Exploration Of Conformational Space Research Articles

Simulation-Guided Conformational Space Exploration to Assess Reactive Conformations of a Ribozyme.

Self-splicing ribozymes are small ribonucleic acid (RNA) enzymes that catalyze their own cleavage through a transphosphoesterification reaction. While this process is involved in some specific steps of viral RNA replication and splicing, it is also of importance in the context of the (putative) first autocatalytic RNA-based systems that could have preceded the emergence of modern life. The uncatalyzed phosphoester bond formation is thermodynamically very unfavorable, and many experimental studies have focused on understanding the molecular features of catalysis in these ribozymes. However, chemical reaction paths are short-lived and not easily characterized by experimental approaches, so molecular simulation approaches appear as an ideal tool to unveil the molecular details of the reaction. Here, we focus on the model hairpin ribozyme. We show that identifying a relevant initial conformation for reactivity studies, which is frequently overlooked in mixed quantum-classical studies that predominantly concentrate on the chemical reaction itself, can be highly challenging. These challenges stem from limitations in both available experimental structures (which are chemically altered to prevent self-cleavage) and the accuracy of force fields, together with the necessity for comprehensive sampling. We show that molecular dynamics simulations, combined with extensive conformational phase space exploration with Hamiltonian replica-exchange simulations, enable us to characterize the relevant conformational basins of the minimal hairpin ribozyme in the ligated state prior to self-cleavage. We find that what is usually considered a canonical reactive conformation with active site geometries and hydrogen-bond patterns that are optimal for the addition-elimination reaction with general acid/general base catalysis is metastable and only marginally populated. The thermodynamically stable conformation appears to be consistent with the expectations of a mechanism that does not require the direct participation of ribozyme residues in the reaction. While these observations may suffer from forcefield inaccuracies, all investigated forcefields lead to the same conclusions upon proper sampling, contrasting with previous investigations on shorter timescales suggesting that at least one reparametrization of the Amber99 forcefield allowed to stabilize aligned active site conformations. Our study demonstrates that identifying the most pertinent reactant state conformation holds equal importance alongside the accurate determination of the thermodynamics and kinetics of the chemical steps of the reaction.

Exploring Molecular Energy Landscapes by Coupling the DFTB Potential with a Tree-Based Stochastic Algorithm: Investigation of the Conformational Diversity of Phthalates.

Exploring the global energy landscape of relatively large molecules at the quantum level is a challenging problem. In this work, we report the coupling of a nonredundant conformational space exploration method, namely, the robotics-inspired iterative global exploration and local optimization (IGLOO) algorithm, with the quantum-chemical density functional tight binding (DFTB) potential. The application of this fast and efficient computational approach to three close-sized molecules of the phthalate family (DBP, BBP, and DEHP) showed that they present different conformational landscapes. These differences have been rationalized by making use of descriptors based on distances and dihedral angles. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties. A strong correlation has been evidenced between the two dihedral angles describing the side-chain orientation of the phthalate molecules. Our approach identifies low-energy minima without prior knowledge of the potential energy surface, paving the way for future investigations into transition paths and states.

AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics

Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.

An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package.

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

Toward a Molecular Mechanism of Complementary RNA Duplexes Denaturation.

RNA duplexes are relatively rare but play very important biological roles. As an end-product of template-based RNA replication, they also have key implications for hypothetical primitive forms of life. Unless they are specifically separated by enzymes, these duplexes denature upon a temperature increase. However, mechanistic and kinetic aspects of RNA (and DNA) duplex thermal denaturation remain unclear at the microscopic level. We propose an in silico strategy that probes the thermal denaturation of RNA duplexes and allows for an extensive conformational space exploration along a wide temperature range with atomistic precision. We show that this approach first accounts for the strong sequence and length dependence of the duplexes melting temperature, reproducing the trends seen in the experiments and predicted by nearest-neighbor models. The simulations are then instrumental at providing a molecular picture of the temperature-induced strand separation. The textbook canonical "all-or-nothing" two-state model, very much inspired by the protein folding mechanism, can be nuanced. We demonstrate that a temperature increase leads to significantly distorted but stable structures with extensive base-fraying at the extremities, and that the fully formed duplexes typically do not form around melting. The duplex separation therefore appears as much more gradual than commonly thought.

