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Interstellar and cometary ices play an important role in the formation of planetary systems around young stars. Their main constituent is amorphous solid water (ASW). Although ASW is widely studied, vibrational energy dissipation and structural changes due to vibrational excitation are less well understood. The hydrogen-bonding network is likely a crucial component in this. Here we present experimental results on hydrogen-bonding changes in ASW induced by the intense, nearly monochromatic mid-IR free-electron laser (FEL) radiation of the FELIX-2 beamline at the HFML-FELIX facility at the Radboud University in Nijmegen, the Netherlands. Structural changes in ASW are monitored by reflection-absorption infrared spectroscopy and depend on the irradiation history of the ice. The experiments show that FEL irradiation can induce changes in the local neighborhood of the excited molecules due to energy transfer. Molecular Dynamics simulations confirm this picture: vibrationally excited molecules can reorient for a more optimal tetrahedral surrounding without breaking existing hydrogen bonds. The vibrational energy can transfer through the hydrogen-bonding network to water molecules that have the same vibrational frequency. We hence expect a reduced energy dissipation in amorphous material with respect to crystalline material due to the inhomogeneity in vibrational frequencies as well as the presence of specific hydrogen-bonding defect sites which can also hamper the energy transfer., 34 page, 9 figures

Flexibility is essential for many proteins to function, but can be difficult to characterize. Experiments lack resolution in space and time, while the time scales involved are prohibitively long for straightforward molecular dynamics simulations. In this work, we present a multiple state transition path sampling simulation study of a protein that has been notoriously difficult to characterize in its active state. The GTPase enzyme KRas is a signal transduction protein in pathways for cell differentiation, growth, and division. When active, KRas tightly binds guanosine triphosphate (GTP) in a rigid state. The protein-GTP complex can also visit more flexible states, in which it is not active. KRas mutations can affect the conversion between these rigid and flexible states, thus prolonging the activation of signal transduction pathways, which may result in tumor formation. In this work, we apply path sampling simulations to investigate the dynamic behavior of KRas-4B (wild type, WT) and the oncogenic mutant Q61L (Q61L). Our results show that KRas visits several conformational states, which are the same for WT and Q61L. The multiple state transition path sampling (MSTPS) method samples transitions between the different states in a single calculation. Tracking which transitions occur shows large differences between WT and Q61L. The MSTPS results further reveal that for Q61L, a route to a more flexible state is inaccessible, thus shifting the equilibrium to more rigid states. The methodology presented here enables a detailed characterization of protein flexibility on time scales not accessible with brute-force molecular dynamics simulations.

The dynamics and spectroscopy of N-methyl-acetamide (NMA) and trialanine in solution are characterized from molecular dynamics simulations using different energy functions, including a conventional point charge (PC)-based force field, one based on a multipolar (MTP) representation of the electrostatics, and a semiempirical DFT method. For the 1D infrared spectra, the frequency splitting between the two amide-I groups is 10 cm–1 from the PC, 13 cm–1 from the MTP, and 47 cm–1 from self-consistent charge density functional tight-binding (SCC-DFTB) simulations, compared with 25 cm–1 from experiment. The frequency trajectory required for the frequency fluctuation correlation function (FFCF) is determined from individual normal mode (INM) and full normal mode (FNM) analyses of the amide-I vibrations. The spectroscopy, time-zero magnitude of the FFCF C(t = 0), and the static component Δ02 from simulations using MTP and analysis based on FNM are all consistent with experiments for (Ala)3. Contrary to this, for the analysis excluding mode–mode coupling (INM), the FFCF decays to zero too rapidly and for simulations with a PC-based force field, the Δ02 is too small by a factor of two compared with experiments. Simulations with SCC-DFTB agree better with experiment for these observables than those from PC-based simulations. The conformational ensemble sampled from simulations using PCs is consistent with the literature (including PII, β, αR, and αL), whereas that covered by the MTP-based simulations is dominated by PII with some contributions from β and αR. This agrees with and confirms recently reported Bayesian-refined populations based on 1D infrared experiments. FNM analysis together with a MTP representation provides a meaningful model to correctly describe the dynamics of hydrated trialanine.

Photocatalytic hydrogen atom transfer (HAT) processes have been the object of numerous studies showcasing the potential of the homogeneous photocatalyst tetrabutylammonium decatungstate (TBADT) for the functionalization of C(sp3)–H bonds. However, to translate these studies into large-scale industrial processes, careful considerations of catalyst loading, cost, and removal are required. This work presents organic solvent nanofiltration (OSN) as an answer to reduce TBADT consumption, increase its turnover number and lower its concentration in the product solution, thus enabling large-scale photocatalytic HAT-based transformations. The operating parameters for a suitable membrane for TBADT recovery in acetonitrile were optimized. Continuous photocatalytic C(sp3)-H alkylation and amination reactions were carried out with in-line TBADT recovery via two OSN steps. Promisingly, the observed product yields for the reactions with in-line catalyst recycling are comparable to those of reactions performed with pristine TBADT, therefore highlighting that not only catalyst recovery (>99%, TON > 8400) is a possibility, but also that it does not happen at the expense of reaction performance.

The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not captured. In general, the quality of coarse-grained models is evaluated at the coarse-grained resolution, hindering the detection of important discrepancies between the all-atom and coarse-grained ensembles. In this work, the quality of different coarse-grained models is assessed at the atomistic resolution deploying reverse-mapping strategies. In particular, coarse-grained structures for Tris-Meta-Biphenyl-Triazine are reverse-mapped from two different sources: 1) All-atom configurations projected onto the coarse-grained resolution and 2) snapshots obtained by molecular dynamics simulations based on the coarse-grained force fields. To assess the quality of the coarse-grained models, reverse-mapped structures of both sources are compared revealing significant discrepancies between the all-atom and the coarse-grained ensembles. Specifically, the reintroduced details enable force computations based on the all-atom force field that yield a clear ranking for the quality of the different coarse-grained models.

