Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints.

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Differential global structural changes in the core particle of yeast and mouse proteasome induced by ligand binding.

Two clusters of configurations of the main proteolytic subunit ß5 were identified by principal component analysis of crystal structures of the yeast proteasome core particle (yCP). The apo-cluster encompasses unliganded species and complexes with nonpeptidic ligands, and the pep-cluster comprises complexes with peptidic ligands. The murine constitutive CP structures conform to the yeast system, with the apo-form settled in the apo-cluster and the PR-957 (a peptidic ligand) complex in the pep-cluster. In striking contrast, the murine immune CP classifies into the pep-cluster in both the apo and the PR-957-liganded species. The two clusters differ essentially by multiple small structural changes and a domain motion enabling enclosure of the peptidic ligand and formation of specific hydrogen bonds in the pep-cluster. The immune CP species is in optimal peptide binding configuration also in its apo form. This favors productive ligand binding and may help to explain the generally increased functional activity of the immunoproteasome. Molecular dynamics simulations of the representative murine species are consistent with the experimentally observed configurations. A comparison of all 28 subunits of the unliganded species with the peptidic liganded forms demonstrates a greatly enhanced plasticity of ß5 and suggests specific signaling pathways to other subunits.

Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.

A mechanism for the auto-inhibition of hyperpolarization-activated cyclic nucleotide-gated (HCN) channel opening and its relief by cAMP.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels control neuronal and cardiac electrical rhythmicity. There are four homologous isoforms (HCN1-4) sharing a common multidomain architecture that includes an N-terminal transmembrane tetrameric ion channel followed by a cytoplasmic "C-linker," which connects a more distal cAMP-binding domain (CBD) to the inner pore. Channel opening is primarily stimulated by transmembrane elements that sense membrane hyperpolarization, although cAMP reduces the voltage required for HCN activation by promoting tetramerization of the intracellular C-linker, which in turn relieves auto-inhibition of the inner pore gate. Although binding of cAMP has been proposed to relieve auto-inhibition by affecting the structure of the C-linker and CBD, the nature and extent of these cAMP-dependent changes remain limitedly explored. Here, we used NMR to probe the changes caused by the binding of cAMP and of cCMP, a partial agonist, to the apo-CBD of HCN4. Our data indicate that the CBD exists in a dynamic two-state equilibrium, whose position as gauged by NMR chemical shifts correlates with the V½ voltage measured through electrophysiology. In the absence of cAMP, the most populated CBD state leads to steric clashes with the activated or "tetrameric" C-linker, which becomes energetically unfavored. The steric clashes of the apo tetramer are eliminated either by cAMP binding, which selects for a CBD state devoid of steric clashes with the tetrameric C-linker and facilitates channel opening, or by a transition of apo-HCN to monomers or dimer of dimers, in which the C-linker becomes less structured, and channel opening is not facilitated.

A hybrid NMR/SAXS-based approach for discriminating oligomeric protein interfaces using Rosetta.

Oligomeric proteins are important targets for structure determination in solution. While in most cases the fold of individual subunits can be determined experimentally, or predicted by homology-based methods, protein-protein interfaces are challenging to determine de novo using conventional NMR structure determination protocols. Here we focus on a member of the bet-V1 superfamily, Aha1 from Colwellia psychrerythraea. This family displays a broad range of crystallographic interfaces none of which can be reconciled with the NMR and SAXS data collected for Aha1. Unlike conventional methods relying on a dense network of experimental restraints, the sparse data are used to limit conformational search during optimization of a physically realistic energy function. This work highlights a new approach for studying minor conformational changes due to structural plasticity within a single dimeric interface in solution.

Access to Cα backbone dynamics of biological solids by 13C T1 relaxation and molecular dynamics simulation.

We introduce a labeling scheme for magic angle spinning (MAS) solid-state NMR that is based on deuteration in combination with dilution of the carbon spin system. The labeling strategy achieves spectral editing by simplification of the HαCα and aliphatic side chain spectral region. A reduction in both proton and carbon spin density in combination with fast spinning (≥50 kHz) is essential to retrieve artifact-free (13)C-R1 relaxation data for aliphatic carbons. We obtain good agreement between the NMR experimental data and order parameters extracted from a molecular dynamics (MD) trajectory, which indicates that carbon based relaxation parameters can yield complementary information on protein backbone as well as side chain dynamics.

Determination of solution structures of proteins up to 40 kDa using CS-Rosetta with sparse NMR data from deuterated samples.

