Molecular Dynamics of Biomolecules through Direct Analysis of Dipolar Couplings

A team of chaperones play to win in the bacterial periplasm

The survival and virulence of Gram-negative bacteria require proper biogenesis and maintenance of the outer membrane (OM), which is densely packed with β-barrel OM proteins (OMPs). Before reaching the OM, precursor unfolded OMPs (uOMPs) must cross the whole cell envelope. A network of periplasmic chaperones and proteases maintains unfolded but folding-competent conformations of these membrane proteins in the aqueous periplasm while simultaneously preventing off-pathway aggregation. These periplasmic proteins utilize different strategies, including conformational heterogeneity, oligomerization, multivalency, and kinetic partitioning, to perform and regulate their functions. Redundant and unique characteristics of the individual periplasmic players synergize to create a protein quality control team capable responding to changing environmental stresses.

FkpA enhances membrane protein folding using an extensive interaction surface

Outer membrane protein (OMP) biogenesis in gram-negative bacteria is managed by a network of periplasmic chaperones that includes SurA, Skp, and FkpA. These chaperones bind unfolded OMPs (uOMPs) in dynamic conformational ensembles to suppress aggregation, facilitate diffusion across the periplasm, and enhance folding. FkpA primarily responds to heat-shock stress, but its mechanism is comparatively understudied. To determine FkpA chaperone function in the context of OMP folding, we monitored the folding of three OMPs and found that FkpA, unlike other periplasmic chaperones, increases the folded yield but decreases the folding rate of OMPs. The results indicate that FkpA behaves as a chaperone and not as a folding catalyst to influence the OMP folding trajectory. Consistent with the folding assay results, FkpA binds all three uOMPs as determined by sedimentation velocity (SV) and photo-crosslinking experiments. We determine the binding affinity between FkpA and uOmpA171 by globally fitting SV titrations and find it to be intermediate between the known affinities of Skp and SurA for uOMP clients. Notably, complex formation steeply depends on the urea concentration, suggesting an extensive binding interface. Initial characterizations of the complex using photo-crosslinking indicate that the binding interface spans the entire FkpA molecule. In contrast to prior findings, folding and binding experiments performed using subdomain constructs of FkpA demonstrate that the full-length chaperone is required for full activity. Together these results support that FkpA has a distinct and direct effect on OMP folding that it achieves by utilizing an extensive chaperone-client interface to tightly bind clients.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

Residual Dipolar Couplings have the potential to improve the accuracy and reduce the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, provide assignment of resonances, and characterize the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. One key factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can complement existing methods to provide faster and more accurate studies of larger and more complex protein structures by NMR spectroscopy in solution state.

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.

Conformational ensembles of an RNA hairpin using molecular dynamics and sparse NMR data

Solution nuclear magnetic resonance (NMR) experiments allow RNA dynamics to be determined in an aqueous environment. However, when a limited number of peaks are assigned, it is difficult to obtain structural information. We here show a protocol based on the combination of experimental data (Nuclear Overhauser Effect, NOE) and molecular dynamics simulations with enhanced sampling methods. This protocol allows to (a) obtain a maximum entropy ensemble compatible with NMR restraints and (b) obtain a minimal set of metastable conformations compatible with the experimental data (maximum parsimony). The method is applied to a hairpin of 29 nt from an inverted SINEB2, which is part of the SINEUP family and has been shown to enhance protein translation. A clustering procedure is introduced where the annotation of base-base interactions and glycosidic bond angles is used as a metric. By reweighting the contributions of the clusters, minimal sets of four conformations could be found which are compatible with the experimental data. A motif search on the structural database showed that some identified low-population states are present in experimental structures of other RNA transcripts. The introduced method can be applied to characterize RNA dynamics in systems where a limited amount of NMR information is available.

Structural Characterization of Biomolecules through Atomistic Simulations Guided by DEER Measurements

Double electron-electron resonance (DEER) is a popular technique that exploits attached spin labels to probe the collective dynamics of biomolecules in a native environment. Like most spectroscopic approaches, DEER detects an ensemble of states accounting for biomolecular dynamics as well as the labels' intrinsic flexibility. Hence, the DEER data alone do not provide high-resolution structural information. To disentangle this variability, we introduce a minimally biased simulation method to sample a structural ensemble that reproduces multiple experimental signals within the uncertainty. In contrast to previous approaches, our method targets the raw data themselves, and thereby it brings forth an unbiased molecular interpretation of the experiments. After validation on the T4 lysozyme, we apply this technique to interpret recent DEER experiments on a membrane transporter binding protein (VcSiaP). The results highlight the large-scale conformational movement that occurs upon substrate binding and reveal that the unbound VcSiaP is more open in solution than the X-ray structure.

Perspectives on paramagnetic NMR from a life sciences infrastructure

The effects arising in NMR spectroscopy because of the presence of unpaired electrons, collectively referred to as "paramagnetic NMR" have attracted increasing attention over the last decades. From the standpoint of the structural and mechanistic biology, paramagnetic NMR provides long range restraints that can be used to assess the accuracy of crystal structures in solution and to improve them by simultaneous refinements through NMR and X-ray data. These restraints also provide information on structure rearrangements and conformational variability in biomolecular systems. Theoretical improvements in quantum chemistry calculations can nowadays allow for accurate calculations of the paramagnetic data from a molecular structural model, thus providing a tool to refine the metal coordination environment by matching the paramagnetic effects observed far away from the metal. Furthermore, the availability of an improved technology (higher fields and faster magic angle spinning) has promoted paramagnetic NMR applications in the fast-growing area of biomolecular solid-state NMR. Major improvements in dynamic nuclear polarization have been recently achieved, especially through the exploitation of the Overhauser effect occurring through the contact-driven relaxation mechanism: the very large enhancement of the ¹³C signal observed in a variety of liquid organic compounds at high fields is expected to open up new perspectives for applications of solution NMR.