Hoogsteen (HG) base pairing is characterized by a 180° rotation of the purine base with respect to the Watson-Crick-Franklin (WCF) motif. Recently, it has been found that both conformations coexist in a dynamical equilibrium and that several biological functions require HG pairs. This relevance has motivated experimental and computational investigations of the base-pairing transition. However, a systematic simulation of sequence variations has remained out of reach. Here, we employ advanced path-based methods to perform unprecedented free-energy calculations. Our methodology enables us to study the different mechanisms of purine rotation, either remaining inside or after flipping outside of the double helix. We study seven different sequences, which are neighbor variations of a well-studied A⋅T pair in A6-DNA. We observe the known effect of A⋅T steps favoring HG stability, and find evidence of triple-hydrogen-bonded neighbors hindering the inside transition. More importantly, we identify a dominant factor: the direction of the A rotation, with the 6-ring pointing either towards the longer or shorter segment of the chain, respectively relating to a lower or higher barrier. This highlights the role of DNA’s relative flexibility as a modulator of the WCF/HG dynamic equilibrium. Additionally, we provide a robust methodology for future HG proclivity studies.

Conspectus Dark molecular clouds have low temperatures of approximately 10 K and experience very little UV irradiation. These clouds are the birthplace of new stars and consist of gas and dust particles. The latter can act as a meeting place to facilitate surface chemistry to form saturated molecules such as formaldehyde, methyl formate, and dimethyl ether. These complex organic molecules or COMs become encapsulated in the ice that forms on the dust grains, and these ices are the precursor for cometary ices and other icy bodies. They likely played a role in bringing material to the early earth. Although these COMs are likely formed on the surfaces of dust grains, several of them have been detected in the gas phase. This means that they have desorbed from the grain under these cold, dark conditions where thermal desorption and photodesorption are negligible. It has been speculated that reactive, or chemical, desorption is responsible for the high gas-phase abundance. After a surface reaction, its products might be vibrationally, translationally, and/or rotationally excited. Dissipation of the excess energy to translational energy can briefly increase the desorption rate, leading to chemical desorption. Astrochemical modellers have added terms to their rate equations to account for this effect. These terms, however, have had little experimental or theoretical verification. In this Account, we use classical molecular dynamics (MD) simulations to give adsorbed molecules a fixed amount of energy as a proxy for excess energy and to record whether this leads to desorption. The excitation energy can be varied freely while keeping all other variables constant. This allows for the study of trends rather than being limited to a single reaction and a single system. The focus is on the dependence of the chemical desorption on the excitation energy, excitation type, and binding energy. Rotational and vibrational excitation was explicitly taken into account. An analytical expression for the chemical desorption probability was obtained in this way. It depends on the binding energy and reaction enthalpy. This expression was then implemented in a gas–grain astrochemical code to simulate the chemical evolution of a dark molecular cloud, and the results were compared against observational abundances of COMs in three different molecular clouds. The results with our new expression based on the MD simulations show good agreement for all species except H2CO, which has both gas-phase and surface-formation routes. This is a significant improvement over models without chemical desorption or with other expressions for chemical desorption, as frequently used by other authors. It is encouraging to see that a general description with a firmer theoretical basis leads to a significant improvement. Understanding chemical desorption can help to explain the unexpectedly high gas-phase abundance of some COMs, and chemical desorption also provides a link between the gas phase and the ice mantle, and its understanding might help in creating a diagnostic tool to learn more about the ice composition.

Carbon dioxide hydrate is a solid built from hydrogen-bond stabilized water cages that encapsulate individual CO2 molecules. As potential candidates for reducing greenhouse gases, hydrates have attracted attention from both the industry and scientific community. Under high pressure and low temperature, hydrates are formed spontaneously from a mixture of CO2 and water via nucleation and growth. Yet, for moderate undercooling, i.e., moderate supersaturation, studying hydrate formation with molecular simulations is very challenging due to the high nucleation barriers involved. We investigate the homogeneous nucleation mechanism of CO2 hydrate as a function of temperature using transition path sampling (TPS), which generates ensembles of unbiased dynamical trajectories across the high barrier between the liquid and solid states. The resulting path ensembles reveal that at high driving force (low temperature), amorphous structures are predominantly formed, with 4151062 cages being the most abundant. With increasing temperature, the nucleation mechanism changes, and 51262 becomes the most abundant cage type, giving rise to the crystalline sI structure. Reaction coordinate analysis can reveal the most important collective variable involved in the mechanism. With increasing temperature, we observe a shift from a single feature (size of the nucleus) to a 2-dimensional (size and cage type) variable as the salient ingredient of the reaction coordinate, and then back to only the nucleus size. This finding is in line with the underlying shift from an amorphous to a crystalline nucleation channel. Modeling such complex phase transformations using transition path sampling gives unbiased insight into the molecular mechanisms toward different polymorphs, and how these are determined by thermodynamics and kinetics. This study will be beneficial for researchers aiming to produce such hydrates with different polymorphic forms.

The crystallization of methane hydrates via homogeneous nucleation under natural, moderate conditions is of both industrial and scientific relevance, yet still poorly understood. Predicting the nucleation rates at such conditions is notoriously difficult due to high nucleation barriers, and requires, besides an accurate molecular model, enhanced sampling. Here, we apply the transition interface sampling technique, which efficiently computes the exact rate of nucleation by generating ensembles of unbiased dynamical trajectories crossing predefined interfaces located between the stable states. Using an accurate atomistic force field and focusing on specific conditions of 280 K and 500 bar, we compute for nucleation directly into the sI crystal phase at a rate of similar to 10-17 nuclei per nanosecond per simulation volume or similar to 102 nuclei per second per cm3, in agreement with consensus estimates for nearby conditions. As this is most likely fortuitous, we discuss the causes of the large differences between our results and previous simulation studies. Our work shows that it is now possible to compute rates for methane hydrates at moderate supersaturation, without relying on any assumptions other than the force field.