We have developed an approach for determining NMR structures of proteins over 20 kDa that utilizes sparse distance restraints obtained using transverse relaxation optimized spectroscopy experiments on perdeuterated samples to guide RASREC Rosetta NMR structure calculations. The method was tested on 11 proteins ranging from 15 to 40 kDa, seven of which were previously unsolved. The RASREC Rosetta models were in good agreement with models obtained using traditional NMR methods with larger restraint sets. In five cases X-ray structures were determined or were available, allowing comparison of the accuracy of the Rosetta models and conventional NMR models. In all five cases, the Rosetta models were more similar to the X-ray structures over both the backbone and side-chain conformations than the "best effort" structures determined by conventional methods. The incorporation of sparse distance restraints into RASREC Rosetta allows routine determination of high-quality solution NMR structures for proteins up to 40 kDa, and should be broadly useful in structural biology.

Automatic NOESY assignment in CS-RASREC-Rosetta.

We have developed an approach for simultaneous structure calculation and automatic Nuclear Overhauser Effect (NOE) assignment to solve nuclear magnetic resonance (NMR) structures from unassigned NOESY data. The approach, autoNOE-Rosetta, integrates Resolution Adapted Structural RECombination (RASREC) Rosetta NMR calculations with algorithms for automatic NOE assignment. The method was applied to two proteins in the 15-20 kDa size range for which both, NMR and X-ray data, is available. The autoNOE-Rosetta calculations converge for both proteins and yield accurate structures with an RMSD of 1.9 Å to the X-ray reference structures. The method greatly expands the radius of convergence for automatic NOE assignment, and should be broadly useful for NMR structure determination.

Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta.

We have developed a novel and robust approach for automatic and unsupervised simultaneous nuclear Overhauser effect (NOE) assignment and structure determination within the CS-Rosetta framework. Starting from unassigned peak lists and chemical shift assignments, autoNOE-Rosetta determines NOE cross-peak assignments and generates structural models. The approach tolerates incomplete and raw NOE peak lists as well as incomplete or partially incorrect chemical shift assignments, and its performance has been tested on 50 protein targets ranging from 50 to 200 residues in size. We find a significantly improved performance compared to established programs, particularly for larger proteins and for NOE data obtained on perdeuterated protein samples. X-ray crystallographic structures allowed comparison of Rosetta and conventional, PDB-deposited, NMR models in 20 of 50 test cases. The unsupervised autoNOE-Rosetta models were often of significantly higher accuracy than the corresponding expert-supervised NMR models deposited in the PDB. We also tested the method with unrefined peak lists and found that performance was nearly as good as for refined peak lists. Finally, demonstrating our method's remarkable robustness against problematic input data, we provided correct models for an incorrect PDB-deposited NMR solution structure.

Site-specific analysis of heteronuclear Overhauser effects in microcrystalline proteins.

Relaxation parameters such as longitudinal relaxation are susceptible to artifacts such as spin diffusion, and can be affected by paramagnetic impurities as e.g. oxygen, which make a quantitative interpretation difficult. We present here the site-specific measurement of [(1)H](13)C and [(1)H](15)N heteronuclear rates in an immobilized protein. For methyls, a strong effect is expected due to the three-fold rotation of the methyl group. Quantification of the [(1)H](13)C heteronuclear NOE in combination with (13)C-R 1 can yield a more accurate analysis of side chain motional parameters. The observation of significant [(1)H](15)N heteronuclear NOEs for certain backbone amides, as well as for specific asparagine/glutamine sidechain amides is consistent with MD simulations. The measurement of site-specific heteronuclear NOEs is enabled by the use of highly deuterated microcrystalline protein samples in which spin diffusion is reduced in comparison to protonated samples.

Improvement of virtual screening results by docking data feature analysis.

In this study, we propose a novel approach to evaluate virtual screening (VS) experiments based on the analysis of docking output data. This approach, which we refer to as docking data feature analysis (DDFA), consists of two steps. First, a set of features derived from the docking output data is computed and assigned to each molecule in the virtually screened library. Second, an artificial neural network (ANN) analyzes the molecule's docking features and estimates its activity. Given the simple architecture of the ANN, DDFA can be easily adapted to deal with information from several docking programs simultaneously. We tested our approach on the Directory of Useful Decoys (DUD), a well-established and highly accepted VS benchmark. Outstanding results were obtained by DDFA not only in comparison with the conventional rankings of the docking programs used in this work but also with respect to other methods found in the literature. Our approach performs with similar good results as the best available methods, which, however, also require substantially more computing time, economic resources, and/or expert intervention. Taken together, DDFA represents an automatic and highly attractive methodology for VS.