Dynamic Descriptions of Highly Flexible Molecules from NMR Dipolar Couplings: Physical Basis and Limitations

Biomolecules that control physiological function by changing their conformation play key roles in biology and remain poorly characterized. NMR dipolar couplings (DCs) depend intrinsically on both molecular shape and structural fluctuations, thereby providing the enticing prospect of tracking these conformational changes at atomic detail. Although this dual dependence has until now severely complicated analysis of DCs from highly dynamic systems, general approaches have recently been proposed that simplify interpretation of experimental DCs, by entirely eliminating molecular alignment from the analysis. Using simple and intuitive simulation of target ensembles, we investigate the impact of such approaches on the resulting descriptions of the conformational energy landscape. We find that ensemble descriptions of highly flexible systems derived from DCs without explicit consideration of the alignment properties of the constituent conformations can be compromised and inaccurate, despite exhibiting high correlation with experimental measurement.

Bayesian inference of protein ensembles from SAXS data

The inherent flexibility of intrinsically disordered proteins (IDPs) and multi-domain proteins with intrinsically disordered regions (IDRs) presents challenges to structural analysis. These macromolecules need to be represented by an ensemble of conformations, rather than a single structure. Small-angle X-ray scattering (SAXS) experiments capture ensemble-averaged data for the set of conformations. We present a Bayesian approach to ensemble inference from SAXS data, called Bayesian ensemble SAXS (BE-SAXS). We address two issues with existing methods: the use of a finite ensemble of structures to represent the underlying distribution, and the selection of that ensemble as a subset of an initial pool of structures. This is achieved through the formulation of a Bayesian posterior of the conformational space. BE-SAXS modifies a structural prior distribution in accordance with the experimental data. It uses multi-step expectation maximization, with alternating rounds of Markov-chain Monte Carlo simulation and empirical Bayes optimization. We demonstrate the method by employing it to obtain a conformational ensemble of the antitoxin PaaA2 and comparing the results to a published ensemble.

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.

The Exact NOE as an Alternative in Ensemble Structure Determination

The structure-function paradigm is increasingly replaced by the structure-dynamics-function paradigm. All protein activity is steered by the interplay between enthalpy and entropy. Conformational dynamics serves as a proxy of conformational entropy. Therefore, it is essential to study not only the average conformation but also the spatial sampling of a protein on all timescales. To this purpose, we have established a protocol for determining multiple-state ensembles of proteins based on exact nuclear Overhauser effects (eNOEs). We have recently extended our previously reported eNOE data set for the protein GB3 by a very large set of backbone and side-chain residual dipolar couplings and three-bond J couplings. Here, we demonstrate that at least four structural states are required to represent the complete data set by dissecting the contributions to the CYANA target function, which quantifies restraint violations in structure calculation. We present a four-state ensemble of GB3, which largely preserves the characteristics obtained from eNOEs only. Due to the abundance of the input data, the ensemble and χ(1) angles in particular are well suited for cross-validation of the input data and comparison to x-ray structures. Principal component analysis is used to automatically identify and validate relevant states of the ensembles. Overall, our findings suggest that eNOEs are a valuable alternative to traditional NMR probes in spatial elucidation of proteins.

The Dynamic Basis for Signal Propagation in Human Pin1-WW

Allostery is the structural manifestation of information transduction in biomolecules. Its hallmark is conformational change induced by perturbations at a distal site. An increasing body of evidence demonstrates the presence of allostery in very flexible and even disordered proteins, encouraging a thermodynamic description of this phenomenon. Still, resolving such processes at atomic resolution is difficult. Here we establish a protocol to determine atomistic thermodynamic models of such systems using high-resolution solution state nuclear magnetic resonance data and extensive molecular simulations. Using this methodology, we study information transduction in the WW domain of a key cell-cycle regulator Pin1. Pin1 binds promiscuously to phospho-Ser/Thr-Pro motifs, however, disparate structural and dynamic responses have been reported upon binding different ligands. Our model consists of two topologically distinct states whose relative population may be specifically skewed by an incoming ligand. This model provides a canonical basis for the understanding of multi-functionality in Pin1.

Structure of a low-population binding intermediate in protein-RNA recognition

The interaction of the HIV-1 protein transactivator of transcription (Tat) and its cognate transactivation response element (TAR) RNA transactivates viral transcription and represents a paradigm for the widespread occurrence of conformational rearrangements in protein-RNA recognition. Although the structures of free and bound forms of TAR are well characterized, the conformations of the intermediates in the binding process are still unknown. By determining the free energy landscape of the complex using NMR residual dipolar couplings in replica-averaged metadynamics simulations, we observe two low-population intermediates. We then rationally design two mutants, one in the protein and another in the RNA, that weaken specific nonnative interactions that stabilize one of the intermediates. By using surface plasmon resonance, we show that these mutations lower the release rate of Tat, as predicted. These results identify the structure of an intermediate for RNA-protein binding and illustrate a general strategy to achieve this goal with high resolution.