Ensemble Refinement Research Articles

Molecular Dynamics Ensemble Refinement of Intrinsically Disordered Peptides According to Deconvoluted Spectra from Circular Dichroism

We have developed a computational method of atomistically refining the structural ensemble of intrinsically disordered peptides (IDPs) facilitated by experimental measurements using circular dichroism spectroscopy (CD). A major challenge surrounding this approach stems from the deconvolution of experimental CD spectra into secondary structure features of the IDP ensemble. Currently available algorithms for CD deconvolution were designed to analyze the spectra of proteins with stable secondary structures. Herein, our work aims to minimize any bias from the peptide deconvolution analysis by implementing a non-negative linear least-squares fitting algorithm in conjunction with a CD reference data set that contains soluble and denatured proteins (SDP48). The non-negative linear least-squares method yields the best results for deconvolution of proteins with higher disordered content than currently available methods, according to a validation analysis of a set of protein spectra with Protein Data Bank entries. We subsequently used this analysis to deconvolute our experimental CD data to refine our computational model of the peptide secondary structure ensemble produced by all-atom molecular dynamics simulations with implicit solvent. We applied this approach to determine the ensemble structures of a set of short IDPs, that mimic the calmodulin binding domain of calcium/calmodulin-dependent protein kinase II and its 1-amino-acid and 3-amino-acid mutants. Our study offers a, to our knowledge, novel way to solve the ensemble secondary structures of IDPs in solution, which is important to advance the understanding of their roles in regulating signaling pathways through the formation of complexes with multiple partners.

Conformational Plasticity of HLA-B27 Molecules Correlates Inversely With Efficiency of Negative T Cell Selection.

The development of autoimmune disorders is incompletely understood. Inefficient thymic T cell selection against self-peptides presented by major histocompatibility antigens (HLA in humans) may contribute to the emergence of auto-reactive effector cells, and molecular mimicry between foreign and self-peptides could promote T cell cross-reactivity. A pair of class I subtypes, HLA-B2705 and HLA-B2709, have previously been intensely studied, because they are distinguished from each other only by a single amino acid exchange at the floor of the peptide-binding groove, yet are differentially associated with the autoinflammatory disorder ankylosing spondylitis. Using X-ray crystallography in combination with ensemble refinement, we find that the non-disease-associated subtype HLA-B2709, when presenting the self-peptide pGR (RRRWHRWRL), exhibits elevated conformational dynamics, and the complex can also be recognized by T cells. Both features are not observed in case of the sequence-related self-peptide pVIPR (RRKWRRWHL) in complex with this subtype, and T cell cross-reactivity between pGR, pVIPR, and the viral peptide pLMP2 (RRRWRRLTV) is only rarely observed. The disease-associated subtype HLA-B2705, however, exhibits extensive conformational flexibility in case of the three complexes, all of which are also recognized by frequently occurring cross-reactive T cells. A comparison of the structural and dynamic properties of the six HLA-B27 complexes, together with their individual ability to interact with T cells, permits us to correlate the flexibility of HLA-B27 complexes with effector cell reactivity. The results suggest the existence of an inverse relationship between conformational plasticity of peptide-HLA-B27 complexes and the efficiency of negative selection of self-reactive cells within the thymus.

Structural and Dynamic Effects of Phosphorylation of Protein Kinase a Catalytic Subunit

Protein Kinase A (PKA) is an enzyme involved in many physiological processes due to its role in signaling pathways, including regulation of metabolism and memory formation. Mutations in PKA are associated with cancer, cardiac disorders, and neurodegenerative disorders. PKA is a tetramer comprised of two regulatory (R) and two catalytic (c) subunits, forming the holoenzyme complex. My work focuses on the C-subunit. Phosphorylation of different residues in the PKA C-subunit lead to distinct phosphorylation states (phospho-states) with roles that are not well understood. I have studied and compared the structures and dynamics of two phospho-states using protein crystallography. Using room temperature X-ray diffraction data collected at the Berkeley Advanced Light Source and a published starting model, I solved the phases for the electron densities and refined the atomic structure for each dataset. Each dataset, comprised of diffraction data from several crystals per complex, were scaled and merged for final ensemble refinement. Ensemble refinement of each complex found differences in dynamics between phospho-states in several important secondary structures in the protein: the A-Helix, the Hydrophobic Motif, and the H-Helix. This could lead to functional differences of the two phospho-states in the cell. To further study functional differences, I used fluorescence polarization biochemical assays to compare the cyclic AMP-dependent activation of the holoenzyme complex formed with each phospho-state of the C-subunit. Studying the effects of phosphorylation on the structure and dynamics of PKA will lead to a better understanding of how these differences affect their function in the cell. The differential dynamics observed in the ensemble refinements of each phospho-state of the C-subunit could lead to insights on the binding of inhibitors and development of functionally specific therapeutics.