Cell membranes mainly consist of lipid bilayers with an actively regulated composition. The underlying processes are still poorly understood, in particular, how the hundreds of components are controlled. Cholesterol has been found to correlate with phospholipid saturation for reasons that remain unclear. To better understand the link between cell membrane regulation and chemical composition, we establish a computational framework based on chemical reaction networks, resulting in multiple semigrand canonical ensembles. By running computer simulations, we show that regulating the chemical potential of lipid species is sufficient to reproduce the experimentally observed increase in acyl tail saturation with added cholesterol. Our model proposes a different picture of lipid regulation in which components can be regulated passively instead of actively. In this picture, phospholipid acyl tail composition naturally adapts to added molecules such as cholesterol or proteins. A comparison between regulated membranes with commonly studied ternary model membranes shows stark differences: for instance, correlation lengths and viscosities observed are independent of lipid chemical affinity.

Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.

Di-iron hydrogenases are a class of enzymes that are capable of reducing protons to form molecular hydrogen with high efficiency. In addition to the catalytic site, these enzymes have evolved dedicated pathways to transport protons and electrons to the reaction center. Here, we present a detailed study of the most likely proton transfer pathway in such an enzyme using QM/MM molecular dynamics simulations. The protons are transported through a channel lined out from the protein exterior to the di-iron active site, by a series of hydrogen-bonded, weakly acidic or basic, amino acids and two incorporated water molecules. The channel shows remarkable flexibility, which is an essential feature to quickly reset the hydrogen-bond direction in the channel after each proton passing. Proton transport takes place via a “hole” mechanism, rather than an excess proton mechanism, the free energy landscape of which is remarkably flat, with a highest transition state barrier of only 5 kcal/mol. These results confirm our previous assumptions that proton transport is not rate limiting in the H2 formation activity and that cysteine C299 may be considered protonated at physiological pH conditions. Detailed understanding of this proton transport may aid in the ongoing attempts to design artificial biomimetic hydrogenases for hydrogen fuel production.

The primary structures of peptides, originating from food proteins, affect their taste. Connecting primary structure to taste, however, is difficult because the size of the peptide sequence space increases exponentially with increasing peptide length, while experimentally-labeled data on peptides' tastes remain scarce. We propose a method that coarse-grains the sequence space to reduce its size and systematically identifies the most common coarse-grained residue patterns found in known bitter and umami peptides. We select the optimal patterns by performing extensive out-of-sample tests. The optimal patterns better represent the bitter and umami peptides when compared against baseline peptides, bitter peptides with all hydrophobic residues and umami peptides with all negatively charged residues, and peptides with randomly-chosen residues. Our method complements quantitative structure--activity relationship methods by offering generic, coarse-grained bitter and umami residue patterns that can aid in locating short bitter or umami segments in a protein and in designing new umami peptides., Comment: 11 pages, 5 figures

Funder: Federation of European Biochemical Societies; doi: http://dx.doi.org/10.13039/100012623, The maximum caliber approach implements the maximum entropy principle for trajectories by maximizing a path entropy under external constraints. The maximum caliber approach can be applied to a diverse set of equilibrium and non-equilibrium problems concerning the properties of trajectories connecting different states of a system. In this review, we recapitulate the basic concepts of the maximum entropy principle and of its maximum caliber implementation for path ensembles, and review recent applications of this approach. In particular, we describe how we recently used this approach to introduce a framework, called here the continuum path ensemble maximum caliber (CoPE-MaxCal) method, to impose kinetic constraints in molecular simulations, for instance to include experimental information about transition rates. Such incorporation of dynamical information can ameliorate inaccuracies of empirical force fields, and lead to improved mechanistic insights. We conclude by offering an outlook for future research. Graphic Abstract

The viscoelastic properties of filaments and biopolymers play a crucial role in soft and biological materials from biopolymer networks to novel synthetic metamaterials. Colloidal particles with specific valency allow mimicking polymers and more complex molecular structures at the colloidal scale, offering direct observation of their internal degrees of freedom. Here, we elucidate the time-dependent viscoelastic response in the bending of isolated semi-flexible colloidal polymers, assembled from dipatch colloidal particles by reversible critical Casimir forces. By tuning the patch-patch interaction strength, we adjust the polymers' viscoelastic properties, and follow spontaneous bending modes and their relaxation directly on the particle level. We find that the elastic response is well described by that of a semiflexible rod with persistence length of order 1000 mu m, tunable by the critical Casimir interaction strength. We identify the viscous relaxation on longer timescales to be due to internal friction, leading to a wavelength-independent relaxation time similar to single biopolymers, but in the colloidal case arising from the contact mechanics of the bonded patches. These tunable mechanical properties of assembled colloidal filaments open the door to ``colloidal architectures'', rationally designed (network) structures with desired topology and mechanical properties.

We investigate the orientational dynamics of water molecules solvating phenolate ions using ultrafast vibrational spectroscopy and density functional theory-based molecular dynamics simulations. To assess the roles of the hydrophobic and hydrophilic parts of the anion, we also perform experiments and simulations on solutions of phenol. The experiments show that phenolate immobilizes (τor > 10 ps) 6.2 ± 0.5 water molecules beyond the first solvation shell of its oxygen atom, whereas phenol immobilizes only ∼2 water molecules, including the water molecules in its first solvation shell. The simulations reproduce the experiments very well, and show that phenolate causes a local ordering of the hydrogen-bond structure that extends beyond the first solvation shell, thus explaining the experimental observations. The comparison with phenol solution shows that the solvation interaction of phenolate beyond its first solvation shell is due to the high charge density of its negatively charged oxygen atom.

DNA predominantly contains Watson–Crick (WC) base pairs, but a non-negligible fraction of base pairs are in the Hoogsteen (HG) hydrogen bonding motif at any time. In HG, the purine is rotated ∼180° relative to the WC motif. The transitions between WC and HG may play a role in recognition and replication, but are difficult to investigate experimentally because they occur quickly, but only rarely. To gain insight into the mechanisms for this process, we performed transition path sampling simulations on a model nucleotide sequence in which an AT pair changes from WC to HG. This transition can occur in two ways, both starting with loss of hydrogen bonds in the base pair, followed by rotation around the glycosidic bond. In one route the adenine base converts from WC to HG geometry while remaining entirely within the double helix. The other route involves the adenine leaving the confines of the double helix and interacting with water. Our results indicate that this outside route is more probable. We used transition interface sampling to compute rate constants and relative free energies for the transitions between WC and HG. Our results agree with experiments, and provide highly detailed insights into the mechanisms of this important process.

