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.

Prebiotic chemical reactivity in solution with quantum accuracy and microsecond sampling using neural network potentials.

While RNA appears as a good candidate for the first autocatalytic systems preceding the emergence of modern life, the synthesis of RNA oligonucleotides without enzymes remains challenging. Because the uncatalyzed reaction is extremely slow, experimental studies bring limited and indirect information on the reaction mechanism, the nature of which remains debated. Here, we develop neural network potentials (NNPs) to study the phosphoester bond formation in water. While NNPs are becoming routinely applied to nonreactive systems or simple reactions, we demonstrate how they can systematically be trained to explore the reaction phase space for complex reactions involving several proton transfers and exchanges of heavy atoms. We then propagate at moderate computational cost hundreds of nanoseconds of a variety of enhanced sampling simulations with quantum accuracy in explicit solvent conditions. The thermodynamically preferred reaction pathway is a concerted, dissociative mechanism, with the transient formation of a metaphosphate transition state and direct participation of water solvent molecules that facilitate the exchange of protons through the nonbridging phosphate oxygens. Associative-dissociative pathways, characterized by a much tighter pentacoordinated phosphate, are higher in free energy. Our simulations also suggest that diprotonated phosphate, whose reactivity is never directly assessed in the experiments, is significantly less reactive than the monoprotonated species, suggesting that it is probably never the reactive species in normal pH conditions. These observations rationalize unexplained experimental results and the temperature dependence of the reaction rate, and they pave the way for the design of more efficient abiotic catalysts and activating groups.

Atomistic simulations of RNA duplex thermal denaturation: Sequence- and forcefield-dependence.

Double-stranded RNA is the end-product of template-based replication, and is also the functional state of some biological RNAs. Similarly to proteins and DNA, they can be denatured by temperature, with important physiological and technological implications. Here, we use an in silico strategy to probe the thermal denaturation of RNA duplexes. Following previous results that were obtained on a few different duplexes, and which nuanced the canonical 2-state picture of nucleic acid denaturation, we here specifically address three different aspects that greatly improve our description of the temperature-induced dsRNA separation. First, we investigate the effect of the spatial distribution of weak and strong base-pairs among the duplex sequence. We show that the deviations from the two-state dehybridization mechanism are more pronounced when a strong core is flanked with weak extremities, while duplexes with a weak core but strong extremities exhibit a two-state behavior, which can be explained by the key role played by base fraying. This was later verified by generating artificial hairpin or circular states containing one or two locked duplex extremities, which results in an important reinforcement of the entire HB structure of the duplex and higher melting temperatures. Finally, we demonstrate that our results are little sensitive to the employed combination of RNA and water forcefields. The trends in thermal stability among the different sequences as well as the observed unfolding mechanisms (and the deviations from a two-state scenario) remain the same regardless of the employed atomistic models. However, our study points to possible limitations of recent reparametrizations of the Amber RNA forcefield, which sometimes results in duplexes that readily denature under ambient conditions, in contradiction with available experimental results.

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.

Binding site plasticity regulation of the FimH catch-bond mechanism.

The bacterial fimbrial adhesin FimH is a remarkable and well-studied catch-bond protein found at the tip of E. coli type 1 pili, which allows pathogenic strains involved in urinary tract infections to bind high-mannose glycans exposed on human epithelia. The catch-bond behavior of FimH, where the strength of the interaction increases when a force is applied to separate the two partners, enables the bacteria to resist clearance when they are subjected to shear forces induced by urine flow. Two decades of experimental studies performed at the single-molecule level, as well as x-ray crystallography and modeling studies, have led to a consensus picture whereby force separates the binding domain from an inhibitor domain, effectively triggering an allosteric conformational change in the former. This force-induced allostery is thought to be responsible for an increased binding affinity at the core of the catch-bond mechanism. However, some important questions remain, the most challenging one being that the crystal structures corresponding to these two allosteric states show almost superimposable binding site geometries, which questions the molecular origin for the large difference in affinity. Using molecular dynamics with a combination of enhanced-sampling techniques, we demonstrate that the static picture provided by the crystal structures conceals a variety of binding site conformations that have a key impact on the apparent affinity. Crucially, the respective populations in each of these conformations are very different between the two allosteric states of the binding domain, which can then be related to experimental affinity measurements. We also evidence a previously unappreciated but important effect: in addition to the well-established role of the force as an allosteric regulator via domain separation, application of force tends to directly favor the high-affinity binding site conformations. We hypothesize that this additional "local" catch-bond effect could delay unbinding between the bacteria and the host cell before the "global" allosteric transition occurs, as well as stabilizing the complex even more once in the high-affinity allosteric state.

Explicit Models of Motion to Understand Protein Side-Chain Dynamics.

Nuclear magnetic relaxation is widely used to probe protein dynamics. For decades, most analyses of relaxation in proteins have relied successfully on the model-free approach, forgoing mechanistic descriptions of motion. Model-free types of correlation functions cannot describe a large carbon-13 relaxation dataset in protein side chains. Here, we use molecular dynamics simulations to design explicit models of motion and solve Fokker-Planck diffusion equations. These models of motion provide better agreement with relaxation data, mechanistic insight, and a direct link to configuration entropy.

Molecular Mechanisms of Phosphoester Bond Formation in Water Using Tight-Binding Ab Initio Molecular Dynamics.

Phosphate groups are ubiquitous in biomolecules and are usually incorporated through phosphoester bonds between alcohol groups and orthophosphate. The formation of this bond is exceptionally difficult, with associated barriers of 30-45 kcal/mol in the absence of catalysts. In abiotic conditions, polymerizing nucleic acids without enzymes remains very challenging and is still a partly unsolved problem that severely questions the RNA World hypothesis for the origins of life. Offering a solution to this problem would involve a detailed knowledge of the reaction energetics and mechanisms, yet these remain not fully understood at a molecular level, especially because of the very slow reaction rates that represent a significant challenge for the experiments. The number of involved reaction coordinates and the possible role of the solvent in assisting the reaction are challenging for computational studies. Here, we use extensive ab initio molecular dynamics simulations using semiempirical tight-binding methods and enhanced sampling to address these issues. We first show that the choice of the tight-binding method is greatly limited by the instability of the water liquid phase for most DFTB generations and parameter sets that are widely available. We then focus on a model reaction involving methanol and orthophosphate, for which the two protonation states (mono- and dianionic) that are dominant around neutral pH are considered. We compare different proton coordinates that enable (or not) the participation of solvent water molecules. Our simulations suggest that in all cases, a dissociative associative mechanism, with an intermediate metaphosphate, is favored. The main difference between the two phosphate species is that reaction with the monoanion is assisted by the substrate, while that with the dianion involves solvent water molecules. Our results are in agreement with early experimental measurements, but the reaction barriers are underestimated in our framework. We believe that our approach provides an interesting perspective on how to sample the reaction phase space efficiently, but it calls for future studies using more accurate descriptions of chemical reactivity.

Molecular interpretation of single-molecule force spectroscopy experiments with computational approaches.

Single molecule force-spectroscopy techniques have granted access to unprecedented molecular-scale details about biochemical and biological mechanisms. However, the interpretation of the experimental data is often challenging. Computational and simulation approaches (all-atom steered MD simulations in particular) are key to provide molecular details about the associated mechanisms, to help test different hypotheses and to predict experimental results. In this review, particular recent efforts directed towards the molecular interpretation of single-molecule force spectroscopy experiments on proteins and protein-related systems (often in close collaboration with experimental groups) will be presented. These results will be discussed in the broader context of the field, highlighting the recent achievements and the ongoing challenges for computational biophysicists and biochemists. In particular, I will focus on the input gained from molecular simulations approaches to rationalize the origin of the unfolded protein elasticity and the protein conformational behavior under force, to understand how force denaturation differs from chemical, thermal or shear unfolding, and to unravel the molecular details of unfolding events for a variety of systems. I will also discuss the use of models based on Langevin dynamics on a 1-D free-energy surface to understand the effect of protein segmentation on the work exerted by a force, or, at the other end of the spectrum of computational techniques, how quantum calculations can help to understand the reactivity of disulfide bridges exposed to force.

A Single-Molecule Strategy to Capture Non-native Intramolecular and Intermolecular Protein Disulfide Bridges.

