Cys838, Cys1574, and Cys1540 residues of HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2, respectively are involved in E2-to-E3 Ub transfer. Furthermore, MM-PBSA free energy calculations revealed favorable energy values for E2^UbcH5B-HECT complexes along with the individual residue contributions. Subsequently, two E2^UbcH5B-derived peptides (His55-Phe69 and Asn81-Ala96) were tested for their binding abilities against HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2. Their binding was validated through substitution of Phe62, Pro65, Ile84, and Cys85 residues into Ala, which revealed an impaired binding, suggesting that the proposed peptide ligands may selectively target E2-HECT binding and Ub-transfer. Collectively, we propose that peptide-driven blocking of E2-to-HECT Ub loading may limit SARS-CoV-2 egression and spread in the host cells.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Regulation of CYLD activity and specificity by phosphorylation and ubiquitin-binding CAP-Gly domains

Non-degradative ubiquitin chains and phosphorylation events govern signaling responses by innate immune receptors. The deubiquitinase CYLD in complex with SPATA2 is recruited to receptor signaling complexes by the ubiquitin ligase LUBAC and regulates Met1- and Lys63-linked polyubiquitin and receptor signaling outcomes. Here, we investigate the molecular determinants of CYLD activity. We reveal that two CAP-Gly domains in CYLD are ubiquitin-binding domains and demonstrate a requirement of CAP-Gly3 for CYLD activity and regulation of immune receptor signaling. Moreover, we identify a phosphorylation switch outside of the catalytic USP domain, which activates CYLD toward Lys63-linked polyubiquitin. The phosphorylated residue Ser568 is a novel tumor necrosis factor (TNF)-regulated phosphorylation site in CYLD and works in concert with Ser418 to enable CYLD-mediated deubiquitination and immune receptor signaling. We propose that phosphorylated CYLD, together with SPATA2 and LUBAC, functions as a ubiquitin-editing complex that balances Lys63- and Met1-linked polyubiquitin at receptor signaling complexes to promote LUBAC signaling.

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.

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.

An N-terminal conserved region in human Atg3 couples membrane curvature sensitivity to conjugase activity during autophagy

During autophagy the enzyme Atg3 catalyzes the covalent conjugation of LC3 to the amino group of phosphatidylethanolamine (PE) lipids, which is one of the key steps in autophagosome formation. Here, we have demonstrated that an N-terminal conserved region of human Atg3 (hAtg3) communicates information from the N-terminal membrane curvature-sensitive amphipathic helix (AH), which presumably targets the enzyme to the tip of phagophore, to the C-terminally located catalytic core for LC3-PE conjugation. Mutations in the putative communication region greatly reduce or abolish the ability of hAtg3 to catalyze this conjugation in vitro and in vivo, and alter the membrane-bound conformation of the wild-type protein, as reported by NMR. Collectively, our results demonstrate that the N-terminal conserved region of hAtg3 works in concert with its geometry-selective AH to promote LC3-PE conjugation only on the target membrane, and substantiate the concept that highly curved membranes drive spatial regulation of the autophagosome biogenesis during autophagy.

Oxidative Damage? Not a Problem! The Characterization of Humanin-like Mitochondrial Peptide in Anoxia Tolerant Freshwater Turtles

Mitochondria was long thought to be an "end function" organelle that regulated the metabolic flux and apoptosis in the cell. However, with the discovery of the mitochondrial peptide (MDP) humanin (HN/MTRNR2), the cytoprotective and pro-survival applications of MDPs have taken the forefront of therapeutic and diagnostic research. However, the regulation of humanin-like MDPs in natural model systems that can tolerate lethal environmental and cytotoxic insults remains to be investigated. Red-eared sliders are champion anaerobes that can withstand three continuous months of anoxia followed by rapid bouts of oxygen reperfusion without incurring cellular damage. Freshwater turtles employ extensive physiological and biochemical strategies to combat anoxia, with metabolic rate depression and a global enhancement of antioxidant and cytoprotective pathways being the two most important contributors. The main aim of this study was to uncover and characterize the humanin-homologue in freshwater turtles as well as investigate the differential regulation of humanin in response to short and long-term oxygen deprivation. In this study we have used de novo and homology-based protein modelling to elucidate the putative structure of humanin in red-eared sliders as well as an ELISA and western immunoblotting to confirm the protein abundance in the turtle brain and six peripheral tissues during control, 5 h, and 20 h anoxia (n = 4/group). We found that a humanin-homologue (TSE-humanin) is present in red-eared sliders and it may play a cytoprotective role against oxidative damage.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

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.

