NMR-based automated protein structure determination

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.

NMRtist: an online platform for automated biomolecular NMR spectra analysis

SUMMARY

We present NMRtist, an online platform that combines deep learning, large-scale optimization and cloud computing to automate protein NMR spectra analysis. Our website provides virtual storage for NMR spectra deposition together with a set of applications designed for automated peak picking, chemical shift assignment and protein structure determination. The system can be used by non-experts and allows protein assignments and structures to be determined within hours after the measurements, strictly without any human intervention.

AVAILABILITY AND IMPLEMENTATION

NMRtist is freely available to non-commercial users at https://nmrtist.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biological Magnetic Resonance Data Bank

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview

BACKGROUND

NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems.

OBJECTIVE

This review provides a toolkit to investigate peptide conformational properties by NMR.

METHODS

Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well.

RESULTS

The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well.

CONCLUSION

NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

NMR refinement and peptide folding using the GROMACS software

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins and nucleic acids. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package it is possible to model distance restraints, dihedral restraints and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

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.

Automated assignment of methyl NMR spectra from large proteins

As structural biology trends towards larger and more complex biomolecular targets, a detailed understanding of their interactions and underlying structures and dynamics is required. The development of methyl-TROSY has enabled NMR spectroscopy to provide atomic-resolution insight into the mechanisms of large molecular assemblies in solution. However, the applicability of methyl-TROSY has been hindered by the laborious and time-consuming resonance assignment process, typically performed with domain fragmentation, site-directed mutagenesis, and analysis of NOE data in the context of a crystal structure. In response, several structure-based automatic methyl assignment strategies have been developed over the past decade. Here, we present a comprehensive analysis of all available methods and compare their input data requirements, algorithmic strategies, and reported performance. In general, the methods fall into two categories: those that primarily rely on inter-methyl NOEs, and those that utilize methyl PRE- and PCS-based restraints. We discuss their advantages and limitations, and highlight the potential benefits from standardizing and combining different methods.

Characterisation of a Novel A-Superfamily Conotoxin

Conopeptides belonging to the A-superfamily from the venomous molluscs, Conus, are typically α-conotoxins. The α-conotoxins are of interest as therapeutic leads and pharmacological tools due to their selectivity and potency at nicotinic acetylcholine receptor (nAChR) subtypes. Structurally, the α-conotoxins have a consensus fold containing two conserved disulfide bonds that define the two-loop framework and brace a helical region. Here we report on a novel α-conotoxin Pl168, identified from the transcriptome of Conus planorbis, which has an unusual 4/8 loop framework. Unexpectedly, NMR determination of its three-dimensional structure reveals a new structural type of A-superfamily conotoxins with a different disulfide-stabilized fold, despite containing the conserved cysteine framework and disulfide connectivity of classical α-conotoxins. The peptide did not demonstrate activity on a range of nAChRs, or Ca²⁺ and Na⁺ channels suggesting that it might represent a new pharmacological class of conotoxins.

A solid-state NMR tool box for the investigation of ATP-fueled protein engines

Motor proteins are involved in a variety of cellular processes. Their main purpose is to convert the chemical energy released during adenosine triphosphate (ATP) hydrolysis into mechanical work. In this review, solid-state Nuclear Magnetic Resonance (NMR) approaches are discussed allowing studies of structures, conformational events and dynamic features of motor proteins during a variety of enzymatic reactions. Solid-state NMR benefits from straightforward sample preparation based on sedimentation of the proteins directly into the Magic-Angle Spinning (MAS) rotor. Protein resonance assignment is the crucial and often time-limiting step in interpreting the wealth of information encoded in the NMR spectra. Herein, potentials, challenges and limitations in resonance assignment for large motor proteins are presented, focussing on both biochemical and spectroscopic approaches. This work highlights NMR tools available to study the action of the motor domain and its coupling to functional processes, as well as to identify protein-nucleotide interactions during events such as DNA replication. Arrested protein states of reaction coordinates such as ATP hydrolysis can be trapped for NMR studies by using stable, non-hydrolysable ATP analogues that mimic the physiological relevant states as accurately as possible. Recent advances in solid-state NMR techniques ranging from Dynamic Nuclear Polarization (DNP), ³¹P-based heteronuclear correlation experiments, ¹H-detected spectra at fast MAS frequencies >100 kHz to paramagnetic NMR are summarized and their applications to the bacterial DnaB helicase from Helicobacter pylori are discussed.

Searching for Small Molecules with an Atomic Sort

The discovery of biologically active small molecules requires sifting through large amounts of data to identify unique or unusual arrangements of atoms. Here, we develop, test and evaluate an atom-based sort to identify novel features of secondary metabolites and demonstrate its use to evaluate novelty in marine microbial and sponge extracts. This study outlines an important ongoing advance towards the translation of autonomous systems to identify, and ultimately elucidate, atomic novelty within a complex mixture of small molecules.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach

The optimization approaches classically used during the determination of protein structure encounter various difficulties, especially when the size of the conformational space is large. Indeed, in such a case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration. The interval branch-and-prune (iBP) approach, based on the reformulation of the distance geometry problem (DGP) provides a theoretical frame for the generation of protein conformations, by systematically sampling the conformational space. When an appropriate subset of interatomic distances is known exactly, this worst-case exponential-time algorithm is provably complete and fixed-parameter tractable. These guarantees, however, immediately disappear as distance measurement errors are introduced. Here we propose an improvement of this approach: threading-augmented interval branch-and-prune (TAiBP), where the combinatorial explosion of the original iBP approach arising from its exponential complexity is alleviated by partitioning the input instances into consecutive peptide fragments and by using self-organizing maps (SOMs) to obtain clusters of similar solutions. A validation of the TAiBP approach is presented here on a set of proteins of various sizes and structures. The calculation inputs are a uniform covalent geometry extracted from force field covalent terms, the backbone dihedral angles with error intervals, and a few long-range distances. For most of the proteins smaller than 50 residues and interval widths of 20°, the TAiBP approach yielded solutions with RMSD values smaller than 3 Å with respect to the initial protein conformation. The efficiency of the TAiBP approach for proteins larger than 50 residues will require the use of nonuniform covalent geometry and may have benefits from the recent development of residue-specific force-fields.

Methionine in a protein hydrophobic core drives tight interactions required for assembly of spider silk

Web spiders connect silk proteins, so-called spidroins, into fibers of extraordinary toughness. The spidroin N-terminal domain (NTD) plays a pivotal role in this process: it polymerizes spidroins through a complex mechanism of dimerization. Here we analyze sequences of spidroin NTDs and find an unusually high content of the amino acid methionine. We simultaneously mutate all methionines present in the hydrophobic core of a spidroin NTD from a nursery web spider’s dragline silk to leucine. The mutated NTD is strongly stabilized and folds at the theoretical speed limit. The structure of the mutant is preserved, yet its ability to dimerize is substantially impaired. We find that side chains of core methionines serve to mobilize the fold, which can thereby access various conformations and adapt the association interface for tight binding. Methionine in a hydrophobic core equips a protein with the capacity to dynamically change shape and thus to optimize its function.

Spider silk is of interest in material science research. Here the authors show that the tight binding of a spider silk protein domain relies on the amino acid methionine, which is abundant in the domain core where it facilitates dynamic shape adaption of the binding interface.

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.

Simple high-resolution NMR spectroscopy as a tool in molecular biology

NMR is one of the major techniques for investigating the structure, dynamics and interactions between biomolecules. However, non-experts often experience NMR experimentation and data analysis as intimidating. We discuss a simple yet powerful NMR technique, the so-called chemical shift perturbation (CSP) analysis, as a tool to elucidate macromolecular interactions in small- and medium-sized complexes, including protein-protein, protein-drug, and protein-DNA/RNA interactions. We discuss current software packages for NMR data analysis and present a new interactive graphical tool implemented in CcpNmr AnalysisAssign version-3, which can drastically reduce the time required for the CSP analysis. Lastly, we illustrate the usefulness of a protein three-dimensional structure for interpretation of the CSP data.

Structural Basis of TRPV4 N Terminus Interaction with Syndapin/PACSIN1-3 and PIP2

Transient receptor potential (TRP) channels are polymodally regulated ion channels. TRPV4 (vanilloid 4) is sensitized by PIP₂ and desensitized by Syndapin3/PACSIN3, which bind to the structurally uncharacterized TRPV4 N terminus. We determined the nuclear magnetic resonance structure of the Syndapin3/PACSIN3 SH3 domain in complex with the TRPV4 N-terminal proline-rich region (PRR), which binds as a class I polyproline II (PPII) helix. This PPII conformation is broken by a conserved proline in a cis conformation. Beyond the PPII, we find that the proximal TRPV4 N terminus is unstructured, a feature conserved across species thus explaining the difficulties in resolving it in previous structural studies. Syndapin/PACSIN SH3 domain binding leads to rigidification of both the PRR and the adjacent PIP₂ binding site. We determined the affinities of the TRPV4 N terminus for PACSIN1, 2, and 3 SH3 domains and PIP₂ and deduce a hierarchical interaction network where Syndapin/PACSIN binding influences the PIP₂ binding site but not vice versa.

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.

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.

The solution structure of monomeric CCL5 in complex with a doubly sulfated N-terminal segment of CCR5

The inflammatory chemokine CCL5, which binds the chemokine receptor CCR5 in a two-step mechanism so as to activate signaling pathways in hematopoetic cells, plays an important role in immune surveillance, inflammation, and development as well as in several immune system pathologies. The recently published crystal structure of CCR5 bound to a high-affinity variant of CCL5 lacks the N-terminal segment of the receptor that is post-translationally sulfated and is known to be important for high-affinity binding. Here, we report the NMR solution structure of monomeric CCL5 bound to a synthetic doubly sulfated peptide corresponding to the missing first 27 residues of CCR5. Our structures show that two sulfated tyrosine residues, sY10 and sY14, as well as the unsulfated Y15 form a network of strong interactions with a groove on a surface of CCL5 that is formed from evolutionarily conserved basic and hydrophobic amino acids. We then use our NMR structures, in combination with available crystal data, to create an atomic model of full-length wild-type CCR5:CCL5. Our findings reveal the structural determinants involved in the recognition of CCL5 by the CCR5 N terminus. These findings, together with existing structural data, provide a complete structural framework with which to understand the specificity of receptor:chemokine interactions.

DATABASE

Structural data are available in the PDB under the accession number 6FGP.

NMR in structure-based drug design

NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed.