Unveiling Inherent Degeneracies in Determining Population-Weighted Ensembles of Interdomain Orientational Distributions Using NMR Residual Dipolar Couplings: Application to RNA Helix Junction Helix Motifs

Ensemble-function relationships: From qualitative to quantitative relationships between protein structure and function

Structure-function relationships are deeply rooted in modern biochemistry and structural biology and have provided the basis for our understanding of how protein structure defines function. While structure-function relationships continue to provide invaluable qualitative information, they cannot, in principle, provide the quantitative information ultimately needed to fully understand how proteins function and to make quantitative predictions about changes in activity from changes in sequence and structure. These limitations appear to arise from fundamental principles of physics, which dictate that proteins exist as interchanging ensembles of conformations, rather than as static structures that underly conventional structure-function relationships. This perspective discusses the concept of ensemble-function relationships as quantitative relationships that build on and extend traditional structure-function relationships. The concepts of free energy landscapes and conformational ensembles and their application to proteins are reviewed. The perspective summarizes a range of approaches that can provide conformational ensemble information and focuses on X-ray crystallography methods for obtaining experimental protein conformational ensembles. Focusing on enzymes as archetypes of protein function, recent literature examples are reviewed that used ensemble-function relationships to understand how protein residues contribute to function and how changes in protein sequence and structure impact activity, leading to new models and providing previously inaccessible mechanistic insights. Potential applications of conformational ensembles and ensemble-function relationships to protein design are examined. The perspective concludes with current limitations of the ensemble-function relationships and potential paths forward toward the next generation of quantitative ensemble-function models.

Kinetic Resolution of the Atomic 3D Structures Formed by Ground and Excited Conformational States in an RNA Dynamic Ensemble

Knowing the 3D structures formed by the various conformations populating the RNA free-energy landscape, their relative abundance, and kinetic interconversion rates is required to obtain a quantitative and predictive understanding of how RNAs fold and function at the atomic level. While methods integrating ensemble-averaged experimental data with computational modeling are helping define the most abundant conformations in RNA ensembles, elucidating their kinetic rates of interconversion and determining the 3D structures of sparsely populated short-lived RNA excited conformational states (ESs) remains challenging. Here, we developed an approach integrating Rosetta-FARFAR RNA structure prediction with NMR residual dipolar couplings and relaxation dispersion that simultaneously determines the 3D structures formed by the ground-state (GS) and ES subensembles, their relative abundance, and kinetic rates of interconversion. The approach is demonstrated on HIV-1 TAR, whose six-nucleotide apical loop was previously shown to form a sparsely populated (∼13%) short-lived (lifetime ∼ 45 μs) ES. In the GS, the apical loop forms a broad distribution of open conformations interconverting on the pico-to-nanosecond time scale. Most residues are unpaired and preorganized to bind the Tat-superelongation protein complex. The apical loop zips up in the ES, forming a narrow distribution of closed conformations, which sequester critical residues required for protein recognition. Our work introduces an approach for determining the 3D ensemble models formed by sparsely populated RNA conformational states, provides a rare atomic view of an RNA ES, and kinetically resolves the atomic 3D structures of RNA conformational substates, interchanging on time scales spanning 6 orders of magnitude, from picoseconds to microseconds.

Measurement of Residual Dipolar Couplings Using Magnetically Aligned and Flipped Nanodiscs

Recent developments in lipid nanodisc technology have successfully overcome the major challenges in the structural and functional studies of membrane proteins and drug delivery. Among the different types of nanodiscs, the use of synthetic amphiphilic polymers created new directions including the applications of solution and solid-state NMR spectroscopy. The ability to magnetically align large-size (>20 nm diameter) polymer nanodiscs and flip them using paramagnetic lanthanide ions has enabled high-resolution studies on membrane proteins using solid-state NMR techniques. The use of polymer-based macro-nanodiscs (>20 nm diameter) as an alignment medium to measure residual dipolar couplings (RDCs) and residual quadrupole couplings by NMR experiments has also been demonstrated. In this study, we demonstrate the use of magnetically aligned and 90°-flipped polymer nanodiscs as alignment media for structural studies on proteins by solution NMR spectroscopy. These macro-nanodiscs, composed of negatively charged SMA-EA polymers and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids, were used to measure residual ¹H-¹⁵N dipolar couplings (RDCs) from the water-soluble ∼21 kDa uniformly ¹⁵N-labeled flavin mononucleotide binding domain (FBD) of cytochrome-P450 reductase. The experimentally measured ¹H-¹⁵N RDC values are compared with the values calculated from the crystal structures of cytochrome-P450 reductase that lacks the transmembrane domain. The N-H RDCs measured using aligned and 90°-flipped nanodiscs show a modulation by the function (3 cos²θ - 1), where θ is the angle between the N-H bond vector and the applied magnetic field direction. This successful demonstration of the use of two orthogonally oriented alignment media should enable structural studies on a variety of systems including small molecules, DNA, and RNA.

Increased usability, algorithmic improvements and incorporation of data mining for structure calculation of proteins with REDCRAFT software package

Background

Traditional approaches to elucidation of protein structures by Nuclear Magnetic Resonance spectroscopy (NMR) rely on distance restraints also known as Nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. However, to meet its full potential, several improvements to REDCRAFT must be made.

Results

In this work, we present improvements to REDCRAFT that include increased usability, better interoperability, and a more robust core algorithm. We have demonstrated the impact of the improved core algorithm in the successful folding of the protein 1A1Z with as high as ±4 Hz of added error. The REDCRAFT computed structure from the highly corrupted data exhibited less than 1.0 Å with respect to the X-ray structure. We have also demonstrated the interoperability of REDCRAFT in a few instances including with PDBMine to reduce the amount of required data in successful folding of proteins to unprecedented levels. Here we have demonstrated the successful folding of the protein 1D3Z (to within 2.4 Å of the X-ray structure) using only N-H RDCs from one alignment medium.

Conclusions

The additional GUI features of REDCRAFT combined with the NEF compliance have significantly increased the flexibility and usability of this software package. The improvements of the core algorithm have substantially improved the robustness of REDCRAFT in utilizing less experimental data both in quality and quantity.

RNA Dynamics by NMR Spectroscopy

An ever-increasing number of functional RNAs require a mechanistic understanding. RNA function relies on changes in its structure, so-called dynamics. To reveal dynamic processes and higher energy structures, new NMR methods have been developed to elucidate these dynamics in RNA with atomic resolution. In this Review, we provide an introduction to dynamics novices and an overview of methods that access most dynamic timescales, from picoseconds to hours. Examples are provided as well as insight into theory, data acquisition and analysis for these different methods. Using this broad spectrum of methodology, unprecedented detail and invisible structures have been obtained and are reviewed here. RNA, though often more complicated and therefore neglected, also provides a great system to study structural changes, as these RNA structural changes are more easily defined-Lego like-than in proteins, hence the numerous revelations of RNA excited states.

Conformations of an RNA Helix-Junction-Helix Construct Revealed by SAXS Refinement of MD Simulations

RNA is involved in a broad range of biological processes that extend far beyond translation. Many of RNA's recently discovered functions rely on folding to a specific conformation or transitioning between conformations. The RNA structure contains rigid, short basepaired regions connected by more flexible linkers. Studies of model constructs such as small helix-junction-helix (HJH) motifs are useful in understanding how these elements work together to determine RNA conformation. Here, we reveal the full ensemble of solution structures assumed by a model RNA HJH. We apply small-angle x-ray scattering and an ensemble optimization method to selectively refine models generated by all-atom molecular dynamics simulations. The expectation of a broad distribution of helix orientations, at and above physiological ionic strength, is not met. Instead, this analysis shows that the HJH structures are dominated by two distinct conformations at moderate to high ionic strength. Atomic structures, selected from the molecular dynamics simulations, reveal strong base-base interactions in the junction that critically constrain the conformational space available to the HJH molecule and lead to a surprising re-extension at high salt. These results are corroborated by comparison with previous single-molecule fluorescence resonance energy transfer experiments on the same constructs.