Computational chemistry uses computer simulation to assist in solving chemical problems. Typical workflows of computational chemists include the use of dozens of utilities. 3D modeling programs are powerful tools that help researchers visualize their work and create illustrative graphics. In this review, we describe and highlight tools and visualization packages that are commonly used in the field of (bio-)chemistry and material science.

Synthetic colloidal patchy particles immersed in a binary liquid mixture can self-assemble via critical Casimir interactions into various superstructures, such as chains and networks. Up to now, there are no quantitatively accurate potential models that can simulate and predict this experimentally observed behavior precisely. Here, we develop a protocol to establish such a model based on a combination of theoretical Casimir potentials and angular switching functions. Using Monte Carlo simulations, we optimize several material-specific parameters in the model to match the experimental chain length distribution and persistence length. Our approach gives a systematic way to obtain accurate potentials for critical Casimir induced patchy particle interactions and can be used in large-scale simulations.

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 million small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role, but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered (Ld) and liquid-ordered (Lo) permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry., Comment: 11 pages, 7 figures

Limited-valency colloidal particles can self-assemble into polymeric structures analogous to molecules. While their structural equilibrium properties have attracted wide attention, insight into their dynamics has proven challenging. Here, we investigate the polymerization dynamics of semiflexible polymers in 2D by direct observation of assembling divalent particles, bonded by critical Casimir forces. The reversible critical Casimir force creates living polymerization conditions with tunable chain dissociation, association, and bending rigidity. We find that unlike dilute polymers that show exponential size distributions in excellent agreement with Flory theory, concentrated samples exhibit arrest of rotational and translational diffusion due to a continuous isotropic-to-nematic transition in 2D, slowing down the growth kinetics. These effects are circumvented by the addition of higher-valency particles, cross linking the polymers into networks. Our results connecting polymer flexibility, polymer interactions, and the peculiar isotropic-nematic transition in 2D offer insight into the polymerization processes of synthetic two-dimensional polymers and biopolymers at membranes and interfaces. © 2021 American Physical Society.

Colloids have a rich history of being used as ‘big atoms’ mimicking real atoms to study crystallization, gelation and the glass transition of condensed matter. Emulating the dynamics of molecules, however, has remained elusive. Recent advances in colloid chemistry allow patchy particles to be synthesized with accurate control over shape, functionality and coordination number. Here, we show that colloidal alkanes, specifically colloidal cyclopentane, assembled from tetrameric patchy particles by critical Casimir forces undergo the same chemical transformations as their atomic counterparts, allowing their dynamics to be studied in real time. We directly observe transitions between chair and twist conformations in colloidal cyclopentane, and we elucidate the interplay of bond bending strain and entropy in the molecular transition states and ring-opening reactions. These results open the door to investigate complex molecular kinetics and molecular reactions in the high-temperature classical limit, in which the colloidal analogue becomes a good model., Anisotropically functionalized colloids can serve as meso-atoms for self-assembly of new materials. Swinkels et al. extend the analogy with atomic scale counterparts and show how familiar ring opening and puckering emerges in alkane-like assemblies of tetraedric patchy particles.

Carbon dioxide and water can form solid clathrate structures in which water cages encapsulate the gas molecules. Such hydrates have sparked much interest due to their possible application in CO2 sequestration. How the solid structure forms exactly from the liquid phase via a homogenous nucleation process is still poorly understood. This nucleation event is rare on the molecular timescale even under moderate undercooling or supersaturation conditions because of the large free energy barrier toward crystallization, rendering a brute force simulation of hydrate nucleation unfeasible for moderate undercooling or supersaturation. Here, we perform transition interface sampling simulations to quantify the homogenous nucleation rate for CO2 hydrate formation using accurate atomistic force fields at 500 bars for three different temperatures between 260 and 273 K. Collecting more than 100 000 pathways comprising roughly two milliseconds of simulation time, we computed a nucleation rate in the amorphous phase of ∼1021 nuclei s−1 cm−3 for a temperature of 260 K and a rate of ∼1012 nuclei s−1 cm−3 for a temperature of 265 K. For a temperature of 273 K, we find that the hydrate forms an sI crystalline phase with a rate of order of ∼101 nuclei s−1 cm−3. We compare these rates to classical nucleation theory estimates as well as experiments, and to nucleation rate estimates for methane hydrates and discuss possible causes of the observed differences. Our findings shed light on the kinetics of this important clathrate and should assist in future hydrate formation investigation.

Computer simulations generate microscopic trajectories of complex systems at a single thermodynamic state point. We recently introduced a Maximum Caliber (MaxCal) approach for dynamical reweighting. Our approach mapped these trajectories to a Markovian description on the configurational coordinates, and reweighted path probabilities as a function of external forces. Trajectory probabilities can be dynamically reweighted both from and to equilibrium or non-equilibrium steady states. As the system's dimensionality increases, an exhaustive description of the microtrajectories becomes prohibitive--even with a Markovian assumption. Instead we reduce the dimensionality of the configurational space to collective variables (CVs). Going from configurational to CV space, we define local entropy productions derived from configurationally averaged mean forces. The entropy production is shown to be a suitable constraint on MaxCal for non-equilibrium steady states expressed as a function of CVs. We test the reweighting procedure on two systems: a particle subject to a two-dimensional potential and a coarse-grained peptide. Our CV-based MaxCal approach expands dynamical reweighting to larger systems, for both static and dynamical properties, and across a large range of driving forces., 12 pages, 7 figures

Bacillus subtilis forms dormant spores upon nutrient depletion. Germinant receptors (GRs) in spore’s inner membrane respond to ligands such as L-alanine, and trigger spore germination. In B. subtilis spores, GerA is the major GR, and has three subunits, GerAA, GerAB, and GerAC. L-Alanine activation of GerA requires all three subunits, but which binds L-alanine is unknown. To date, how GRs trigger germination is unknown, in particular due to lack of detailed structural information about B subunits. Using homology modelling with molecular dynamics (MD) simulations, we present structural predictions for the integral membrane protein GerAB. These predictions indicate that GerAB is an α-helical transmembrane protein containing a water channel. The MD simulations with free L-alanine show that alanine binds transiently to specific sites on GerAB. These results provide a starting point for unraveling the mechanism of L-alanine mediated signaling by GerAB, which may facilitate early events in spore germination.