Combining Biophysical Experiments and Biomolecular Simulations

Biological macromolecules are dynamic entities and their ability to adopt alternative conformations can be central to their function. Examples include motions that underlie regulation or ligand binding, membrane proteins that act as efficient and selective transporters across biological membranes, or intrinsically disordered proteins that can adopt their shape to binding partners. I will discuss methods and applications for how simulations and experiments can be used synergistically to study protein and RNA dynamics. Despite recent progress in both force fields and sampling methods, one may still find that a molecular simulation does not match quantitatively experimental measurements. Then, experiments and simulations may be combined in a very direct fashion to provide a description of the molecular motions that combines the details of atomic simulations with the accuracy afforded by experimental measurements. The resulting conformational ensembles may provide novel insight into biomolecular systems that are not obtainable by simulations of experiments alone. I will discuss how this may be achieved, and give examples of the application of such approaches using both NMR and small-angle scattering experiments. I will begin by describing our software for performing Bayesian/Maximum Entropy refinement of conformational ensembles, and then provide several examples of how we have used it to refine conformational ensembles of proteins. Ensemble-refinement methods are particularly relevant for studying highly dynamical entities such as flexible RNA molecules and intrinsically disordered protein, and I will describe how we have been able to use simulations and a range of experiments together to study in particular IDPs. I will show how the framework naturally brings together information from molecular simulations with that from NMR and small-angle scattering experiments to provide information about both local and global structural properties of dynamical biomolecules.

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.

The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing.

On the structure and function of Escherichia coli YjhC: An oxidoreductase involved in bacterial sialic acid metabolism.

Human pathogenic and commensal bacteria have evolved the ability to scavenge host-derived sialic acids and subsequently degrade them as a source of nutrition. Expression of the Escherichia coli yjhBC operon is controlled by the repressor protein nanR, which regulates the core machinery responsible for the import and catabolic processing of sialic acid. The role of the yjhBC encoded proteins is not known-here, we demonstrate that the enzyme YjhC is an oxidoreductase/dehydrogenase involved in bacterial sialic acid degradation. First, we demonstrate in vivo using knockout experiments that YjhC is broadly involved in carbohydrate metabolism, including that of N-acetyl-d-glucosamine, N-acetyl-d-galactosamine and N-acetylneuraminic acid. Differential scanning fluorimetry demonstrates that YjhC binds N-acetylneuraminic acid and its lactone variant, along with NAD(H), which is consistent with its role as an oxidoreductase. Next, we solved the crystal structure of YjhC in complex with the NAD(H) cofactor to 1.35 Å resolution. The protein fold belongs to the Gfo/Idh/MocA protein family. The dimeric assembly observed in the crystal form is confirmed through solution studies. Ensemble refinement reveals a flexible loop region that may play a key role during catalysis, providing essential contacts to stabilize the substrate-a unique feature to YjhC among closely related structures. Guided by the structure, in silico docking experiments support the binding of sialic acid and several common derivatives in the binding pocket, which has an overall positive charge distribution. Taken together, our results verify the role of YjhC as a bona fide oxidoreductase/dehydrogenase and provide the first evidence to support its involvement in sialic acid metabolism.

