An integrative NMR-SAXS approach for structural determination of large RNAs defines the substrate-free state of a trans-cleaving Neurospora Varkud Satellite ribozyme

The divide-and-conquer strategy is commonly used for protein structure determination, but its applications to high-resolution structure determination of RNAs have been limited. Here, we introduce an integrative approach based on the divide-and-conquer strategy that was undertaken to determine the solution structure of an RNA model system, the Neurospora VS ribozyme. NMR and SAXS studies were conducted on a minimal trans VS ribozyme as well as several isolated subdomains. A multi-step procedure was used for structure determination that first involved pairing refined NMR structures with SAXS data to obtain structural subensembles of the various subdomains. These subdomain structures were then assembled to build a large set of structural models of the ribozyme, which was subsequently filtered using SAXS data. The resulting NMR-SAXS structural ensemble shares several similarities with the reported crystal structures of the VS ribozyme. However, a local structural difference is observed that affects the global fold by shifting the relative orientation of the two three-way junctions. Thus, this finding highlights a global conformational change associated with substrate binding in the VS ribozyme that is likely critical for its enzymatic activity. Structural studies of other large RNAs should benefit from similar integrative approaches that allow conformational sampling of assembled fragments.     

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.     

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage‐structured, seasonal, nonlinear, two‐sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture‐mark‐recapture analysis, we find that seasonal sea ice concentration anomalies (SIC_a) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SIC_a, because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SIC_a. We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SIC_a, which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems.     

QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information

Background  

The selection of the most accurate protein model from a set of alternatives is a crucial step in protein structure prediction both in template-based and ab initio approaches. Scoring functions have been developed which can either return a quality estimate for a single model or derive a score from the information contained in the ensemble of models for a given sequence. Local structural features occurring more frequently in the ensemble have a greater probability of being correct. Within the context of the CASP experiment, these so called consensus methods have been shown to perform considerably better in selecting good candidate models, but tend to fail if the best models are far from the dominant structural cluster. In this paper we show that model selection can be improved if both approaches are combined by pre-filtering the models used during the calculation of the structural consensus.     

Refinement of Peptide Conformational Ensembles by 2D IR Spectroscopy: Application to Ala‒Ala‒Ala

Characterizing ensembles of intrinsically disordered proteins is experimentally challenging because of the ill-conditioned nature of ensemble determination with limited data and the intrinsic fast dynamics of the conformational ensemble. Amide I two-dimensional infrared (2D IR) spectroscopy has picosecond time resolution to freeze structural ensembles as needed for probing disordered-protein ensembles and conformational dynamics. Also, developments in amide I computational spectroscopy now allow a quantitative and direct prediction of amide I spectra based on conformational distributions drawn from molecular dynamics simulations, providing a route to ensemble refinement against experimental spectra. We performed a Bayesian ensemble refinement method on Ala–Ala–Ala against isotope-edited Fourier-transform infrared spectroscopy and 2D IR spectroscopy and tested potential factors affecting the quality of ensemble refinements. We found that isotope-edited 2D IR spectroscopy provides a stringent constraint on Ala–Ala–Ala conformations and returns consistent conformational ensembles with the dominant ppII conformer across varying prior distributions from many molecular dynamics force fields and water models. The dominant factor influencing ensemble refinements is the systematic frequency uncertainty from spectroscopic maps. However, the uncertainty of conformer populations can be significantly reduced by incorporating 2D IR spectra in addition to traditional Fourier-transform infrared spectra. Bayesian ensemble refinement against isotope-edited 2D IR spectroscopy thus provides a route to probe equilibrium-complex protein ensembles and potentially nonequilibrium conformational dynamics.     