Improved chemical shift based fragment selection for CS-Rosetta using Rosetta3 fragment picker.

A new fragment picker has been developed for CS-Rosetta that combines beneficial features of the original fragment picker, MFR, used with CS-Rosetta, and the fragment picker, NNMake, that was used for purely sequence based fragment selection in the context of ROSETTA de-novo structure prediction. Additionally, the new fragment picker has reduced sensitivity to outliers and other difficult to match data points rendering the protocol more robust and less likely to introduce bias towards wrong conformations in cases where data is bad, missing or inconclusive. The fragment picker protocol gives significant improvements on 6 of 23 CS-Rosetta targets. An independent benchmark on 39 protein targets, whose NMR data sets were published only after protocol optimization had been finished, also show significantly improved performance for the new fragment picker (van der Schot et al. in J Biomol NMR, 2013).

Improving 3D structure prediction from chemical shift data.

We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50-100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 Å RMSD from the reference).

Resolution-adapted recombination of structural features significantly improves sampling in restraint-guided structure calculation.

Recent work has shown that NMR structures can be determined by integrating sparse NMR data with structure prediction methods such as Rosetta. The experimental data serve to guide the search for the lowest energy state towards the deep minimum at the native state which is frequently missed in Rosetta de novo structure calculations. However, as the protein size increases, sampling again becomes limiting; for example, the standard Rosetta protocol involving Monte Carlo fragment insertion starting from an extended chain fails to converge for proteins over 150 amino acids even with guidance from chemical shifts (CS-Rosetta) and other NMR data. The primary limitation of this protocol--that every folding trajectory is completely independent of every other--was recently overcome with the development of a new approach involving resolution-adapted structural recombination (RASREC). Here we describe the RASREC approach in detail and compare it to standard CS-Rosetta. We show that the improved sampling of RASREC is essential in obtaining accurate structures over a benchmark set of 11 proteins in the 15-25 kDa size range using chemical shifts, backbone RDCs and HN-HN NOE data; in a number of cases the improved sampling methodology makes a larger contribution than incorporation of additional experimental data. Experimental data are invaluable for guiding sampling to the vicinity of the global energy minimum, but for larger proteins, the standard Rosetta fold-from-extended-chain protocol does not converge on the native minimum even with experimental data and the more powerful RASREC approach is necessary to converge to accurate solutions.

Determination of the structures of symmetric protein oligomers from NMR chemical shifts and residual dipolar couplings.

Symmetric protein dimers, trimers, and higher-order cyclic oligomers play key roles in many biological processes. However, structural studies of oligomeric systems by solution NMR can be difficult due to slow tumbling of the system and the difficulty in identifying NOE interactions across protein interfaces. Here, we present an automated method (RosettaOligomers) for determining the solution structures of oligomeric systems using only chemical shifts, sparse NOEs, and domain orientation restraints from residual dipolar couplings (RDCs) without a need for a previously determined structure of the monomeric subunit. The method integrates previously developed Rosetta protocols for solving the structures of monomeric proteins using sparse NMR data and for predicting the structures of both nonintertwined and intertwined symmetric oligomers. We illustrated the performance of the method using a benchmark set of nine protein dimers, one trimer, and one tetramer with available experimental data and various interface topologies. The final converged structures are found to be in good agreement with both experimental data and previously published high-resolution structures. The new approach is more readily applicable to large oligomeric systems than conventional structure-determination protocols, which often require a large number of NOEs, and will likely become increasingly relevant as more high-molecular weight systems are studied by NMR.

Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data.

Protein dynamics on the atomic level and on the microsecond timescale has recently become accessible from both computation and experiment. To validate molecular dynamics (MD) at the submicrosecond timescale against experiment we present microsecond MD simulations in 10 different force-field configurations for two globular proteins, ubiquitin and the gb3 domain of protein G, for which extensive NMR data is available. We find that the reproduction of the measured NMR data strongly depends on the chosen force field and electrostatics treatment. Generally, particle-mesh Ewald outperforms cut-off and reaction-field approaches. A comparison to measured J-couplings across hydrogen bonds suggests that there is room for improvement in the force-field description of hydrogen bonds in most modern force fields. Our results show that with current force fields, simulations beyond hundreds of nanoseconds run an increased risk of undergoing transitions to nonnative conformational states or will persist within states of high free energy for too long, thus skewing the obtained population frequencies. Only for the AMBER99sb force field have such transitions not been observed. Thus, our results have significance for the interpretation of data obtained with long MD simulations, for the selection of force fields for MD studies and for force-field development. We hope that this comprehensive benchmark based on NMR data applied to many popular MD force fields will serve as a useful resource to the MD community. Finally, we find that for gb3, the force-field AMBER99sb reaches comparable accuracy in back-calculated residual dipolar couplings and J-couplings across hydrogen bonds to ensembles obtained by refinement against NMR data.

Adaptive anisotropic kernels for nonparametric estimation of absolute configurational entropies in high-dimensional configuration spaces.

The quasiharmonic approximation is the most widely used estimate for the configurational entropy of macromolecules from configurational ensembles generated from atomistic simulations. This method, however, rests on two assumptions that severely limit its applicability, (i) that a principal component analysis yields sufficiently uncorrelated modes and (ii) that configurational densities can be well approximated by Gaussian functions. In this paper we introduce a nonparametric density estimation method which rests on adaptive anisotropic kernels. It is shown that this method provides accurate configurational entropies for up to 45 dimensions thus improving on the quasiharmonic approximation. When embedded in the minimally coupled subspace framework, large macromolecules of biological interest become accessible, as demonstrated for the 67-residue coldshock protein.

Full correlation analysis of conformational protein dynamics.

Correlated motions in biomolecules are often essential for their function, for example, allosteric signal transduction or mechanical/thermodynamic energy transport. Principal component analysis (PCA) is a widely used method to extract functionally relevant collective motions from a molecular dynamics (MD) trajectory. Being based on the covariance matrix, however, PCA detects only linear correlations. Here we present a new method, full correlation analysis (FCA), which is based on mutual information and thus quantifies all correlations, including nonlinear and higher order correlations. For comparison, we applied both, PCA and FCA, to approximately 100 ns MD trajectories of T4 lysozyme and the hexapeptide neurotensin. For both systems, FCA yielded better resolved conformational substates and aligned its modes more often with actual transition pathways. This improved resolution is shown to be due to a strongly increased anharmonicity of FCA modes as compared to the respective PCA modes. The high anharmonicity further suggests that the motions extracted by FCA are functionally more relevant than those captured by PCA. In summary, FCA should provide improved collective degrees of freedom for dimension-reduced descriptions of macromolecular dynamics.

Residual dipolar couplings as a tool to study molecular recognition of ubiquitin.

RDCs (residual dipolar couplings) in NMR spectroscopy provide information about protein dynamics complementary to NMR relaxation methods, especially in the previously inaccessible time window between the protein correlation time tau(c) and 50 micros. For ubiquitin, new modes of motion of the protein backbone could be detected using RDC-based techniques. An ensemble of ubiquitin based on these RDC values is found to comprise all different conformations that ubiquitin adopts upon binding to different recognition proteins. These conformations in protein-protein complexes had been derived from 46 X-ray structures. Thus, for ubiquitin recognition by other proteins, conformational selection rather than induced fit seems to be the dominant mechanism.

Generalized correlation for biomolecular dynamics.

Correlated motions in biomolecules are often essential for their function, e.g., allosteric signal transduction or mechanical/thermodynamic energy transport. Because correlated motions in biomolecules remain difficult to access experimentally, molecular dynamics (MD) simulations are particular useful for their analysis. The established method to quantify correlations from MD simulations via calculation of the covariance matrix, however, is restricted to linear correlations and therefore misses part of the correlations in the atomic fluctuations. Herein, we propose a general statistical mechanics approach to detect and quantify any correlated motion from MD trajectories. This generalized correlation measure is contrasted with correlations obtained from covariance matrices for the B1 domain of protein G and T4 lysozyme. The new method successfully quantifies correlations and provides a valuable global overview over the functionally relevant collective motions of lysozyme. In particular, correlated motions of helix 1 together with the two main lobes of lysozyme are detected, which are not seen by the conventional covariance matrix. Overall, the established method misses more than 50% of the correlation. This failure is attributed to both, an interfering and unnecessary dependence on mutual orientations of the atomic fluctuations and, to a lesser extent, attributed to nonlinear correlations. Our generalized correlation measure overcomes these problems and, moreover, allows for an improved understanding of the conformational dynamics by separating linear and nonlinear contributions of the correlation.