Metal-triggered conformational reorientation of a self-peptide bound to a disease-associated HLA-B*27 subtype

Conformational changes of major histocompatibility complex (MHC) antigens have the potential to be recognized by T cells and may arise from polymorphic variation of the MHC molecule, the binding of modifying ligands, or both. Here, we investigated whether metal ions could affect allele-dependent structural variation of the two minimally distinct human leukocyte antigen (HLA)-B*27:05 and HLA-B*27:09 subtypes, which exhibit differential association with the rheumatic disease ankylosing spondylitis (AS). We employed NMR spectroscopy and X-ray crystallography coupled with ensemble refinement to study the AS-associated HLA-B*27:05 subtype and the AS-nonassociated HLA-B* 27:09 in complex with the self-peptide pVIPR (RRKWRRWHL). Both techniques revealed that pVIPR exhibits a higher degree of flexibility when complexed with HLA-B*27:05 than with HLA-B*27:09. Furthermore, we found that the binding of the metal ion Cu2+ or Ni2+, but not Mn2+, Zn2+, or Hg2+, affects the structure of a pVIPR-bound HLA-B*27 molecule in a subtype-dependent manner. In HLA-B*27:05, the metals triggered conformational reorientations of pVIPR, but no such structural changes were observed in the HLA-B*27:09 subtype, with or without bound metal ion. These observations provide the first demonstration that not only major histocompatibility complex class II, but also class I, molecules can undergo metal ion-induced conformational alterations. Our findings suggest that metals may have a role in triggering rheumatic diseases such as AS and also have implications for the molecular basis of metal-induced hypersensitivities and allergies.

Structural basis for functional interactions in dimers of SLC26 transporters

The SLC26 family of transporters maintains anion equilibria in all kingdoms of life. The family shares a 7 + 7 transmembrane segments inverted repeat architecture with the SLC4 and SLC23 families, but holds a regulatory STAS domain in addition. While the only experimental SLC26 structure is monomeric, SLC26 proteins form structural and functional dimers in the lipid membrane. Here we resolve the structure of an SLC26 dimer embedded in a lipid membrane and characterize its functional relevance by combining PELDOR/DEER distance measurements and biochemical studies with MD simulations and spin-label ensemble refinement. Our structural model reveals a unique interface different from the SLC4 and SLC23 families. The functionally relevant STAS domain is no prerequisite for dimerization. Characterization of heterodimers indicates that protomers in the dimer functionally interact. The combined structural and functional data define the framework for a mechanistic understanding of functional cooperativity in SLC26 dimers.

Efficient Ensemble Refinement by Reweighting.

Ensemble refinement produces structural ensembles of flexible and dynamic biomolecules by integrating experimental data and molecular simulations. Here we present two efficient numerical methods to solve the computationally challenging maximum-entropy problem arising from a Bayesian formulation of ensemble refinement. Recasting the resulting constrained weight optimization problem into an unconstrained form enables the use of gradient-based algorithms. In two complementary formulations that differ in their dimensionality, we optimize either the log-weights directly or the generalized forces appearing in the explicit analytical form of the solution. We first demonstrate the robustness, accuracy, and efficiency of the two methods using synthetic data. We then use NMR J-couplings to reweight an all-atom molecular dynamics simulation ensemble of the disordered peptide Ala-5 simulated with the AMBER99SB*-ildn-q force field. After reweighting, we find a consistent increase in the population of the polyproline-II conformations and a decrease of α-helical-like conformations. Ensemble refinement makes it possible to infer detailed structural models for biomolecules exhibiting significant dynamics, such as intrinsically disordered proteins, by combining input from experiment and simulation in a balanced manner.

Allostery in Coagulation Factor VIIa Revealed by Ensemble Refinement of Crystallographic Structures

A critical step in injury-induced initiation of blood coagulation is the formation of the complex between the trypsin-like protease coagulation factor VIIa (FVIIa) and its cofactor tissue factor (TF), which converts FVIIa from an intrinsically poor enzyme to an active protease capable of activating zymogens of downstream coagulation proteases. Unlike its constitutively active ancestor trypsin, FVIIa is allosterically activated (by TF). Here, ensemble refinement of crystallographic structures, which uses multiple copies of the entire structure as a means of representing structural flexibility, is applied to explore the impacts of inhibitor binding to trypsin and FVIIa, as well as cofactor binding to FVIIa. To assess the conformational flexibility and its role in allosteric pathways in these proteases, main-chain hydrogen bond networks are analyzed by calculating the hydrogen-bond propensity. Mapping pairwise propensity differences between relevant structures shows that binding of the inhibitor benzamidine to trypsin has a minor influence on the protease flexibility. For FVIIa, in contrast, the protease domain is “locked” into the catalytically competent trypsin-like configuration upon benzamidine binding as indicated by the stabilization of key structural features: the nonprime binding cleft and the oxyanion hole are stabilized, and the effect propagates from the active site region to the calcium-binding site and to the vicinity of the disulphide bridge connecting with the light chain. TF binding to FVIIa furthermore results in stabilization of the 170 loop, which in turn propagates an allosteric signal from the TF-binding region to the active site. Analyses of disulphide bridge energy and flexibility reflect the striking stability difference between the unregulated enzyme and the allosterically activated form after inhibitor or cofactor binding. The ensemble refinement analyses show directly, for the first time to our knowledge, whole-domain structural footprints of TF-induced allosteric networks present in x-ray crystallographic structures of FVIIa, which previously only have been hypothesized or indirectly inferred.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles.

