Conformational Ensemble Research Articles

Boosting the photocatalytic decontamination efficiency using a supramolecular photoenzyme ensemble.

Continuous industrialization has raised daunting environmental concerns, and there is an urgent need to develop a sustainable strategy to tackle the contamination issues. Here, we report a supramolecular photoenzyme ensemble enabling the harvest of solar energy to remove contaminations in water. The well-sourced oxidoreductase, laccase, is confined into a photoactive hydrogen-bonded organic framework (PHOF) through an in situ encapsulation method. The direct electron migration between the oxidation center in a PHOF and the reduction center in laccase facilitates synergistic photoenzyme-coupled catalysis, showing two orders of magnitude higher activity than free laccase for pollutant degradation under visible light, without the need for sacrificial agents or costly co-mediators. Such high decontamination efficiency also surpasses the reported catalysts. The structure and decontamination function of this supramolecular photoenzyme ensemble remain highly stable in complex environment matrices, presenting desirable reusability and almost 100% conversion efficiency of pollutants for real sewage samples. Our conceptual photoenzyme hybrid catalyst offers important insights into green and sustainable water decontamination.

In cell NMR reveals cells selectively amplify and structurally remodel amyloid fibrils.

Amyloid forms of α-synuclein adopt different conformations depending on environmental conditions. Advances in structural biology have accelerated fibril characterization. However, it remains unclear which conformations predominate in biological settings because current methods typically not only require isolating fibrils from their native environments, but they also do not provide insight about flexible regions. To address this, we characterized α-syn amyloid seeds and used sensitivity enhanced nuclear magnetic resonance to investigate the amyloid fibrils resulting from seeded amyloid propagation in different settings. We found that the amyloid fold and conformational preferences of flexible regions are faithfully propagated in vitro and in cellular lysates. However, seeded propagation of amyloids inside cells led to the minority conformation in the seeding population becoming predominant and more ordered, and altered the conformational preferences of flexible regions. The examination of the entire ensemble of protein conformations in biological settings that is made possible with this approach may advance our understanding of protein misfolding disorders and facilitate structure-based drug design efforts.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Cation-induced intramolecular coil-to-globule transition in poly(ADP-ribose)

Poly(ADP-ribose) (PAR), a non-canonical nucleic acid, is essential for DNA/RNA metabolism and protein condensation, and its dysregulation is linked to cancer and neurodegeneration. However, key structural insights into PAR’s functions remain largely uncharacterized, hindered by the challenges in synthesizing and characterizing PAR, which are attributed to its length heterogeneity. A central issue is how PAR, comprised solely of ADP-ribose units, attains specificity in its binding and condensing proteins based on chain length. Here, we integrate molecular dynamics simulations with small-angle X-ray scattering to analyze PAR structures. We identify diverse structural ensembles of PAR that fall into distinct subclasses and reveal distinct compaction of two different lengths of PAR upon the addition of small amounts of Mg2+ ions. Unlike PAR15, PAR22 forms ADP-ribose bundles via local intramolecular coil-to-globule transitions. Understanding these length-dependent structural changes could be central to deciphering the specific biological functions of PAR.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