Non-native disulfide bonds are dynamic covalent bridges that form post-translationally between two cysteines within the same protein (intramolecular) or with a neighboring protein (intermolecular), frequently due to changes in the cellular redox potential. The reversible formation of non-native disulfides is intimately linked to alterations in protein function; while they can provide a mechanism to protect against cysteine overoxidation, they are also involved in the early stages of protein multimerization, a hallmark of several protein aggregation diseases. Yet their identification using current protein chemistry technology remains challenging, mainly because of their fleeting reactivity. Here, we use single-molecule spectroscopy AFM and molecular dynamics simulations to capture both intra- and intermolecular disulfide bonds in γD-crystallin, a cysteine-rich, structural human lens protein involved in age-related eye cataracts. Our approach showcases the power of mechanical force as a conformational probe in dynamically evolving proteins and presents a platform to detect non-native disulfide bridges with single-molecule resolution.

Mass effects for thermodiffusion in dilute aqueous solutions.

Thermodiffusion is the phenomenon by which molecules in a mixture present concentration gradients in response to an imposed temperature gradient. Despite decades of investigations, this effect remains poorly understood at a molecular level. A common, phenomenological approach is to individuate the molecular factors that influence the Soret coefficient, the parameter that quantifies the resulting concentration-gradient. Experimental studies, often performed on organic mixtures, as well as simulations of model particle systems have evidenced that the difference in masses between the mixture components has an important effect on the amplitude of the Soret coefficient. Here, we use molecular dynamics simulations of a thermophoretic setting to investigate the mass dependence of the Soret coefficient in dilute aqueous solutions. An advantage of simulation approaches is that they are not limited in the range of explored molecular masses, which is often limited to isotopic substitutions in the experiments. Our simulations reveal that the mass dependence of the Soret coefficient in these solutions is in agreement with previous experimental and simulation work on molecular-size systems. In particular, it is sensitive to the relative mass difference between the solute and the solvent, but not to their absolute mass. Adjusting the mass of the solvent and of the solute can turn a thermophobic solution into a thermophilic one, where solute accumulation is reversed. This demonstrates that the mass effect can indeed compensate for the other contributions to the Soret coefficient. Finally, we find that changing the molecular moments of inertia has a much more limited impact as compared to a change in the total molecular mass.

Recent Advances and Emerging Challenges in the Molecular Modeling of Mechanobiological Processes.

Many biological processes result from the effect of mechanical forces on macromolecular structures and on their interactions. In particular, the cell shape, motion, and differentiation directly depend on mechanical stimuli from the extracellular matrix or from neighboring cells. The development of experimental techniques that can measure and characterize the tiny forces acting at the cellular scale and down to the single-molecule, biomolecular level has enabled access to unprecedented details about the involved mechanisms. However, because the experimental observables often do not provide a direct atomistic picture of the corresponding phenomena, particle-based simulations performed at various scales are instrumental in complementing these experiments and in providing a molecular interpretation. Here, we will review the recent key achievements in the field, and we will highlight and discuss the many technical challenges these simulations are facing, as well as suggest future directions for improvement.

Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods.

While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism.

In silico all-atom approach to thermodiffusion in dilute aqueous solutions.

Thermodiffusion (or thermophoresis) is the phenomenon by which the spatial distributions of constituents of liquid or gas phases become inhomogeneous in response to a temperature gradient. It has been evidenced in a variety of systems and has many practical applications as well as implications in the context of the origins of life. A complete molecular picture of thermophoresis is still missing, and phenomenological approaches are often employed to account for the experimental observations. In particular, the amplitude of the resulting concentration-gradients (quantified by the Soret coefficient) depends on many factors that are not straightforwardly rationalized. All-atom molecular dynamics simulations appear as an exquisite tool to shed light on the molecular origins for this phenomenon in molecular systems, but the practical implementation of thermophoretic settings in silico poses significant challenges. Here, we propose a robust approach to tackle thermophoresis in dilute realistic solutions at the molecular level. We rely on a recent enhanced heat-exchange algorithm to generate temperature-gradients. We carefully assess the convergence of thermophoretic simulations in dilute aqueous solutions. We show that simulations typically need to be propagated on long timescales (hundreds of nanoseconds). We find that the magnitude of the temperature gradient and the box sizes have little effect on the measured Soret coefficients. Practical guidelines are derived from such observations. Provided with this reliable setup, we discuss the results of thermophoretic simulations on several examples of molecular, neutral solutes, which we find in very good agreement with experimental measurements regarding the concentration-, mass-, and temperature-dependence of the Soret coefficient.

Specific Interactions and Environment Flexibility Tune Protein Stability under Extreme Crowding.

Macromolecular crowding influences protein mobility and stability in vivo. A precise description of the crowding effect on protein thermal stability requires the estimate of the combined effects of excluded volume, specific protein-environment interactions, as well as the thermal response of the crowders. Here, we explore an ideal model system, the lysozyme protein in powder state, to dissect the factors controlling the melting of the protein under extreme crowding. By deploying state-of-the art molecular simulations, supported by calorimetric experiments, we assess the role of the environment flexibility and of intermolecular electrostatic interactions. In particular, we show that the temperature-dependent flexibility of the macromolecular crowders, along with specific interactions, significantly alleviates the stabilizing contributions of the static volume effect.

Differences in thermal structural changes and melting between mesophilic and thermophilic dihydrofolate reductase enzymes.

A key aspect of life's evolution on Earth is the adaptation of proteins to be stable and work in a very wide range of temperature conditions. A detailed understanding of the associated molecular mechanisms would also help to design enzymes optimized for biotechnological processes. Despite important advances, a comprehensive picture of how thermophilic enzymes succeed in functioning under extreme temperatures remains incomplete. Here, we examine the temperature dependence of stability and of flexibility in the mesophilic monomeric Escherichia coli (Ec) and thermophilic dimeric Thermotoga maritima (Tm) homologs of the paradigm dihydrofolate reductase (DHFR) enzyme. We use all-atom molecular dynamics simulations and a replica-exchange scheme that allows to enhance the conformational sampling while providing at the same time a detailed understanding of the enzymes' behavior at increasing temperatures. We show that this approach reproduces the stability shift between the two homologs, and provides a molecular description of the denaturation mechanism by identifying the sequence of secondary structure elements melting as temperature increases, which is not straightforwardly obtained in the experiments. By repeating our approach on the hypothetical TmDHFR monomer, we further determine the respective effects of sequence and oligomerization in the exceptional stability of TmDFHR. We show that the intuitive expectation that protein flexibility and thermal stability are correlated is not verified. Finally, our simulations reveal that significant conformational fluctuations already take place much below the melting temperature. While the difference between the TmDHFR and EcDHFR catalytic activities is often interpreted via a simplified two-state picture involving the open and closed conformations of the key M20 loop, our simulations suggest that the two homologs' markedly different activity temperature dependences are caused by changes in the ligand-cofactor distance distributions in response to these conformational changes.

Thermal Adaptation of Enzymes: Impacts of Conformational Shifts on Catalytic Activation Energy and Optimum Temperature.

Thermal adaptation of enzymes is essential for both living organism development in extreme conditions and efficient biocatalytic applications. However, the molecular mechanisms leading to a shift in catalytic activity optimum temperatures remain unclear, and there is increasing experimental evidence that thermal adaptation involves complex changes in both structural and reactive properties. Here, a combination of enhanced protein conformational sampling with an explicit chemical reaction description was applied to mesophilic and thermophilic homologues of the dihydrofolate reductase enzyme, and a quantitative description of the stability and catalytic activity shifts between homologues was obtained. The key role played by temperature-induced shifts in protein conformational distributions is revealed. In contrast with pictures focusing on protein flexibility and dynamics, it is shown that while the homologues' reaction free energies are similar, the striking discrepancy between their activation energies is caused by their different conformational changes with temperature. An analytic model is proposed that combines catalytic activity and structural stability, and which quantitatively predicts the shift in homologue optimum temperatures. It is shown that this general model provides a molecular explanation of changes in optimum temperatures for several other enzymes.

Water dynamics at electrified graphene interfaces: a jump model perspective.