The hydrophobic tails of aliphatic primary alcohols do insert into the hydrophobic core of a lipid bilayer. Thereby, they disrupt hydrophobic interactions between the lipid molecules, resulting in a decreased lipid order, i.e., an increased membrane fluidity. While aromatic alcohols, such as 2-phenylethanol, also insert into lipid bilayers and disturb the membrane organization, the impact of aromatic alcohols on the structure of biological membranes, as well as the potential physiological implication of membrane incorporation has only been studied to a limited extent. Although diverse targets are discussed to be causing the bacteriostatic and bactericidal activity of 2-phenylethanol, it is clear that 2-phenylethanol severely affects the structure of biomembranes, which has been linked to its bacteriostatic activity. Yet, in fungi some 2-phenylethanol derivatives are also produced, some of which appear to also have bacteriostatic activities. We showed that the 2-phenylethanol derivatives phenylacetic acid, phenyllactic acid, and methyl phenylacetate, but not Tyrosol, were fully incorporated into model membranes and affected the membrane organization. Furthermore, we observed that the propensity of the herein-analyzed molecules to partition into biomembranes positively correlated with their respective bacteriostatic activity, which clearly linked the bacteriotoxic activity of the substances to biomembranes.

Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study we have introduced deepBackmap: a deep neural-network-based approach to reverse-map equilibrated molecular structures for condensed-phase systems. Our method combines data-driven and physics-based aspects, leading to high-quality reconstructed structures. In this work, we expand the scope of our model and examine its chemical transferability. To this end, we train deepBackmap solely on homogeneous molecular liquids of small molecules, and apply it to a more challenging polymer melt. We augment the generator's objective with different force-field-based terms as prior to regularize the results. The best performing physical prior depends on whether we train for a specific chemistry, or transfer our model. Our local environment representation combined with the sequential reconstruction of fine-grained structures help reach transferability of the learned correlations., 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2003.07753

Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parameterization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parameterized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parameterizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation–anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different CG models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.

From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations, and the respective interconversion rates. Toward this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints and about populations using thermodynamics restraints. However, it is still unclear how to include experimental knowledge about interconversion rates. Here, we introduce a method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum-entropy and maximum-caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, this method provides a minimally perturbed path distribution consistent with the kinetic constraints, as well as modified free energy and committor landscapes. We illustrate the application of the method to a series of model systems, including all-atom molecular simulations of protein folding. Our results show that by combining experimental rate constants and molecular dynamics simulations, this approach enables the determination of transition states, reaction mechanisms, and free energies. We anticipate that this method will extend the applicability of molecular simulations to kinetic studies in structural biology and that it will assist the development of force fields to reproduce kinetic and thermodynamic observables. Furthermore, this approach is generally applicable to a wide range of systems in biology, physics, chemistry, and material science.

Non-equilibrium steady states are of great interest for the study of photosynthesis, molecular motors, biological switches or driven systems in statistical physics. The aim of this thesis is to broaden the understanding of non-equilibrium steady states (NESS) in soft-matter systems and provide a framework for reweighting dynamical information between NESS. The method is applied to phenomenological single particle systems described in full configurational coordinates and a tetraalanine peptide described in system-specific collective variables. We avoid the combinatorial explosion of microtrajectories by systematically constructing pathways through Markovian transitions. The reweighting is based on the information theoretic approach of Jaynes' Maximum Caliber, connecting data drawn from simulation or experiment to system information in the form of constraints. It is shown that local entropy production constraints define a NESS by controlling dissipation of a system on a local level. Non-dissipative contributions to the dynamics are drawn from the reference data and are shown to define an invariant quantity under reweighting. The presented reweighting method demonstrates the potential of the Maximum Caliber to understand and analyse systems off-equilibrium.

[FeFe] hydrogenase enzymes can reversibly catalyze the conversion of protons into molecular hydrogen. The active site of the [FeFe] hydrogenase enzyme has inspired many biomimetic catalysts to produce molecular hydrogen. A fundamental understanding of the reaction mechanism of the enzyme could help in designing better biomimetic catalysts. The active site of the [FeFe] hydrogenase enzyme is buried inside the protein. The transport of electrons and protons to the active site of the protein is crucial for an efficient catalytic cycle. In this thesis we used computational techniques to understand the transport process as well as the reaction mechanism of the enzyme and the catalysts and finally provides a guideline in choosing various ligands for designing a biomimetic catalyst. Classical molecular dynamics, QM/MM and DFT-MD are used in this study for an elaborative understanding of electron transfer, proton transfer and reaction mechanism.

Mixtures of methane gas & water forms ice-like solid methane hydrates (MH) via homogeneous nucleation. MH have important industrial and climate implications, yet their formation is poorly understood. Obtaining such understanding could lead to improved control of crystallization, as well as insight in polymorph selection in general, but is hampered by limited experimental resolution. Until now direct molecular dynamics simulations was used which could provide such insight, but is not feasible for realistic conditions due to rare event nature of nucleation. We harvest ensemble of the rare unbiased nucleation trajectories by employing Transition Path Sampling (TPS) and Transition Interface Sampling (TIS). These are trajectories that sample the free energy barrier across the liquid & solid phase. With an exorbitant simulation time of 4ms, we show that with decreasing undercooling the mechanism shifts from amorphous to crystalline polymorph formation. At intermediate temperature two mechanisms compete with each other. Reaction coordinate analysis reveals the amount of specific cage type is crucial for crystallisation, while irrelevant for amorphous solids. Polymorph selection is thus governed by kinetic accessibility of the correct cage type and occurs at precritical nucleus sizes against Ostwald’s step rule. We estimate the rate and nucleation barrier height for this process. Beyond this, we have explored carbon dioxide hydrate formation using similar methodology and got some novel and exciting new results. In the last part of the thesis, we applied machine learning algorithm to the data generated from TPS simulation of methane hydrate to find the reaction coordinate in the nucleation process. Overall, this thesis illustrates how a complicated process of nucleation can be fathomed crystal clearly, using path sampling.

A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement -- backmapping -- of a coarse-grained structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the coarse-grained structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data., Comment: 11 pages, 6 figures

The lipid raft hypothesis postulates that cell membranes possess some degree of lateral organization. The last decade has seen a large amount of experimental evidence for rafts. Yet, the underlying mechanism remains elusive. One hypothesis that supports rafts relies on the membrane to lie near a critical point. While supported by experimental evidence, the role of regulation is unclear. Using both a lattice model and molecular dynamics simulations, we show that lipid regulation of a many-component membrane can lead to critical behavior over a large temperature range. Across this range, the membrane displays a critical composition due to finite-size effects. This mechanism provides a rationale as to how cells tune their composition without the need for specific sensing mechanisms. It is robust and reproduces important experimentally verified biological trends: membrane-demixing temperature closely follows cell growth temperature, and the composition evolves along a critical manifold. The simplicity of the mechanism provides a strong argument in favor of the critical membrane hypothesis.SIGNIFICANCEWe show that biological regulation of a large amount of phospholipids in membranes naturally leads to a critical composition for finite-size systems. This suggests that regulating a system near a critical point is trivial for cells. These effects vanish logarithmically and therefore can be present in micron-sized systems.

The Fe2(bdt)(CO)6 [bdt = benzenedithiolato] complex, a synthetic mimic of the [FeFe] hydrogenase enzyme can electrochemically convert protons into molecular hydrogen. Molecular understanding of the cascade of reaction steps is important for the design of more efficient catalysts. In this study, we investigate the reaction mechanism of the hydrogen production catalysis in explicit solution of acetonitrile using first principles molecular dynamics simulations. We have characterized all reduction and protonation intermediates taking part in the catalytic cycle. Free energy surfaces of the activated reaction steps are calculated using metadynamics. We find that the second protonation leading to molecular hydrogen formation is the rate limiting step. Direct protonation of the bridging hydride by a proton from the solution to form H2 is the most favorable reaction pathway. However, also a bdt sulfur atom can become protonated, leading to a possible proton trap state that reduces the catalytic efficiency. Our calculations validate the ECEC mechanism proposed using cyclic voltammetry.

The OpenPathSampling (OPS) package provides an easy-to-use framework to apply transition path sampling methodologies to complex molecular systems with a minimum of effort. Yet, the extensibility of OPS allows for the exploration of new path sampling algorithms by building on a variety of basic operations. In a companion paper [Swenson et al. J. Chem. Theory Comput. 2018, 10.1021/acs.jctc.8b00626] we introduced the basic concepts and the structure of the OPS package, and how it can be employed to perform standard transition path sampling and (replica exchange) transition interface sampling. In this paper, we elaborate on two theoretical developments that went into the design of OPS. The first development relates to the construction of path ensembles, the what is being sampled. We introduce a novel set-based notation for the path ensemble, which provides an alternative paradigm for constructing path ensembles and allows building arbitrarily complex path ensembles from fundamental ones. The second fundamental development is the structure for the customization of Monte Carlo procedures; how path ensembles are being sampled. We describe in detail the OPS objects that implement this approach to customization, the MoveScheme and the PathMover, and provide tools to create and manipulate these objects. We illustrate both the path ensemble building and sampling scheme customization with several examples. OPS thus facilitates both standard path sampling application in complex systems as well as the development of new path sampling methodology, beyond the default.

Molecular motor proteins are used in biological systems to generate directed motion. They consist of one end that can bind to and then move along a filament. The other end can then bind to a cargo that needs transporting and the motor then pulls it along. Here, we consider the energetics of this process allowing for the friction force exerted by the surrounding fluid, given that the process takes place in a confined geometry. In nature, not all motor/cargo complexes are bound to the filament, many are in solution. Here, we address the question of whether this can be energetically favourable given that the unbound complexes will be transported by the flow generated by those that are bound. A simple theory suggests that this is the case and that there exists an optimal coverage of bound complexes. Simulations of a model of this system that includes all the relevant hydrodynamic effects confirm that the assumptions in the theory are valid so long as the coverage of bound complexes is not too low. Using realistic values for the parameters involved yields an optimal coverage that is plausible for the biological systems involved.

Many proteins exhibit multiple binding patches. A patch may harbour a key chemical modification site, but may also simply act as a trap for the binding to another site. Here we consider the scenario in which one molecule (enzyme) binds another molecule (substrate) which contains two sites. We present microscopic expressions for the rate at which the enzyme binds to a particular site on the substrate, both for the scenario in which the enzyme directly binds the site without first visiting the other site, and for the case in which it may visit the other site an arbitrary number of times before binding to the site of interest. We also present the expressions for the corresponding dissociation reactions. These expressions can be used to compute in a single rare-event simulation of the dissociation pathway not only both the intrinsic and effective dissociation rate constants but also both association rate constants. [GRAPHICS] .

The reaction coordinate (RC) is the principal collective variable or feature that determines the progress along an activated or reactive process. In a molecular simulation using enhanced sampling, a good description of the RC is crucial for generating sufficient statistics. Moreover, the RC provides invaluable atomistic insight into the process under study. The optimal RC is the committor, which represents the likelihood of a system to evolve toward a given state based on the coordinates of all its particles. As the interpretability of such a high dimensional function is low, a more practical approach is to describe the RC by some low-dimensional molecular collective variables or order parameters. While several methods can perform this dimensionality reduction, they usually require a preselection of these low-dimension collective variables (CVs). Here, we propose to automate this dimensionality reduction using an extended autoencoder, which maps the input (many CVs) onto a lower-dimensional latent space, which is subsequently used for the reconstruction of the input as well as the prediction of the committor function. As a consequence, the latent space is optimized for both reconstruction and committor prediction and is likely to yield the best non-linear low-dimensional representation of the committor. We test our extended autoencoder model on simple but nontrivial toy systems, as well as extensive molecular simulation data of methane hydrate nucleation. The extended autoencoder model can effectively extract the underlying mechanism of a reaction, make reliable predictions about the committor of a given configuration, and potentially even generate new paths representative for a reaction.