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.

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.

Applications of In-Cell NMR in Structural Biology and Drug Discovery

In-cell nuclear magnetic resonance (NMR) is a method to provide the structural information of a target at an atomic level under physiological conditions and a full view of the conformational changes of a protein caused by ligand binding, post-translational modifications or protein⁻protein interactions in living cells. Previous in-cell NMR studies have focused on proteins that were overexpressed in bacterial cells and isotopically labeled proteins injected into oocytes of Xenopus laevis or delivered into human cells. Applications of in-cell NMR in probing protein modifications, conformational changes and ligand bindings have been carried out in mammalian cells by monitoring isotopically labeled proteins overexpressed in living cells. The available protocols and successful examples encourage wide applications of this technique in different fields such as drug discovery. Despite the challenges in this method, progress has been made in recent years. In this review, applications of in-cell NMR are summarized. The successful applications of this method in mammalian and bacterial cells make it feasible to play important roles in drug discovery, especially in the step of target engagement.

Force-dependent allostery of the α-catenin actin-binding domain controls adherens junction dynamics and functions

α-catenin is a key mechanosensor that forms force-dependent interactions with F-actin, thereby coupling the cadherin-catenin complex to the actin cytoskeleton at adherens junctions (AJs). However, the molecular mechanisms by which α-catenin engages F-actin under tension remained elusive. Here we show that the α1-helix of the α-catenin actin-binding domain (αcat-ABD) is a mechanosensing motif that regulates tension-dependent F-actin binding and bundling. αcat-ABD containing an α1-helix-unfolding mutation (H1) shows enhanced binding to F-actin in vitro. Although full-length α-catenin-H1 can generate epithelial monolayers that resist mechanical disruption, it fails to support normal AJ regulation in vivo. Structural and simulation analyses suggest that α1-helix allosterically controls the actin-binding residue V796 dynamics. Crystal structures of αcat-ABD-H1 homodimer suggest that α-catenin can facilitate actin bundling while it remains bound to E-cadherin. We propose that force-dependent allosteric regulation of αcat-ABD promotes dynamic interactions with F-actin involved in actin bundling, cadherin clustering, and AJ remodeling during tissue morphogenesis.

De novo protein structure prediction using ultra-fast molecular dynamics simulation

Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. The results show that the ultra-fast molecular dynamics simulation could dramatically reduce the gap between the sequence and its structure at atom level, and it could also present high efficiency in protein structure determination if sparse experimental data is available.

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 Fragmentation QM/MM Calculation of NMR Chemical Shifts for Protein-Ligand Complexes

In this study, the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method was applied for NMR chemical shift calculations of protein-ligand complexes. In the AF-QM/MM approach, the protein binding pocket is automatically divided into capped fragments (within ~200 atoms) for density functional theory (DFT) calculations of NMR chemical shifts. Meanwhile, the solvent effect was also included using the Poission-Boltzmann (PB) model, which properly accounts for the electrostatic polarization effect from the solvent for protein-ligand complexes. The NMR chemical shifts of neocarzinostatin (NCS)-chromophore binding complex calculated by AF-QM/MM accurately reproduce the large-sized system results. The 1H chemical shift perturbations (CSP) between apo-NCS and holo-NCS predicted by AF-QM/MM are also in excellent agreement with experimental results. Furthermore, the DFT calculated chemical shifts of the chromophore and residues in the NCS binding pocket can be utilized as molecular probes to identify the correct ligand binding conformation. By combining the CSP of the atoms in the binding pocket with the Glide scoring function, the new scoring function can accurately distinguish the native ligand pose from decoy structures. Therefore, the AF-QM/MM approach provides an accurate and efficient platform for protein-ligand binding structure prediction based on NMR derived information.

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.

Rapid and reliable protein structure determination via chemical shift threading

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often < 10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)-with an average TM-score performance of 0.68 (vs. 0.50-0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca .

The PROSECCO server for chemical shift predictions in ordered and disordered proteins

The chemical shifts measured in solution-state and solid-state nuclear magnetic resonance (NMR) are powerful probes of the structure and dynamics of protein molecules. The exploitation of chemical shifts requires methods to correlate these data with the protein structures and sequences. We present here an approach to calculate accurate chemical shifts in both ordered and disordered proteins using exclusively the information contained in their sequences. Our sequence-based approach, protein sequences and chemical shift correlations (PROSECCO), achieves the accuracy of the most advanced structure-based methods in the characterization of chemical shifts of folded proteins and improves the state of the art in the study of disordered proteins. Our analyses revealed fundamental insights on the structural information carried by NMR chemical shifts of structured and unstructured protein states.

Unraveling the meaning of chemical shifts in protein NMR

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Determining Complex Structures using Docking Method with Single Particle Scattering Data

Protein complexes are critical for many molecular functions. Due to intrinsic flexibility and dynamics of complexes, their structures are more difficult to determine using conventional experimental methods, in contrast to individual subunits. One of the major challenges is the crystallization of protein complexes. Using X-ray free electron lasers (XFELs), it is possible to collect scattering signals from non-crystalline protein complexes, but data interpretation is more difficult because of unknown orientations. Here, we propose a hybrid approach to determine protein complex structures by combining XFEL single particle scattering data with computational docking methods. Using simulations data, we demonstrate that a small set of single particle scattering data collected at random orientations can be used to distinguish the native complex structure from the decoys generated using docking algorithms. The results also indicate that a small set of single particle scattering data is superior to spherically averaged intensity profile in distinguishing complex structures. Given the fact that XFEL experimental data are difficult to acquire and at low abundance, this hybrid approach should find wide applications in data interpretations.

Protein dynamics and function from solution state NMR spectroscopy

It is well-established that dynamics are central to protein function; their importance is implicitly acknowledged in the principles of the Monod, Wyman and Changeux model of binding cooperativity, which was originally proposed in 1965. Nowadays the concept of protein dynamics is formulated in terms of the energy landscape theory, which can be used to understand protein folding and conformational changes in proteins. Because protein dynamics are so important, a key to understanding protein function at the molecular level is to design experiments that allow their quantitative analysis. Nuclear magnetic resonance (NMR) spectroscopy is uniquely suited for this purpose because major advances in theory, hardware, and experimental methods have made it possible to characterize protein dynamics at an unprecedented level of detail. Unique features of NMR include the ability to quantify dynamics (i) under equilibrium conditions without external perturbations, (ii) using many probes simultaneously, and (iii) over large time intervals. Here we review NMR techniques for quantifying protein dynamics on fast (ps-ns), slow (μs-ms), and very slow (s-min) time scales. These techniques are discussed with reference to some major discoveries in protein science that have been made possible by NMR spectroscopy.

Prediction of Protein Structure Using Surface Accessibility Data

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance-to-surface information encoded in the sPRE data in the chemical shift-based CS-Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach.

The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data

We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27-98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models.

Advances in NMR Methods To Map Allosteric Sites: From Models to Translation

The last five years have witnessed major developments in the understanding of the allosteric phenomenon, broadly defined as coupling between remote molecular sites. Such advances have been driven not only by new theoretical models and pharmacological applications of allostery, but also by progress in the experimental approaches designed to map allosteric sites and transitions. Among these techniques, NMR spectroscopy has played a major role given its unique near-atomic resolution and sensitivity to the dynamics that underlie allosteric couplings. Here, we highlight recent progress in the NMR methods tailored to investigate allostery with the goal of offering an overview of which NMR approaches are best suited for which allosterically relevant questions. The picture of the allosteric "NMR toolbox" is provided starting from one of the simplest models of allostery (i.e., the four-state thermodynamic cycle) and continuing to more complex multistate mechanisms. We also review how such an "NMR toolbox" has assisted the elucidation of the allosteric molecular basis for disease-related mutations and the discovery of novel leads for allosteric drugs. From this overview, it is clear that NMR plays a central role not only in experimentally validating transformative theories of allostery, but also in tapping the full translational potential of allosteric systems.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.