Heterogeneity and inaccuracy in protein structures solved by X-ray crystallography

Proteins are dynamic molecules, exhibiting structural heterogeneity in the form of anisotropic motion and discrete conformational substates, often of functional importance. In protein structure determination by X-ray crystallography, the observed diffraction pattern results from the scattering of X-rays by an ensemble of heterogeneous molecules, ordered and oriented by packing in a crystal lattice. The majority of proteins diffract to resolutions where heterogeneity is difficult to identify and model, and are therefore approximated by a single, average conformation with isotropic variance. Here we show that disregarding structural heterogeneity introduces degeneracy into the structure determination process, as many single, isotropic models exist that explain the diffraction data equally well. The large differences among these models imply that the accuracy of crystallographic structures has been widely overestimated. Further, it suggests that analyses that depend on small differences in the relative positions of atoms may be flawed.     

Effect of interdomain dynamics on the structure determination of modular proteins by small-angle scattering

Multidomain proteins in which consecutive globular regions are connected by linkers are prevalent in nature (Levitt in Proc Natl Acad Sci USA 106:11079–11084, 2009). Some members of this family have largely resisted structural characterization as a result of challenges associated with their inherent flexibility. Small-angle scattering (SAS) is often the method of choice for their structural study. An extensive set of simulated data for both flexible and rigid multidomain systems was analyzed and modeled using standard protocols. This study clearly shows that SAXS profiles obtained from highly flexible proteins can be wrongly interpreted as arising from a rigid structure. In this context, it would be important to identify features from the SAXS data or from the derived structural models that indicate interdomain motions to differentiate between these two scenarios. Features of SAXS data that identify flexible proteins are: (1) general attenuation of fine structure in the scattering profiles, which becomes more dramatic in Kratky representations, and (2) a reduced number of interdomain correlation peaks in p(r) functions that also present large D _max values and a smooth decrease to 0. When modeling this dynamically averaged SAXS data, the structures obtained present characteristic trends: (1) ab initio models display a decrease in resolution, and (2) rigid-body models present highly extended conformations with a lack of interdomain contacts. The ensemble optimization method represents an excellent strategy to identify interdomain motions unambiguously. This study provides information that should help researchers to select the best modeling strategy for the structural interpretation of SAS experiments of multidomain proteins.     

Statistical mechanics analysis of sparse data

Inferential structure determination uses Bayesian theory to combine experimental data with prior structural knowledge into a posterior probability distribution over protein conformational space. The posterior distribution encodes everything one can say objectively about the native structure in the light of the available data and additional prior assumptions and can be searched for structural representatives. Here an analogy is drawn between the posterior distribution and the canonical ensemble of statistical physics. A statistical mechanics analysis assesses the complexity of a structure calculation globally in terms of ensemble properties. Analogs of the free energy and density of states are introduced; partition functions evaluate the consistency of prior assumptions with data. Critical behavior is observed with dwindling restraint density, which impairs structure determination with too sparse data. However, prior distributions with improved realism ameliorate the situation by lowering the critical number of observations. An in-depth analysis of various experimentally accessible structural parameters and force field terms will facilitate a statistical approach to protein structure determination with sparse data that avoids bias as much as possible.     

Investigating energy‐based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure‐based and physics‐based atomistic force field with an efficient sampling strategy is adopted to simulate a model di‐domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low‐energy structures and the minimum‐size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small‐angle X‐ray scattering data. It is illustrated that the regularizations of energy and ensemble‐size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high‐energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure‐ensemble optimizations with a topology‐based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.     

Accurate structural correlations from maximum likelihood superpositions

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology.     

Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

Hybrid computational methods combining experimental information with molecular dynamics

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.     