A Comprehensive Study on the Mid-Infrared Variability of Blazars

We present a comprehensive investigation of mid-infrared (MIR) flux variability at 3.4 μm (W1 band) for a large sample of 3816 blazars, using Wide-field Infrared Survey Explorer (WISE) data through December 2022. The sample consists of 1740 flat-spectrum radio quasars (FSRQs), 1281 BL Lac objects (BL Lacs), and 795 blazars of uncertain type (BCUs). Considering Fermi Large Area Telescope detection, we classify 2331 as Fermi blazars and 1485 as non-Fermi blazars. Additionally, based on synchrotron peak frequency, the sample includes 2264 low-synchrotron peaked (LSP), 512 intermediate-synchrotron peaked (ISP), and 655 high-synchrotron peaked (HSP) sources. We conduct a comparative analysis of short- and long-term intrinsic variability amplitude (σm), duty cycle (DC), and ensemble structure function (ESF) across blazar subclasses. The median short-term σm values were 0.181−0.106+0.153, 0.104−0.054+0.101, 0.135−0.076+0.154, 0.173−0.097+0.158, 0.177−0.100+0.156, 0.096−0.050+0.109, and 0.106−0.058+0.100 mag for FSRQs, BL Lacs, Fermi blazars, non-Fermi blazars, LSPs, ISPs, and HSPs, respectively. The median DC values were 71.03−22.48+14.17, 64.02−22.86+16.97, 68.96−25.52+15.66, 69.40−22.17+14.42, 71.24−21.36+14.25, 63.03−33.19+16.93, and 64.63−24.26+15.88 percent for the same subclasses. The median long-term σm values were 0.137−0.105+0.408, 0.171−0.132+0.206, 0.282−0.184+0.332, 0.071−0.062+0.143, 0.218−0.174+0.386, 0.173−0.132+0.208, and 0.101−0.077+0.161 mag for the same subclasses, respectively. Our results reveal significant differences in 3.4 μm flux variability among these subclasses. FSRQs (LSPs) exhibit larger σm and DC values compared to BL Lacs (ISPs and HSPs). Fermi blazars display higher long-term σm but lower short-term σm relative to non-Fermi blazars, while DC distributions between the two groups are similar. ESF analysis further confirms the greater variability of FSRQs, LSPs, and Fermi blazars across a wide range of time scales compared to BL Lacs, ISPs/HSPs, and non-Fermi blazars. These findings highlight a close correlation between MIR variability and blazar properties, providing valuable insights into the underlying physical mechanisms responsible for their emission.

Differential Behavior of Conformational Dynamics in Active and Inactive States of Cannabinoid Receptor 1.

Cannabinoid receptor 1 (CB1) is a G protein-coupled receptor that regulates critical physiological processes including pain, appetite, and cognition. Understanding the conformational dynamics of CB1 associated with transitions between inactive and active signaling states is imperative for developing targeted modulators. Using microsecond-level all-atom molecular dynamics simulations, we identified marked differences in the conformational ensembles of inactive and active CB1 in apo. The inactive state exhibited substantially increased structural heterogeneity and plasticity compared to the more rigidified active state in the absence of stabilizing ligands. Transmembrane helices TM3 and TM7 were identified as distinguishing factors modulating the state-dependent dynamics. TM7 displayed amplified fluctuations selectively in the inactive state simulations attributed to disruption of conserved electrostatic contacts anchoring it to surrounding helices in the active state. Additionally, we identified significant reorganizations in key salt bridge and hydrogen bond networks contributing to the CB1 activation/inactivation. For instance, D213-Y224 hydrogen bond and D184-K192 salt bridge showed marked rearrangements between the states. Collectively, these findings reveal the specialized role of TM7 in directing state-dependent CB1 dynamics through electrostatic switch mechanisms. By elucidating the intrinsic enhanced flexibility of inactive CB1, this study provides valuable insights into the conformational landscape enabling functional transitions. Our perspective advances understanding of CB1 activation mechanisms and offers opportunities for structure-based drug discovery targeting the state-specific conformational dynamics of this receptor.

Kernel-Based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations.

A kernel-based method (kernelized minimal distributed charge model (kMDCM)) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges is introduced. The positions of the charges adapt to the molecular geometry and allow the description of intramolecular charge flow. Using Gaussian kernels and atom-atom distances as the features, the ESPs for water and methanol are shown to improve by at least a factor of 2 compared with point charge models fit to an ensemble of structures. The conformationally fluctuating molecular dipole moment of water is reproduced almost twice as accurately using kMDCM compared with static PCs, despite not fitting to the dipole directly. The role of hyperparameters in the kernelization is investigated and their implication on model performance and simulation stability is discussed. Combining kMDCM for the electrostatics and reproducing kernels for the bonded terms allows energy-conserving simulations of 2000 water molecules with periodic boundary conditions on the nanosecond time scale. These MD simulations sample geometries outside the training set but remain stable, which demonstrates the robustness of the model and its implementation.