The reorientation dynamics of water at electrified graphene interfaces was recently shown [J. Phys. Chem. Lett., 2020, 11, 624-631] to exhibit a surprising and strongly asymmetric behavior: positive electrode potentials slow down interfacial water reorientation, while for increasingly negative potentials water dynamics first accelerates before reaching an extremum and then being retarded for larger potentials. Here we use classical molecular dynamics simulations to determine the molecular mechanisms governing water dynamics at electrified interfaces. We show that changes in water reorientation dynamics with electrode potential arise from the electrified interfaces' impacts on water hydrogen-bond jump exchanges, and can be quantitatively described by the extended jump model. Finally, our simulations indicate that no significant dynamical heterogeneity occurs within the water interfacial layer next to the weakly interacting graphene electrode.

Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding.

7SK RNA, as part of the 7SK ribonucleoprotein complex, is crucial to the regulation of transcription by RNA-polymerase II, via its interaction with the positive transcription elongation factor P-TEFb. The interaction is induced by binding of the protein HEXIM to the 5' hairpin (HP1) of 7SK RNA. Four distinct structural models have been obtained experimentally for HP1. Here, we employ computational methods to investigate the relative stability of these structures, transitions between them, and the effects of mutations on the observed structural ensembles. We further analyse the results with respect to mutational binding assays, and hypothesize a mechanism for HEXIM binding. Our results indicate that the dominant structure in the wild type exhibits a triplet involving the unpaired nucleotide U40 and the base pair A43-U66 in the GAUC/GAUC repeat. This conformation leads to an open major groove with enough potential binding sites for peptide recognition. Sequence mutations of the RNA change the relative stability of the different structural ensembles. Binding affinity is consequently lost if these changes alter the dominant structure.

Effect of Ions on Water Dynamics in Dilute and Concentrated Aqueous Salt Solutions.

Aqueous ionic solutions are ubiquitous in chemistry and in biology. Experiments show that ions affect water dynamics, but a full understanding of several questions remains needed: why some salts accelerate water dynamics while others slow it down, why the effect of a given salt can be concentration-dependent, whether the effect of ions is rather local or more global. Numerical simulations are particularly suited to disentangle these different effects, but current force fields suffer from limitations and often lead to a poor description of dynamics in several aqueous salt solutions. Here, we develop an improved classical force field for the description of alkali halides that yields dynamics in excellent agreement with experimental measurements for water reorientational and translational dynamics. These simulations are analyzed with an extended jump model, which allows to compare the effects of ions on local hydrogen-bond exchange dynamics and on more global properties like viscosity. Our results unambiguously show that the ion-induced changes in water dynamics are usually mostly due to a local effect on the hydrogen-bond exchange dynamics; in contrast, the change in viscosity leads to a smaller effect, which governs the retardation only for a minority of salts and at high concentrations. We finally show how the respective importance of these two effects can be directly determined from experimental measurements alone, thus providing guidelines for the selection of an electrolyte with specific dynamical properties.

Three Weaknesses for Three Perturbations: Comparing Protein Unfolding Under Shear, Force, and Thermal Stresses.

The perturbation of a protein conformation by a physiological fluid flow is crucial in various biological processes including blood clotting and bacterial adhesion to human tissues. Investigating such mechanisms by computer simulations is thus of great interest, but it requires development of ad hoc strategies to mimic the complex hydrodynamic interactions acting on the protein from the surrounding flow. In this study, we apply the Lattice Boltzmann Molecular Dynamics (LBMD) technique built on the implicit solvent coarse-grained model for protein Optimized Potential for Efficient peptide structure Prediction (OPEP) and a mesoscopic representation of the fluid solvent, to simulate the unfolding of a small globular cold-shock protein in shear flow and to compare it to the unfolding mechanisms caused either by mechanical or thermal perturbations. We show that each perturbation probes a specific weakness of the protein and causes the disruption of the native fold along different unfolding pathways. Notably, the shear flow and the thermal unfolding exhibit very similar pathways, while because of the directionality of the perturbation, the unfolding under force is quite different. For force and thermal disruption of the native state, the coarse-grained simulations are compared to all-atom simulations in explicit solvent, showing an excellent agreement in the explored unfolding mechanisms. These findings encourage the use of LBMD based on the OPEP model to investigate how a flow can affect the function of larger proteins, for example, in catch-bond systems.