Cyclic peptides (CPs) that self-assemble in nanotubes can be candidates for use as antifreeze proteins. Based on the cyclic peptide sequence cyclo-[(L-LYS-D-ALA-L-LEU-D-ALA)2], which can stack into nanotubes, we propose a putative antifreeze cyclic peptide (AFCP) sequence, cyclo-[(L-LYS-D-ALA)2-(L-THR-D-ALA)2], containing THR-ALA-THR ice binding motifs. Using molecular dynamics simulations we investigate the stability of these cyclic peptides and their growth mechanism. Both nanotube sequences get more stable as a function of size. The relative stability of the AFCP sequence CPNT increases at sizes greater than a dimer by forming intermolecular THR side chain H-bonds. We find that, like the naturally occurring AF protein from spruce budworm (Choristoneura fumiferana), the THR distances of the AFCP's ice binding motif match the ice prism plane O–O distances, thus making the AFCP a suitable AF candidate. In addition, we investigated the nanotube growth process, i.e. the association/dissociation of a single CP to an existing AFCP nanotube, by Transition Path Sampling. We found a general dock-lock mechanism, in which a single CP first docks loosely before locking into place. Moreover, we identified several qualitatively different mechanisms for association, involving different metastable intermediates, including a state in which the peptide was misfolded inside the hydrophobic core of the tube. Finally, we find evidence for a mechanism involving non-specific association followed by 1D diffusion. Under most conditions, this will be the dominant pathway. The results yield insights into the mechanisms of peptide assembly, and might lead to an improved design of self-assembling antifreeze proteins.

We performed replica exchange molecular dynamics and forward flux sampling simulations of hexapeptide VQIINK and VQIVYK systems, also known as, respectively, fragments PHF6* and PHF6 from the tau protein. Being a part of the microtubule binding region, these fragments are known to be aggregation prone, and at least one of them is a prerequisite for fibril formation of the tau protein. Using a coarse-grained force field, we establish the phase behavior of both fragments, and investigate the nucleation kinetics for the conversion into a β-sheet fibril. As the conversion is, in principle, a reversible process, we predict the rate constants for both the fibril formation and melting, and examine the corresponding mechanisms. Our simulations indicate that, while both fragments form disordered aggregates, only PHF6 is able to form β-sheet fibrils. This observation provides a possible explanation for the lack of available steric zipper crystal structures for PHF6*.

We develop two novel transition path sampling (TPS) algorithms for harvesting ensembles of rare event trajectories using non-equilibrium dynamics. These methods have the advantage that no predefined reaction coordinate is needed. Instead, an instantaneous reaction coordinate is based on the current path. Constituting a Monte Carlo random walk in trajectory space, the algorithms can be viewed as bridging between the original TPS methodology and the Rosenbluth based forward flux sampling methodology. We illustrate the new methods on toy models undergoing equilibrium and non-equilibrium dynamics, including an active Brownian particle system. For the latter, we find that transitions between steady states occur via states that are locally ordered but globally disordered.

Coarse-grained (CG) conformational surface hopping (SH) adapts the concept of multisurface dynamics, initially developed to describe electronic transitions in chemical reactions, to accurately describe classical molecular dynamics at a reduced level. The SH scheme couples distinct conformational basins (states), each described by its own force field (surface), resulting in a significant improvement of the approximation to the many-body potential of mean force [Phys. Rev. Lett. 121, 256002 (2018)]. The present study first describes CG SH in more detail, through both a toy model and a three-bead model of hexane. We further extend the methodology to non-bonded interactions and report its impact on liquid properties. Finally, we investigate the transferability of the surfaces to distinct systems and thermodynamic state points, through a simple tuning of the state probabilities. In particular, applications to variations in temperature and chemical composition show good agreement with reference atomistic calculations, introducing a promising "weak-transferability regime," where CG force fields can be shared across thermodynamic and chemical neighborhoods., Comment: 15 pages, 7 figures

H-NS proteins act as osmotic sensors translating changes in osmolarity into altered DNA binding properties, thus, regulating enterobacterial genome organization and genes transcription. The molecular mechanism underlying the switching process and its conservation among H-NS family members remains elusive.Here, we focus on the H-NS family protein MvaT from P. aeruginosa and demonstrate experimentally that its protomer exists in two different conformations, corresponding to two different functional states. In the half-opened state (dominant at low salt) the protein forms filaments along DNA, in the fully opened state (dominant at high salt) the protein bridges DNA. This switching is a direct effect of ionic strengths on electrostatic interactions between the appositively charged DNA binding and N-terminal domains of MvaT. The asymmetric charge distribution and intramolecular interactions are conserved among the H-NS family of proteins. Therefore, our study establishes a general paradigm for the molecular mechanistic basis of the osmosensitivity of H-NS proteins.

Transition path sampling is a powerful technique for investigating rare transitions, especially when the mechanism is unknown and one does not have access to the reaction coordinate. Straightforward application of transition path sampling does not directly provide the free energy landscape nor the kinetics. This drawback has motivated the development of path sampling extensions able to simultaneously access both kinetics and thermodynamics, such as transition interface sampling, and the reweighted path ensemble. However, performing transition interface sampling is more involved than standard two-state transition path sampling and still requires (some) insight into the reaction to define interfaces. While packages that can efficiently compute path ensembles for transition interface sampling are now available, it would be useful to directly compute the free energy from a single standard transition path sampling simulation. To achieve this, we present here an approximate method, denoted virtual interface exchange transition path sampling, that makes use of the rejected pathways in a form of waste recycling. The method yields an approximate reweighted path ensemble that allows an immediate view of the free energy landscape from a standard single transition path sampling simulation, as well as enables a committor analysis.