Converting the Amyloidogenic Islet Amyloid Polypeptide into a Potent Nonaggregating Peptide Ligand by Side Chain-to-Side Chain Macrocyclization.

The islet amyloid polypeptide (IAPP), also known as amylin, is a hormone playing key physiological roles. However, its aggregation and deposition in the pancreatic islets are associated with type 2 diabetes. While this peptide adopts mainly a random coil structure in solution, its secondary conformational conversion into α-helix represents a critical step for receptor activation and contributes to amyloid formation and associated cytotoxicity. Considering the large conformational landscape and high amyloidogenicity of the peptide, as well as the complexity of the self-assembly process, it is challenging to delineate the delicate interplay between helical folding, peptide aggregation, and receptor activation. In the present study, we probed the roles of helical folding on the function-toxicity duality of IAPP by restricting its conformational ensemble through side chain-to-side chain stapling via azide-alkyne cycloaddition. Intramolecular macrocyclization (i; i + 4) constrained IAPP into α-helix and inhibited its aggregation into amyloid fibrils. These helical derivatives slowed down the self-assembly of unmodified IAPP. Site-specific macrocyclization modulated the capacity of IAPP to perturb lipid bilayers and cell plasma membrane and reduced, or even fully inhibited, the cytotoxicity associated with aggregation. Furthermore, the α-helical IAPP analogs showed moderate to high potency toward cognate G protein-coupled receptors. Overall, these results indicate that macrocyclization represents a promising strategy to protect an amyloidogenic peptide hormone from aggregation and associated toxicity, while maintaining high receptor activity.

An explicit multilevel power turbulent wall function based on the de-thresholding Douglas–Peucker algorithm

Wall functions are extensively applied in engineering simulations with turbulence. They facilitate a significant increase in the scale of the grids next to the wall, which in turn reduces the total number of grids needed. This optimization enhances computational efficiency, making the simulation process more effective and streamlined. However, the current commonly used wall functions, such as the Spalding wall function, are an implicit expression that needs to be solved iteratively, which affects the computational efficiency, and the multilayer segmented wall function is not smoothly articulated, which affects the fidelity. In this study, based on flat plate direct numerical simulation (DNS) data, combined with structural ensemble dynamics theory, the de-thresholding Douglas–Peucker algorithm is introduced to construct an explicit wall function expression in the form of multilevel power exponential concatenated multiplication. The comparison of the new wall function against DNS data reveals that it demonstrates superior fitting accuracy in contrast to the traditional ones, and eliminates the need for manual calibration, reduces subjective influence, and enhances reliability. The numerical simulation outcomes for the flat plate boundary layer and a series of airfoils showcase the new wall function's exceptional accuracy, which not only meets but also surpasses the demanding standards of engineering practice.

Synthesis, conformational analysis and host-guest chemistry of an ester-containing tub[4]arene with a conformational ensemble

Ester tub[4]arene, a conformationally adaptive macrocycle with interconvertible cavities, has been synthesized and characterized. Its conformational complexity was analyzed, and its binding ability to organic cations was studied by 1H NMR spectroscopy and UV–vis spectroscopy. The conformational conversion of the host can be achieved by binding a suitable guest. The binding performance of such adaptable receptors may have significant implications for the molecular recognition processes occurring in natural settings.

Visualization of the Cdc48 AAA+ ATPase protein unfolding pathway.

The Cdc48 AAA+ ATPase is an abundant and essential enzyme that unfolds substrates in multiple protein quality control pathways. The enzyme includes two conserved AAA+ ATPase motor domains, D1 and D2, that assemble as hexameric rings with D1 stacked above D2. Here, we report an ensemble of native structures of Cdc48 affinity purified from budding yeast lysate in complex with the adaptor Shp1 in the act of unfolding substrate. Our analysis reveals a continuum of structural snapshots that spans the entire translocation cycle. These data uncover elements of Shp1-Cdc48 interactions and support a 'hand-over-hand' mechanism in which the sequential movement of individual subunits is closely coordinated. D1 hydrolyzes ATP and disengages from substrate prior to D2, while D2 rebinds ATP and re-engages with substrate prior to D1, thereby explaining the dominant role played by the D2 motor in substrate translocation/unfolding.