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), 3J-couplings (3Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

Rigid-body motion is the main source of diffuse scattering in protein crystallography.

The origin of diffuse X-ray scattering from protein crystals has been the subject of debate over the past three decades regarding whether it arises from correlated atomic motions within the molecule or from rigid-body disorder. Here, a supercell approach to modelling diffuse scattering is presented that uses ensembles of molecular models representing rigid-body motions as well as internal motions as obtained from ensemble refinement. This approach allows oversampling of Miller indices and comparison with equally oversampled diffuse data, thus allowing the maximum information to be extracted from experiments. It is found that most of the diffuse scattering comes from correlated motions within the unit cell, with only a minor contribution from longer-range correlated displacements. Rigid-body motions, and in particular rigid-body translations, make by far the most dominant contribution to the diffuse scattering, and internal motions give only a modest addition. This suggests that modelling biologically relevant protein dynamics from diffuse scattering may present an even larger challenge than was thought.

Dealing with Dynamics and Disorder by Combining Simulation and Experiment

Many biomolecular systems are highly dynamic. In mechanistic studies using the tools of structural biology, dynamics and disorder pose major challenges. In my talk, I will show how the combination of a range of experiments with structural modeling and molecular simulations can address these challenges. I will show how results from disparate experiments and computational approaches can be combined within a Bayesian formulation of ensemble refinement. On this basis, I will describe approaches to deal with dynamic systems in cryo-electron microscopy, and on sampling and analyzing the conformation space and dynamics of disordered proteins and nucleic acids using NMR, SAXS, and fluorescence data.

Characterization and structure determination of a llama-derived nanobody targeting the J-base binding protein 1.

J-base binding protein 1 (JBP1) contributes to the biosynthesis and maintenance of base J (β-D-glucosylhydroxymethyluracil), a modification of thymidine confined to some protozoa. Camelid (llama) single-domain antibody fragments (nanobodies) targeting JBP1 were produced for use as crystallization chaperones. Surface plasmon resonance screening identified Nb6 as a strong binder, recognizing JBP1 with a 1:1 stoichiometry and high affinity (Kd = 30 nM). Crystallization trials of JBP1 in complex with Nb6 yielded crystals that diffracted to 1.47 Å resolution. However, the dimensions of the asymmetric unit and molecular replacement with a nanobody structure clearly showed that the crystals of the expected complex with JBP1 were of the nanobody alone. Nb6 crystallizes in space group P31 with two molecules in the asymmetric unit; its crystal structure was refined to a final resolution of 1.64 Å. Ensemble refinement suggests that in the ligand-free state one of the complementarity-determining regions (CDRs) is flexible, while the other two adopt well defined conformations.

Improving signal strength in serial crystallography with DIALS geometry refinement.

The DIALS diffraction-modeling software package has been applied to serial crystallography data. Diffraction modeling is an exercise in determining the experimental parameters, such as incident beam wavelength, crystal unit cell and orientation, and detector geometry, that are most consistent with the observed positions of Bragg spots. These parameters can be refined by nonlinear least-squares fitting. In previous work, it has been challenging to refine both the positions of the sensors (metrology) on multipanel imaging detectors such as the CSPAD and the orientations of all of the crystals studied. Since the optimal models for metrology and crystal orientation are interdependent, alternate cycles of panel refinement and crystal refinement have been required. To simplify the process, a sparse linear algebra technique for solving the normal equations was implemented, allowing the detector panels to be refined simultaneously against the diffraction from thousands of crystals with excellent computational performance. Separately, it is shown how to refine the metrology of a second CSPAD detector, positioned at a distance of 2.5 m from the crystal, used for recording low-angle reflections. With the ability to jointly refine the detector position against the ensemble of all crystals used for structure determination, it is shown that ensemble refinement greatly reduces the apparent nonisomorphism that is often observed in the unit-cell distributions from still-shot serial crystallography. In addition, it is shown that batching the images by timestamp and re-refining the detector position can realistically model small, time-dependent variations in detector position relative to the sample, and thereby improve the integrated structure-factor intensity signal and heavy-atom anomalous peak heights.