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromo-lecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Bacteria contain several nucleoid-associated proteins that organize their genomic DNA into the nucleoid by bending, wrapping or bridging DNA. The Histone-like Nucleoid Structuring protein H-NS found in many Gram-negative bacteria is a DNA bridging protein and can structure DNA by binding to two separate DNA duplexes or to adjacent sites on the same duplex, depending on external conditions. Several nucleotide sequences have been identified to which H-NS binds with high affinity, indicating H-NS prefers AT-rich DNA. To date, highly detailed structural information of the H-NS DNA complex remains elusive. Molecular simulation can complement experiments by modelling structures and their time evolution in atomistic detail. In this paper we report an exploration of the different binding modes of H-NS to a high affinity nucleotide sequence and an estimate of the associated rate constant. By means of molecular dynamics simulations, we identified three types of binding for H-NS to AT-rich DNA. To further sample the transitions between these binding modes, we performed Replica Exchange Transition Interface Sampling, providing predictions of the mechanism and rate constant of H-NS binding to DNA. H-NS interacts with the DNA through a conserved QGR motif, aided by a conserved arginine at position 93. The QGR motif interacts first with phosphate groups, followed by the formation of hydrogen bonds between acceptors in the DNA minor groove and the sidechains of either Q112 or R114. After R114 inserts into the minor groove, the rest of the QGR motif follows. Full insertion of the QGR motif in the minor groove is stable over several tens of nanoseconds, and involves hydrogen bonds between the bases and both backbone and sidechains of the QGR motif. The rate constant for the process of H-NS binding to AT-rich DNA resulting in full insertion of the QGR motif is in the order of 106 M−1s−1, which is rate limiting compared to the non-specific association of H-NS to the DNA backbone at a rate of 108 M−1s−1., Author summary The Histone-like Nucleoid Structuring protein (H-NS) occurs in enterobacteria, such as Salmonella typhimurium and Escherichia coli, and structures DNA by forming filaments along DNA duplexes. Several nucleotide sequences have been identified to which H-NS binds with high affinity. Yet, obtaining highly detailed structural information of the H-NS DNA complex has proven to be a major challenge, which has not been yet resolved. By employing molecular dynamics simulations we were able to provide high resolution insights into the mechanism of DNA binding by H-NS. We identified various ways in which H-NS can bind to DNA. In all binding events, a conserved region in the protein initiates the association of H-NS to DNA. Our results show that H-NS binds in the minor groove of AT-rich DNA via a series of intermediate steps. Using advanced molecular simulation methods we predicted that the process of H-NS binding to the DNA backbone to full insertion into the minor groove occurs in the order of a million times per second, which is slower than the non-specific association of H-NS to the DNA backbone.

Association and dissociation of proteins are fundamental processes in nature. Although simple to understand conceptually, the details of the underlying mechanisms and role of the solvent are poorly understood. Here, we investigate the dissociation of the hydrophilic β-lactoglobulin dimer by employing transition path sampling. Analysis of the sampled path ensembles reveals a variety of mechanisms: (1) a direct aligned dissociation (2) a hopping and rebinding transition followed by unbinding, and (3) a sliding transition before unbinding. Reaction coordinate and transition-state analysis predicts that, besides native contact and neighboring salt-bridge interactions, solvent degrees of freedom play an important role in the dissociation process. Bridging waters, hydrogen-bonded to both proteins, support contacts in the native state and nearby lying transition-state regions, whereas they exhibit faster dynamics in further lying transition-state regions, rendering the proteins more mobile and assisting in rebinding. Analysis of the structure and dynamics of the solvent molecules reveals that the dry native interface induces enhanced populations of both disordered hydration water near hydrophilic residues and tetrahedrally ordered hydration water nearby hydrophobic residues. Although not exhaustive, our sampling of rare unbiased reactive molecular dynamics trajectories enhances the understanding of protein dissociation via complex pathways including (multiple) rebinding events., The Journal of Physical Chemistry B, 123 (9), ISSN:1520-6106, ISSN:1520-5207, ISSN:1089-5647

Colloidal particles suspended in a binary liquid mixture can interact via solvent mediated interactions, known as critical Casimir forces. For anisotropic colloids this interaction becomes directional, which leads to rich phase behavior. While experimental imaging and particle tracking techniques allow determination of isotropic effective potentials via Boltzmann inversion, the modeling of effective interaction in anisotropic systems is non-trivial precisely because of this directionality. Here we extract effective interaction potentials for non-spherical dumbbell particles from observed radial and angular distributions, by employing reference interaction site model (RISM) theory and direct Monte Carlo simulations. For colloidal dumbbell particles dispersed in a binary liquid mixture and interacting via induced critical Casimir forces, we determine the effective site–site potentials for a range of experimental temperatures. Using these potentials to simulate the system for strong Casimir forces, we reproduce the experimentally observed collapse, and provide a qualitative explanation for this behavior.

With the imminent threat of global warming and climate change, it has never been more crucial to look for cleaner sources of energy and efficient ways to store it such as splitting water to produce Hydrogen. DFT-MD simulations has been and still is an immensely powerful instrument to obtain microscopic insight in aqueous solutions. To get reliable information about the reaction mechanism, we benchmark DFT parameters on various structural and dynamical properties of bulkwater. Next, we developed a new framework to simulate electron transfer reactions between a pair of Ru ions in water by combining DFT-MD simulations with transition path sampling. We show that the simple self-exchange reaction between two metal ions is coupled to a proton transfer. We also developed a proton transfer collective variable (PTCV) to study proton dissociation. The free energy calculations performed using enhanced sampling methods do not include nuclear quantum effects such as zero point energy (ZPE). We use the 2PT method to calculate the zero point energy of various liquids and their mixtures. Finally, we apply the newly developed frameworks to study the water splitting reaction on the highly active RuO₂ (110) surface. We study various proton transfer processes occurring on the metal oxide-water interface and show that by considering the explicit solvent, the barrier for the rate determining step (O-O bond formation) of the OER decreases from 0.74 to 0.4 eV. Our framework is straightforward to extend to other systems involving proton transfer such as homogenous catalysis, hydrogen evolution reaction and meta-organic frameworks.