A spirocyclic backbone accesses new conformational space in an extended, dipole-stabilized foldamer

Most aromatic foldamers adopt uniform secondary structures, offering limited potential for the exploration of conformational space and the formation of tertiary structures. Here we report the incorporation of spiro bis-lactams to allow controlled rotation of the backbone of an iteratively synthesised foldamer. This enables precise control of foldamer shape along two orthogonal directions, likened to the aeronautical yaw and roll axes. XRD, NMR and computational data suggest that homo-oligomers adopt an extended right-handed helix with a pitch of over 30 Å, approximately that of B-DNA. Compatibility with extant foldamers to form hetero-oligomers is demonstrated, allowing greater structural complexity and function in future hybrid foldamer designs.

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. In order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

Structural Analysis of the Spike Protein of SARS-CoV-2 Variants and Other Betacoronaviruses Using Molecular Dynamics.

A structural analysis over various spike proteins from three highly pathogenic Betacoronavirus was done to understand their structural differences. The proteins were modeled using crystal structures from SARS-CoV, MERS-CoV, and other Betacoronavirus that infect bats and pangolins. The group was split in two sets; the first set corresponds to the non-mutated spike proteins, while the second set corresponds to mutated spike variants alpha, beta, gamma, delta, omicron and mu; five of them classified as variants of concern and the last one as variant of interest. A conformational space exploration was carried out for every protein by using molecular dynamic simulations. Root mean square fluctuations, principal component and cross-correlation analysis were carried out over the dynamics to analyze the flexibility and rigidity of every protein in comparison to the wild type Spike protein from the SARS-CoV-2. The obtained results indicate that the proteins, which are not spread among humans, have smooth movements compared to those of SARS-CoV-2 and its variants. In addition, a relationship between the speed of the virulence and the movement of the protein can explain the behavior of delta and omicron variants.

The impact of AlphaFold2 on experimental structure solution.

AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps.

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

Molecular Transfer Model for pH Effects on Intrinsically Disordered Proteins: Theory and Applications.

We present a theoretical method to study how changes in pH shape the heterogeneous conformational ensemble explored by intrinsically disordered proteins (IDPs). The theory is developed in the context of coarse-grained models, which enable a fast, accurate, and extensive exploration of conformational space at a given protonation state. In order to account for pH effects, we generalize the molecular transfer model (MTM), in which conformations are re-weighted using the transfer free energy, which is the free energy necessary for bringing to equilibrium in a new environment a "frozen" conformation of the system. Using the semi-grand ensemble, we derive an exact expression of the transfer free energy, which amounts to the appropriate summation over all the protonation states. Because the exact result is computationally too demanding to be useful for large polyelectrolytes or IDPs, we introduce a mean-field (MF) approximation of the transfer free energy. Using a lattice model, we compare the exact and MF results for the transfer free energy and a variety of observables associated with the model IDP. We find that the precise location of the charged groups (the sequence), and not merely the net charge, determines the structural properties. We demonstrate that some of the limitations previously noted for MF theory in the context of globular proteins are mitigated when disordered polymers are studied. The excellent agreement between the exact and MF results poises us to use the method presented here as a computational tool to study the properties of IDPs and other biological systems as a function of pH.

Computational characterization of the glutamate receptor antagonist perampanel and its close analogs: density functional exploration of conformational space and molecular docking study.