Direct methods and residue type specific isotope labeling in NMR structure determination and model-driven sequential assignment

Direct methods in NMR based structure determination start from an unassigned ensemble of unconnected gaseous hydrogen atoms. Under favorable conditions they can produce low resolution structures of proteins. Usually a prohibitively large number of NOEs is required, to solve a protein structure ab-initio, but even with a much smaller set of distance restraints low resolution models can be obtained which resemble a protein fold. One problem is that at such low resolution and in the absence of a force field it is impossible to distinguish the correct protein fold from its mirror image. In a hybrid approach these ambiguous models have the potential to aid in the process of sequential backbone chemical shift assignment when ¹³C^β and ¹³C′ shifts are not available for sensitivity reasons. Regardless of the overall fold they enhance the information content of the NOE spectra. These, combined with residue specific labeling and minimal triple-resonance data using ¹³C^α connectivity can provide almost complete sequential assignment. Strategies for residue type specific labeling with customized isotope labeling patterns are of great advantage in this context. Furthermore, this approach is to some extent error-tolerant with respect to data incompleteness, limited precision of the peak picking, and structural errors caused by misassignment of NOEs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Unraveling the symmetry ambiguity in a hexamer: Calculation of the R6 human insulin structure

Crystallographic and NMR studies of insulin have revealed a highly flexible molecule with a range of different aggregation and structural states; the importance of these states for the function of the hormone is still unclear. To address this question, we have studied the solution structure of the insulin R₆ symmetric hexamer using NMR spectroscopy. Structure determination of symmetric oligomers by NMR is complicated due to `symmetry ambiguity' between intra- and intermonomer NOEs, and between different classes of intermonomer NOEs. Hence, to date, only two symmetric tetramers and one symmetric pentamer (VTB, B subunit of verotoxin) have been solved by NMR; there has been no other symmetric hexamer or higher-order oligomer. Recently, we reported a solution structure for R₆ insulin hexamer. However, in that study, a crystal structure was used as a reference to resolve ambiguities caused by the threefold symmetry; the same method was used in solving VTB. Here, we have successfully recalculated R₆ insulin using the symmetry-ADR method, a computational strategy in which ambiguities are resolved using the NMR data alone. Thus the obtained structure is a refinement of the previous R₆ solution structure. Correlated motions in the final structural ensemble were analysed using a recently developed principal component method; this suggests the presence of two major conformational substates. The study demonstrates that the solution structure of higher-order symmetric oligomers can be determined unambiguously from NMR data alone, using the symmetry-ADR method. This success bodes well for future NMR studies of higher-order symmetric oligomers. The correlated motions observed in the structural ensemble suggest a new insight into the mechanism of phenol exchange and the T ₆ R ₆ transition of insulin in solution.     

The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data

In the present study, we investigate the determination accuracy of the Universal Thermal Climate Index (UTCI). We study especially the UTCI uncertainties due to uncertainties in radiation fluxes, whose impacts on UTCI are evaluated via the mean radiant temperature (Tmrt). We assume “normal conditions”, which means that usual meteorological information and data are available but no special additional measurements. First, the uncertainty arising only from the measurement uncertainties of the meteorological data is determined. Here, simulations show that uncertainties between 0.4 and 2 K due to the uncertainty of just one of the meteorological input parameters may be expected. We then analyse the determination accuracy when not all radiation data are available and modelling of the missing data is required. Since radiative transfer models require a lot of information that is usually not available, we concentrate only on the determination accuracy achievable with empirical models. The simulations show that uncertainties in the calculation of the diffuse irradiance may lead to Tmrt uncertainties of up to ±2.9 K. If long-wave radiation is missing, we may expect an uncertainty of ±2 K. If modelling of diffuse radiation and of longwave radiation is used for the calculation of Tmrt, we may then expect a determination uncertainty of ±3 K. If all radiative fluxes are modelled based on synoptic observation, the uncertainty in Tmrt is ±5.9 K. Because Tmrt is only one of the four input data required in the calculation of UTCI, the uncertainty in UTCI due to the uncertainty in radiation fluxes is less than ±2 K. The UTCI uncertainties due to uncertainties of the four meteorological input values are not larger than the 6 K reference intervals of the UTCI scale, which means that UTCI may only be wrong by one UTCI scale. This uncertainty may, however, be critical at the two temperature extremes, i.e. under extreme hot or extreme cold conditions.     

The inherent flexibility of peptides and protein fragments quantitized by CD in conjunction with CCA+

ECD spectroscopy is traditionally used for rapid, non‐atomic level structure analysis of natural products such as peptides and proteins. Unlike globular proteins, peptides less frequently adopt a single 3D‐fold in a time average manner. Moreover, they exhibit an ensemble of conformers composed of a multitude of substantially different structures. In principle, both ECD‐ and vibrational circular dichroism (VCD)‐spectroscopy are sensitive enough to pick up structural information on these dynamic ensembles. However, the interpretation of the raw spectral data of these highly dynamic molecular systems can be cumbersome. The herein presented Convex Constraint Analysis Plus method, or CCA+ for short ( http://www.chem.elte.hu/departments/protnmr/cca/ ), provides a unique opportunity for spectral ensemble analysis of peptides, glycopeptides, peptidomimetics, and other foldamers. The precision and accuracy of the approach is presented here through different peptide model systems. An interesting temperature and pH dependent folding and unfolding of a miniprotein (e.g. Tc5b variant) is also described. Analysis of CD spectra sets strongly affected by solvent and ion type is also introduced to account for severe environmental‐induced structure influencing effect(s). The deconvolution makes always possible the quantitative data analysis even when the interpretation of the deconvolution resulted in pure CD curves is complex. Copyright © 2009 European Peptide Society and John Wiley & Sons, Ltd.     

Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia

Given the limited resources available for weed management, a strategic approach is required to give the “best bang for your buck.” The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees [BRT]; maximum entropy [Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species’ invasive potential.     

COCO: a simple tool to enrich the representation of conformational variability in NMR structures

NMR structures are typically deposited in databases such as the PDB in the form of an ensemble of structures. Generally, each of the models in such an ensemble satisfies the experimental data and is equally valid. No unique solution can be calculated because the experimental NMR data is insufficient, in part because it reflects the conformational variability and dynamical behavior of the molecule in solution. Even for relatively rigid molecules, the limited number of structures that are typically deposited cannot completely encompass the structural diversity allowed by the observed NMR data, but they can be chosen to try and maximize its representation. We describe here the adaptation and application of techniques more commonly used to examine large ensembles from molecular dynamics simulations, to the analysis of NMR ensembles. The approach, which is based on principal component analysis, we call COCO ("Complementary Coordinates"). The COCO approach analyses the distribution of an NMR ensemble in conformational space, and generates a new ensemble that fills "gaps" in the distribution. The method is very rapid, and analysis of a 25-member ensemble and generation of a new 25 member ensemble typically takes 1-2 min on a conventional workstation. Applied to the 545 structures in the RECOORD database, we find that COCO generates new ensembles that are as structurally diverse-both from each other and from the original ensemble-as are the structures within the original ensemble. The COCO approach does not explicitly take into account the NMR restraint data, yet in tests on selected structures from the RECOORD database, the COCO ensembles are frequently good matches to this data, and certainly are structures that can be rapidly refined against the restraints to yield high-quality, novel solutions. COCO should therefore be a useful aid in NMR structure refinement and in other situations where a richer representation of conformational variability is desired-for example in docking studies. COCO is freely accessible via the website www.ccpb.ac.uk/COCO.     

Recent advances in maximum entropy biasing techniques for molecular dynamics

ABSTRACT

This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods which incorporate experimental data improve accuracy in molecular simulation by minimally modifying the thermodynamic ensemble. This is especially important where force fields are approximate, such as when employing coarse-grain models, or where high accuracy is required, such as when attempting to mimic a multiscale self-assembly process. The authors review here the experiment directed simulation (EDS) and experiment directed metadynamics (EDM) methods that allow matching averages and distributions in simulations, respectively. Important system-specific considerations are discussed such as using enhanced sampling simultaneously, the role of pressure, treating uncertainty, and implementations of these methods. Recent examples of EDS and EDM are reviewed including applications to ab initio molecular dynamics of water, incorporating environmental fluctuations inside of a macromolecular protein complex, improving RNA force fields, and the combination of enhanced sampling with minimal biasing to model peptides