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

StrIDR: a database of intrinsically disordered regions of proteins with experimentally resolved structures.

Intrinsically disordered regions (IDRs) of proteins exist as an ensemble of conformations, and not as a single structure. Existing databases contain extensive, experimentally derived annotations of intrinsic disorder for millions of proteins at the sequence level. However, only a tiny fraction of these IDRs are associated with an experimentally determined protein structure. Moreover, even if a structure exists, parts of the disordered regions may still be unresolved. Here we organize Structures of Intrinsically Disordered Regions (StrIDR), a database of IDRs confirmed via experimental or homology-based evidence, resolved in experimentally determined structures. The database can provide useful insights into the dynamics, folding, and interactions of IDRs. It can also facilitate computational studies on IDRs, such as those using molecular dynamics simulations and/or machine learning. StrIDR is available at https://isblab.ncbs.res.in/stridr. The web UI allows for downloading PDB structures and SIFTS mappings of individual entries. Additionally, the entire database can be downloaded in a JSON format. The source code for creating and updating the database is available at https://github.com/isblab/stridr.

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles.

Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, "solved" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.

Constitutive Model of Uniaxial Compression for Rock (Coal) and Bursting Liability Index Based on the Structure Ensemble Dynamics Theory

Constitutive Model of Uniaxial Compression for Rock (Coal) and Bursting Liability Index Based on the Structure Ensemble Dynamics Theory

The Influence of Disulfide, Thioacetal and Lanthionine-Bridges on the Conformation of a Macrocyclic Peptide.

Cyclisation of peptides by forming thioether (lanthionine), disulfide (cystine) or methylene thioacetal bridges between side chains is established as an important tool to stabilise a given structure, enhance metabolic stability and optimise both potency and selectivity. However, a systematic comparative study of the effects of differing bridging modalities on peptide conformation has not previously been carried out. In this paper, we have used the NMR deconvolution algorithm, NAMFIS, to determine the conformational ensembles, in aqueous solution, of three cyclic analogues of angiotensin(1-7), incorporating either disulfide, or non-reducible thioether or methylene thioacetal bridges. We demonstrate that the major solution conformations are conserved between the different bridged peptides, but the distribution of conformations differs appreciably. This suggests that subtle differences in ring size and bridging structure can be exploited to fine-tune the conformational properties of cyclic peptides, which may modulate their bioactivities.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules.

RNA molecules play a vital role in biological processes within the cell, with significant implications for science and medicine. Notably, the biological functions exerted by specific RNA molecules are often linked to the RNA conformational ensemble. However, the experimental characterization of such three-dimensional RNA structures is challenged by the structural heterogeneity of RNA and by its multiple dynamic interactions with binding partners such as small molecules, proteins, and metal ions. Consequently, our current understanding of the structure-function relationship of RNA molecules is still limited. In this context, we highlight molecular dynamics (MD) simulations as a powerful tool to complement experimental efforts on RNAs. Despite the recognized limitations of current force fields for RNA MD simulations, examining the dynamics of selected RNAs has provided valuable functional insights into their structures.

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning.

Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously adapt upon binding with specific interaction partners and/or certain alterations in environmental conditions. This malleability is foundational for their role as tunable interaction hubs in core cellular processes such as signaling, transcription, and translation. Tracing the conformational ensemble of an IDP/IDR and its perturbation in response to regulatory cues is thus paramount for illuminating its function. However, the conformational heterogeneity of IDP/IDRs poses several challenges. Here, we review experimental and computational methods devised to disentangle the conformational landscape of IDP/IDRs, highlighting recent computational advances that permit proteome-wide scans of IDP/IDRs conformations. We briefly evaluate selected computational methods using the disordered N-terminal of the human copper transporter 1 as a test case and outline further challenges in IDP/IDRs ensemble prediction.