High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sublibraries optimized for evaluating enrichment when virtually screening a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics simulations. Ensemble-based virtual screening scores molecules with an area under the receiver operator characteristic curve of ~0.85-0.94 and with ~40-75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same molecular dynamics simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits and that the degree of enrichment is dependent on the accuracy of the ensemble.

Determination of the conformational ensemble of the TAR RNA by X-ray scattering interferometry

The conformational ensembles of structured RNA's are crucial for biological function, but they remain difficult to elucidate experimentally. We demonstrate with HIV-1 TAR RNA that X-ray scattering interferometry (XSI) can be used to determine RNA conformational ensembles. X-ray scattering interferometry (XSI) is based on site-specifically labeling RNA with pairs of heavy atom probes, and precisely measuring the distribution of inter-probe distances that arise from a heterogeneous mixture of RNA solution structures. We show that the XSI-based model of the TAR RNA ensemble closely resembles an independent model derived from NMR-RDC data. Further, we show how the TAR RNA ensemble changes shape at different salt concentrations. Finally, we demonstrate that a single hybrid model of the TAR RNA ensemble simultaneously fits both the XSI and NMR-RDC data set and show that XSI can be combined with NMR-RDC to further improve the quality of the determined ensemble. The results suggest that XSI-RNA will be a powerful approach for characterizing the solution conformational ensembles of RNAs and RNA-protein complexes under diverse solution conditions.

A critical assessment of methods to recover information from averaged data

Conformational heterogeneity is key to the function of many biomacromolecules, but only a few groups have tried to characterize it until recently. Now, thanks to the increased throughput of experimental data and the increased computational power, the problem of the characterization of protein structural variability has become more and more popular. Several groups have devoted their efforts in trying to create quantitative, reliable and accurate protocols for extracting such information from averaged data. We analyze here different approaches, discussing strengths and weaknesses of each. All approaches can roughly be clustered into two groups: those satisfying the maximum entropy principle and those recovering ensembles composed of a restricted number of molecular conformations. In the first case, the solution focuses on the features that are common to all the infinite solutions satisfying the experimental data; in the second case, the reconstructed ensemble shows the conformational regions where a large probability can be placed. The upper limits for conformational probabilities (MaxOcc) can also be calculated. We also give an overview of the mainstream experimental observables, with considerations on the assumptions underlying their usage.

Chemo-enzymatic labeling for rapid assignment of RNA molecules

Even though Nuclear Magnetic Resonance (NMR) spectroscopy is one of the few techniques capable of determining atomic resolution structures of RNA, it is constrained by two major problems of chemical shift overlap of resonances and rapid signal loss due to line broadening. Emerging tools to tackle these problems include synthesis of atom specifically labeled or chemically modified nucleotides. Herein we review the synthesis of these nucleotides, the design and production of appropriate RNA samples, and the application and analysis of the NMR experiments that take advantage of these labels.

Structure Calculation and Reconstruction of Discrete-State Dynamics from Residual Dipolar Couplings

Residual dipolar couplings (RDCs) acquired by nuclear magnetic resonance (NMR) spectroscopy are an indispensable source of information in investigation of molecular structures and dynamics. Here, we present a comprehensive strategy for structure calculation and reconstruction of discrete-state dynamics from RDC data that is based on the singular value decomposition (SVD) method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ±1 Hz of error from an 83 residue α protein (PDB ID 1A1Z ) and a 213 residue α/β protein DGCR8 (PDB ID 2YT4 ). In nearly all instances, our method reproduced the structure of the protein including the conformational ensemble to within less than 2 Å. On the basis of our investigations, arc motions with more than 30° of rotation are identified as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ±1 Hz of error.