Perampanel approved by FDA in 2012 is a first-in-class antiepileptic drug which inhibits α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor currents. It is markedly more active than many of its close analogs, and the reasons for this activity difference are not quite clear. Recent crystallographic studies allowed the authorsto identify the location of its binding site. Unfortunately, the resolution is low, and the detailed description of perampanel binding mode is still in part speculative. Here we provide a detailed DFT-level conformational analysis of perampanel in a vacuum and in the solvents, mimicking the protein environment, followed by quantum theory of atoms in molecules (QTAIM), non-covalent interactions (NCI), and natural bond orbital (NBO) analyses. The findings indicate the electrostatic nature of the intramolecular interactions which contribute to energy differences of the conformations in a vacuum whereas the increase of dielectric constant leads to the energy equalization of conformations. Based on these results, the docking study was performed to investigate possible binding modes of perampanel and its close analogs in AMPA receptors. The influence of the pyridine nitrogen and cyano group position was explained based on the results of conformational analysis and molecular docking. These findings may contribute to the design of novel antiepileptic drugs and the development of novel approaches to treat neurodegenerative diseases and major depressive disorder.

EncoderMap: Dimensionality Reduction and Generation of Molecule Conformations.

Molecular simulation is one example where large amounts of high-dimensional (high-d) data are generated. To extract useful information, e.g., about relevant states and important conformational transitions, a form of dimensionality reduction is required. Dimensionality reduction algorithms differ in their ability to efficiently project large amounts of data to an informative low-dimensional (low-d) representation and the way the low and high-d representations are linked. We propose a dimensionality reduction algorithm called EncoderMap that is based on a neural network autoencoder in combination with a nonlinear distance metric. A key advantage of this method is that it establishes a functional link from the high-d to the low-d representation and vice versa. This allows us not only to efficiently project data points to the low-d representation but also to generate high-d representatives for any point in the low-d map. The potential of the algorithm is demonstrated for molecular simulation data of a small, highly flexible peptide as well as for folding simulations of the 20-residue Trp-cage protein. We demonstrate that the algorithm is able to efficiently project the ensemble of high-d structures to a low-d map where major states can be identified and important conformational transitions are revealed. We also show that molecular conformations can be generated for any point or any connecting line between points on the low-d map. This ability of inverse mapping from the low-d to the high-d representation is particularly relevant for the use in algorithms that enhance the exploration of conformational space or the sampling of transitions between conformational states.

Conformationally Programmable Chiral Foldamers with Compact and Extended Domains Controlled by Monomer Structure.

Foldamers are an important class of abiotic macromolecules, with potential therapeutic applications in the disruption of protein–protein interactions. The majority adopt a single conformational motif such as a helix. A class of foldamer is now introduced where the choice of heterocycle within each monomer, coupled with a strong conformation‐determining dipole repulsion effect, allows both helical and extended conformations to be selected. Combining these monomers into hetero‐oligomers enables highly controlled exploration of conformational space and projection of side‐chains along multiple vectors. The foldamers were rapidly constructed via an iterative deprotection‐cross‐coupling sequence, and their solid‐ and solution‐phase conformations were analysed by X‐ray crystallography and NMR and CD spectroscopy. These molecules may find applications in protein surface recognition where the interface does not involve canonical peptide secondary structures.

Conformationally Programmable Chiral Foldamers with Compact and Extended Domains Controlled by Monomer Structure

AbstractFoldamers are an important class of abiotic macromolecules, with potential therapeutic applications in the disruption of protein–protein interactions. The majority adopt a single conformational motif such as a helix. A class of foldamer is now introduced where the choice of heterocycle within each monomer, coupled with a strong conformation‐determining dipole repulsion effect, allows both helical and extended conformations to be selected. Combining these monomers into hetero‐oligomers enables highly controlled exploration of conformational space and projection of side‐chains along multiple vectors. The foldamers were rapidly constructed via an iterative deprotection‐cross‐coupling sequence, and their solid‐ and solution‐phase conformations were analysed by X‐ray crystallography and NMR and CD spectroscopy. These molecules may find applications in protein surface recognition where the interface does not involve canonical peptide secondary structures.

Role of cis-trans proline isomerization in the function of pathogenic enterobacterial Periplasmic Binding Proteins.

Periplasmic Binding Proteins (PBPs) trap nutrients for their internalization into bacteria by ABC transporters. Ligand binding triggers PBP closure by bringing its two domains together like a Venus flytrap. The atomic determinants that control PBP opening and closure for nutrient capture and release are not known, although it is proposed that opening and ligand release occur while in contact with the ABC transporter for concurrent substrate translocation. In this paper we evaluated the effect of the isomerization of a conserved proline, located near the binding site, on the propensity of PBPs to open and close. ArgT/LAO from Salmonella typhimurium and HisJ from Escherichia coli were studied through molecular mechanics at two different temperatures: 300 and 323 K. Eight microseconds were simulated per protein to analyze protein opening and closure in the absence of the ABC transporter. We show that when the studied proline is in trans, closed empty LAO and HisJ can open. In contrast, with the proline in cis, opening transitions were much less frequent and characterized by smaller changes. The proline in trans also renders the open trap prone to close over a ligand. Our data suggest that the isomerization of this conserved proline modulates the PBP mechanism: the proline in trans allows the exploration of conformational space to produce trap opening and closure, while in cis it restricts PBP movement and could limit ligand release until in productive contact with the ABC transporter. This is the first time that a proline isomerization has been related to the control of a large conformational change like the PBP flytrap mechanism.

Comparative atomic-scale hydration of the ceramide and phosphocholine headgroup in solution and bilayer environments.

Previous studies have used neutron diffraction to elucidate the hydration of the ceramide and the phosphatidylcholine headgroup in solution. These solution studies provide bond-length resolution information on the system, but are limited to liquid samples. The work presented here investigates how the hydration of ceramide and phosphatidylcholine headgroups in a solution compares with that found in a lipid bilayer. This work shows that the hydration patterns seen in the solution samples provide valuable insight into the preferential location of hydrating water molecules in the bilayer. There are certain subtle differences in the distribution, which result from a combination of the lipid conformation and the lipid-lipid interactions within the bilayer environment. The lipid-lipid interactions in the bilayer will be dependent on the composition of the bilayer, whereas the restricted exploration of conformational space is likely to be applicable in all membrane environments. The generalized description of hydration gathered from the neutron diffraction studies thus provides good initial estimation for the hydration pattern, but this can be further refined for specific systems.

Probing RNA Native Conformational Ensembles with Structural Constraints.

Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.

Cryoem-Guided Iterative Molecular Dynamics - Rosetta Protein Structure Refinement Protocol Improves Protein Model Quality

Having reliable computational tools for predicting high-resolution protein structures from medium resolution cryoEM density maps becomes more and more important in light of the recent rise in sub-nanometer resolution cryoEM density maps. While programs like EM-Fold, Rosetta or SSEBuilder can accurately predict protein models correct at the topology level - secondary structure elements will be in the correct positions of the density map -, the refinement of those models to atomic resolution remains a challenge in the field. Previously, we had presented an iterative Rosetta - Molecular Dynamics (MD) protocol, showing that iteration of Rosetta with an orthogonal sampling and scoring strategy can facilitate exploration of conformational space and build atomic resolution protein models. Here we present an improved iterative protein refinement protocol which is guided by the cryoEM density map at all stages and does not require previous knowledge of the protein structure. We ran short cryoEM-guided MDFF simulations on models created by de novo folding of large proteins into cryoEM density maps to enable sampling of conformational space not directly accessible to Rosetta all-atom refinement and thus provide an escape route from conformational traps. This cryoEM-guided, combined MD - Rosetta protein structure refinement protocol overcomes some of the sampling limitations that each individual method is subject to. Three of four benchmark proteins showed substantial improvement through three rounds of the iterative refinement protocol. Models with less than 1.5 A RMSD to the native structure were built for proteins with more than 150 residues. Molecular dynamics is most efficient in applying subtle but important rearrangements within secondary structure elements, and is thus highly complementary to the Rosetta refinement which focuses on side chains and loop regions.

Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.

As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules.