Conformational Ensemble Research Articles

Thrombin - A Molecular Dynamics Perspective.

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.

Dynamic molecular portraits of ion-conducting pores characterize functional states of TRPV channels

Structural biology is solving an ever-increasing number of snapshots of ion channel conformational ensembles. Deciphering ion channel mechanisms, however, requires understanding the ensemble dynamics beyond the static structures. Here, we present a molecular modeling-based approach characterizing the ion channel structural intermediates, or their “dynamic molecular portraits”, by assessing water and ion conductivity along with the detailed evaluation of pore hydrophobicity and residue packing. We illustrate the power of this approach by analyzing structures of few vanilloid-subfamily transient receptor potential (TRPV) channels. Based on the pore architecture, there are three major states that are common for TRPVs, which we call α-closed, π-closed, and π-open. We show that the pore hydrophobicity and residue packing for the open state is most favorable for the pore conductance. On the contrary, the α-closed state is the most hydrophobic and always non-conducting. Our approach can also be used for structural and functional classification of ion channels.

Mechanism of folding and stability of Met80Gly mutant of cytochrome-c

The present study deals with identifying conformational ensembles underlying the folding pathway of a model protein, the wild type (WT) horse cytochrome-c (cyt-c) and its mutant (M80G) in water and 8 M urea at different temperatures (300–450 K). Outcomes of this study revealed that the structural dynamics of both proteins remain restricted to the marginal changes in water and urea at 300 and 350 K. The dynamic equilibrium shifted towards unfolding at 400 K, and structures of both proteins are lost completely at 450 K, however, it occurred distinctly in water and urea. Free energy landscape analyses suggested that the unfolding of the WT and M80G proteins happens through different conformational states, the native (N), meta-stable intermediate (IN), intermediate (I), partially unfolded (IU) and completely unfolded (U) states. In urea at 400 K, IN state of M80G mutant is relatively long-lived as compared to the WT protein. However, the structure of M80G protein is lost more frequently in urea at 450 K. Our in vitro studies of the urea-induced denaturation of the WT cyt-c and its M80G mutant at pH 6.0 and 25 °C showed that the mutant is more stable than the WT protein. This conclusion is reached from the analysis of the denaturation curve (mean residue ellipticity at 222 nm versus urea concentration plot).

Multiscale Bayesian simulations reveal functional chromatin condensation of gene loci.

Chromatin, the complex assembly of DNA and associated proteins, plays a pivotal role in orchestrating various genomic functions. To aid our understanding of the principles underlying chromatin organization, we introduce Hi-C metainference, a Bayesian approach that integrates Hi-C contact frequencies into multiscale prior models of chromatin. This approach combines both bottom-up (the physics-based prior) and top-down (the data-driven posterior) strategies to characterize the 3D organization of a target genomic locus. We first demonstrate the capability of this method to accurately reconstruct the structural ensemble and the dynamics of a system from contact information. We then apply the approach to investigate the Sox2, Pou5f1, and Nanog loci of mouse embryonic stem cells using a bottom-up chromatin model at 1 kb resolution. We observe that the studied loci are conformationally heterogeneous and organized as crumpled globules, favoring contacts between distant enhancers and promoters. Using nucleosome-resolution simulations, we then reveal how the Nanog gene is functionally organized across the multiple scales of chromatin. At the local level, we identify diverse tetranucleosome folding motifs with a characteristic distribution along the genome, predominantly open at cis-regulatory elements and compact in between. At the larger scale, we find that enhancer-promoter contacts are driven by the transient condensation of chromatin into compact domains stabilized by extensive internucleosome interactions. Overall, this work highlights the condensed, but dynamic nature of chromatin in vivo, contributing to a deeper understanding of gene structure-function relationships.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

PACSAB Server: A Web-Based Tool for the Study of Aggregation and the Conformational Ensemble of Disordered and Folded Proteins.

We present in this article the PACSAB server, which is designed to provide information about the structural ensemble and interactions of both stable and disordered proteins to researchers in the field of molecular biology. The use of this tool does not require any computational skills as the user just needs to upload the structure of the protein to be studied; the server runs a simulation with the PACSAB model, a highly accurate coarse-grained model that is much more efficient than standard molecular dynamics for the exploration of the conformational space of multiprotein systems. The trajectories generated by the simulations based on this model reveal the propensity of the protein under study for aggregation, identify the residues playing a central role in the aggregation process, and reproduce the whole conformational space of disordered proteins. All of this information is shown and can be downloaded from the web page.