Molecular dynamics ensemble refinement of the heterogeneous native state of NCBD using chemical shifts and NOEs.

Many proteins display complex dynamical properties that are often intimately linked to their biological functions. As the native state of a protein is best described as an ensemble of conformations, it is important to be able to generate models of native state ensembles with high accuracy. Due to limitations in sampling efficiency and force field accuracy it is, however, challenging to obtain accurate ensembles of protein conformations by the use of molecular simulations alone. Here we show that dynamic ensemble refinement, which combines an accurate atomistic force field with commonly available nuclear magnetic resonance (NMR) chemical shifts and NOEs, can provide a detailed and accurate description of the conformational ensemble of the native state of a highly dynamic protein. As both NOEs and chemical shifts are averaged on timescales up to milliseconds, the resulting ensembles reflect the structural heterogeneity that goes beyond that probed, e.g., by NMR relaxation order parameters. We selected the small protein domain NCBD as object of our study since this protein, which has been characterized experimentally in substantial detail, displays a rich and complex dynamical behaviour. In particular, the protein has been described as having a molten-globule like structure, but with a relatively rigid core. Our approach allowed us to describe the conformational dynamics of NCBD in solution, and to probe the structural heterogeneity resulting from both short- and long-timescale dynamics by the calculation of order parameters on different time scales. These results illustrate the usefulness of our approach since they show that NCBD is rather rigid on the nanosecond timescale, but interconverts within a broader ensemble on longer timescales, thus enabling the derivation of a coherent set of conclusions from various NMR experiments on this protein, which could otherwise appear in contradiction with each other.

Previously Hidden Dynamics at the TCR–Peptide–MHC Interface Revealed

A structural characterization of the interaction between αβ TCRs and cognate peptide-MHC (pMHC) is central to understanding adaptive T cell-mediated immunity. X-ray crystallography, although the source of much structural data, traditionally provides only a static snapshot of the protein. Given the emerging evidence for the important role of conformational dynamics in protein function, we interrogated 309 crystallographic structures of pMHC complexes using ensemble refinement, a technique that can extract dynamic information from the x-ray data. Focusing on a subset of human pMHC class I systems, we found that in many cases, ensemble methods were able to uncover previously hidden evidence of significant conformational plasticity, thereby revealing additional information that can build upon and significantly enhance functional interpretations that are based on a single static structure. Notable examples include the interpretation of differences in the disease association of HLA subtypes, the relationship between peptide prominence and TCR recognition, the role of conformational flexibility in vaccine design, and the discrimination between induced fit and conformational selection models of TCR binding. We show that the currently widespread practice of analyzing pMHC interactions via the study of a single crystallographic structure does not make use of pertinent and easily accessible information from x-ray data concerning alternative protein conformations. This new analysis therefore not only highlights the capacity for ensemble methods to significantly enrich the interpretation of decades of structural data but also provides previously missing information concerning the dynamics of existing characterized TCR-pMHC interactions.

Gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations.

Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles. LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi. Supplementary data are available at Bioinformatics online.

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.

Protein crystal lattices are dynamic assemblies: the role of conformational entropy in the protein condensed phase.

Until recently, the occurrence of conformational entropy in protein crystal contacts was considered to be a very unlikely event. A study based on the most accurately refined protein structures demonstrated that side-chain conformational entropy and static disorder might be common in protein crystal lattices. The present investigation uses structures refined using ensemble refinement to show that although paradoxical, conformational entropy is likely to be the major factor in the emergence and integrity of the protein condensed phase. This study reveals that the role of shape entropy and local entropic forces expands beyond the onset of crystallization. For the first time, the complete pattern of intermolecular interactions by protein atoms in crystal lattices is presented, which shows that van der Waals interactions dominate in crystal formation.