Active learning of ternary alloy structures and energies

Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides.

Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.

Transferable deep generative modeling of intrinsically disordered protein conformations.

Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.

Cellular lipids regulate the conformational ensembles of the disordered intracellular loop 3 in β2-adrenergic receptor

The intracellular loops of G protein-coupled receptors (GPCRs) have been shown to play a key role in Gprotein coupling and selectivity. We recently showed that the intrinsically disordered third intracellular loop (ICL3) of β2-adrenergic receptor is dynamic and equilibrates between open and closed conformationsto regulate the G protein coupling. In this study, using the extensive molecular dynamics simulations in multi-lipid bilayer models, we show that the lipid phosphatidylinositol 4,5-bisphosphate (PIP2) stabilizes the active state of β2-adrenergic receptor by keeping ICL3 in an open conformation. This stabilization results in a tilt of the receptor within the membrane. Additionally, the ganglioside lipid, GM3 interacts with extracellular loops, impacting the ligand binding site allosterically. This demonstrates the active role of the chemistry of lipids in stabilizing specific GPCR conformations.

The molecular basis for hydrodynamic properties of PEGylated human serum albumin

Polyethylene glycol (PEG) conjugation provides a protective modification that enhances the pharmacokinetics and solubility of proteins for therapeutic use. A knowledge of the structural ensemble of these PEGylated proteins is necessary to understand the molecular details that contribute to their hydrodynamic and colligative properties. Because of the large size and dynamic flexibility of pharmaceutically important PEGylated proteins, the determination of structure is challenging. In addition, the hydration of these conjugates that contain large polymers is difficult to determine with traditional methods that identify only first shell hydration water, which does not account for the complete hydrodynamic volume of a macromolecule. Here, we demonstrate that structural ensembles, generated by coarse-grained simulations, can be analyzed with HullRad and used to predict sedimentation coefficients and concentration-dependent hydrodynamic and diffusion nonideality coefficients of PEGylated proteins. A knowledge of these concentration-dependent properties enhances the ability to design and analyze new modified protein therapeutics. HullRad accomplishes this analysis by effectively accounting for the complete hydration of a macromolecule, including that of flexible polymers.

Accurate and Efficient Conformer Sampling of Cyclic Drug-Like Molecules with Inverse Kinematics.

Identification of all of the influential conformers of biomolecules is a crucial step in many tasks of computational biochemistry. Specifically, molecular docking, a key component of in silico drug development, requires a comprehensive set of conformations for potential candidates in order to generate the optimal ligand-receptor poses and, ultimately, find the best drug candidates. However, the presence of flexible cycles in a molecule complicates the initial search for conformers since exhaustive sampling algorithms via torsional random and systematic searches become very inefficient. The devised inverse-kinematics-based Monte Carlo with refinement (MCR) algorithm identifies independently rotatable dihedral angles in (poly)cyclic molecules and uses them to perform global conformational sampling, outperforming popular alternatives (MacroModel, CREST, and RDKit) in terms of speed and diversity of the resulting conformer ensembles. Moreover, MCR quickly and accurately recovers naturally occurring macrocycle conformations for most of the considered molecules.

Insight into the Effect of Methylglyoxal on the Conformation, Function, and Aggregation Propensity of α-Synuclein.

It is well-known that people suffering from hyperglycemia have a higher propensity to develop Parkinson's disease (PD). One of the most plausible mechanisms linking these two pathologies is the glycation of neuronal proteins and the pathological consequences of it. α-Synuclein, a key component in PD, can be glycated at its fifteen lysine. In fact, the end products of this process have been detected on aggregated α-synuclein isolated from in vivo. However, the consequences of glycation are not entirely clear, which are of crucial importance to understand the mechanism underlying the connection between diabetes and PD. To better clarify this, we have here examined how methylglyoxal (the most important carbonyl compound found in the cytoplasm) affects the conformation and aggregation propensity of α-synuclein, as well as its ability to cluster and fuse synaptic-like vesicles. The obtained data prove that methylglyoxal induces the Lys-Lys crosslinking through the formation of MOLD. However, this does not have a remarkable effect on the averaged conformational ensemble of α-synuclein, although it completely depletes its native propensity to form soluble oligomers and insoluble amyloid fibrils. Moreover, methylglyoxal has a disrupting effect on the ability of α-synuclein to bind, cluster and fusion synaptic-like vesicles.

Probability Density Reweighting of High-Temperature Molecular Dynamics.

Molecular dynamics (MD) simulation is a popular method for elucidating the structures and functions of biomolecules. However, exploring the conformational space, especially for large systems with slow transitions, often requires enhanced sampling methods. Although conducting MD at high temperatures provides a straightforward approach, resulting conformational ensembles diverge significantly from those at low temperatures. To address this discrepancy, we propose a novel probability density-based reweighting (PDR) method. PDR exhibits robust performance across four distinct systems, including a miniprotein, a cyclic peptide, a protein loop, and a protein-peptide complex. It accurately restores the conformational distributions at high temperatures to those at low temperatures. Additionally, we apply PDR to reweight previously studied high-T MD simulations of 12 protein-peptide complexes, enabling a comprehensive investigation of the conformational space of protein-peptide complexes.

Structural mechanisms for binding and activation of a contact-quenched fluorophore by RhoBAST.

The fluorescent light-up aptamer RhoBAST, which binds and activates the fluorophore-quencher conjugate tetramethylrhodamine-dinitroaniline with high affinity, super high brightness, remarkable photostability, and fast exchange kinetics, exhibits excellent performance in super-resolution RNA imaging. Here we determine the co-crystal structure of RhoBAST in complex with tetramethylrhodamine-dinitroaniline to elucidate the molecular basis for ligand binding and fluorescence activation. The structure exhibits an asymmetric "A"-like architecture for RhoBAST with a semi-open binding pocket harboring the xanthene of tetramethylrhodamine at the tip, while the dinitroaniline quencher stacks over the phenyl of tetramethylrhodamine instead of being fully released. Molecular dynamics simulations show highly heterogeneous conformational ensembles with the contact-but-unstacked fluorophore-quencher conformation for both free and bound tetramethylrhodamine-dinitroaniline being predominant. The simulations also show that, upon RNA binding, the fraction of xanthene-dinitroaniline stacked conformation significantly decreases in free tetramethylrhodamine-dinitroaniline. This highlights the importance of releasing dinitroaniline from xanthene tetramethylrhodamine to unquench the RhoBAST-tetramethylrhodamine-dinitroaniline complex. Using SAXS and ITC, we characterized the magnesium dependency of the folding and binding mode of RhoBAST in solution and indicated its strong structural robustness. The structures and binding modes of relevant fluorescent light-up aptamers are compared, providing mechanistic insights for rational design and optimization of this important fluorescent light-up aptamer-ligand system.

Dispersive surface free energy of adsorbents modified by supramolecular structures of heterocyclic compounds

In the present work, the dispersive surface free energy was calculated by Dorris-Gray method for 30 samples of adsorbents modified with chiral supramolecular structures of uracil, 6-methyluracil, 5-hydroxy-6-methyluracil, 5-fluorouracil, thymine, melamine, cyanuric and barbirutic acids, and perylene-3,4,9,10-tetracarboxylic dianhydride. It was shown that the homologous series of n-alkanes is better suited for measuring dispersive surface free energy than the homologous series of alcohols. It was established that the classical Dorris-Gray method does not allow obtaining well interpretable data for the objects studied. This is due to a noticeable effect that inductive interactions of a polar surface as an inductor with nonpolar alkane molecules have on the calculated values. We suggested to modify the Dorris-Gray method, making it possible to obtain data on the dispersive component of the free energy of adsorption. It was shown that the changes in dispersive surface free energy as a result of the modification correlate well with data on the structure and properties of supramolecular ensembles of the modifiers used. The results obtained can be used to predict the enantioselectivity of chiral adsorbents.

Local Ordering, Distortion, and Redox Activity in (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 Investigated by a Computational Workflow for Compositionally Complex Perovskite Oxides.

Mixing multiple cations can result in a significant configurational entropy, offer a new compositional space with vast tunability, and introduce new computational challenges. For applications such as the two-step solar thermochemical hydrogen (STCH) generation techniques, we demonstrate that using density functional theory (DFT) combined with Metropolis Monte Carlo method (DFT-MC) can efficiently sample the possible cation configurations in compositionally complex perovskite oxide (CCPO) materials, with (La0.75Sr0.25)(Mn0.25Fe0.25Co0.25Al0.25)O3 as an example. In the presence of oxygen vacancies (VO), DFT-MC simulations reveal a significant increase of the local site preference of the cations (short-range ordering), compared to a more random mixing without VO. Co is found to be the redox-active element and the VO is the preferentially generated next to Co due to the stretched Co-O bonds. A clear definition of the vacancy formation energy (Evf) is proposed for CCPO in an ensemble of structures evolved in parallel from independent DFT-MC paths. By combining the distribution of Evf with VO interactions into a statistical model, the oxygen nonstoichiometry (δ), under the STCH thermal reduction and oxidation conditions, is predicted and compared with the experiments. Similar to the experiments, the predicted δ can be used to extract the enthalpy and entropy of reduction using the van't Hoff method, providing direct comparisons with the experimental results. This procedure provides a full predictive workflow for using DFT-MC to obtain possible local ordering or fully random structures, understand the redox activity of each element, and predict the thermodynamic properties of CCPOs, for computational screening and design of these CCPO materials at STCH conditions.

LoCoHD: a metric for comparing local environments of proteins

Protein folds and the local environments they create can be compared using a variety of differently designed measures, such as the root mean squared deviation, the global distance test, the template modeling score or the local distance difference test. Although these measures have proven to be useful for a variety of tasks, each fails to fully incorporate the valuable chemical information inherent to atoms and residues, and considers these only partially and indirectly. Here, we develop the highly flexible local composition Hellinger distance (LoCoHD) metric, which is based on the chemical composition of local residue environments. Using LoCoHD, we analyze the chemical heterogeneity of amino acid environments and identify valines having the most conserved-, and arginines having the most variable chemical environments. We use LoCoHD to investigate structural ensembles, to evaluate critical assessment of structure prediction (CASP) competitors, to compare the results with the local distance difference test (lDDT) scoring system, and to evaluate a molecular dynamics simulation. We show that LoCoHD measurements provide unique information about protein structures that is distinct from, for example, those derived using the alignment-based RMSD metric, or the similarly distance matrix-based but alignment-free lDDT metric.

A Combined Molecular Dynamics and Hydropathic INTeraction (HINT) Approach to Investigate Protein Flexibility: The PPARγ Case Study.

Molecular Dynamics (MD) is a computational technique widely used to evaluate a molecular system's thermodynamic properties and conformational behavior over time. In particular, the energy analysis of a protein conformation ensemble produced though MD simulations plays a crucial role in explaining the relationship between protein dynamics and its mechanism of action. In this research work, the HINT (Hydropathic INTeractions) LogP-based scoring function was first used to handle MD trajectories and investigate the molecular basis behind the intricate PPARγ mechanism of activation. The Peroxisome Proliferator-Activated Receptor γ (PPARγ) is an emblematic example of a highly flexible protein due to the extended ω-loop delimiting the active site, and it is responsible for the receptor's ability to bind chemically different compounds. In this work, we focused on the PPARγ complex with Rosiglitazone, a common anti-diabetic compound and analyzed the molecular basis of the flexible ω-loop stabilization effect produced by the Oleic Acid co-binding. The HINT-based analysis of the produced MD trajectories allowed us to account for all of the energetic contributions involved in interconverting between conformational states and describe the intramolecular interactions between the flexible ω-loop and the helix H3 triggered by the allosteric binding mechanism.

The Collapse of the Vaults of the Ambato Matriz Church in the 1949 Earthquake: A Response with a Technical Approach

This research examines the structural behavior of the central vault of the Matriz church that collapsed during the 1949 earthquake in Ambato, Ecuador. Following the disaster, the church and Episcopal Palace were demolished. The central vault lacked buttresses and had large cracks two years before the collapse. A study was conducted using graphic restitution and the theory of limit analysis of structures to analyze the mechanical behavior of the central vault. The aim was to provide a technical explanation for the cause of the collapse beyond the impact of the earthquake. The limit analysis of structures is a tool that determines the technical stability conditions of a masonry structure using graphic statics. In this case, the respective checks of the structural behavior could be made, concluding that the structural ensemble of the central vault was unstable and could have collapsed at any moment, not necessarily due to an earthquake. The article is based on the research project “Architectural analysis of the central nucleus of Ambato between 1895 and 1949: styles, types, techniques, losses, and continuities”, which was conducted at the Universidad Técnica de Ambato, Ecuador, from 2021 to 2023.