Grant M. Rotskoff

Structural basis of a protein partner switch that regulates the general stress response of α-proteobacteria.

α-Proteobacteria uniquely integrate features of two-component signal transduction (TCS) and alternative sigma factor (σ) regulation to control transcription in response to general stress. The core of this regulatory system is the PhyR protein, which contains a σ-like (SL) domain and a TCS receiver domain. Aspartyl phosphorylation of the PhyR receiver in response to stress signals promotes binding of the anti-σ factor, NepR, to PhyR-SL. This mechanism, whereby NepR switches binding between its cognate σ factor and phospho-PhyR (PhyR∼P), controls transcription of the general stress regulon. We have defined the structural basis of the PhyR∼P/NepR interaction in Caulobacter crescentus and characterized the effect of aspartyl phosphorylation on PhyR structure by molecular dynamics simulations. Our data support a model in which phosphorylation of the PhyR receiver domain promotes its dissociation from the PhyR-SL domain, which exposes the NepR binding site. A highly dynamic loop–helix region (α3-α4) of the PhyR-SL domain plays an important role in PhyR∼P binding to NepR in vitro, and in stress-dependent activation of transcription in vivo. This study provides a foundation for understanding the protein-protein interactions and protein structural dynamics that underpin general stress adaptation in a large and metabolically diverse clade of the bacterial kingdom.

Single-particle mapping of nonequilibrium nanocrystal transformations.

Watching it all fall apart The control of the shape and size of metal nanoparticles can be very sensitive to the growth conditions of the particles. Ye et al. studied the reverse process: They tracked the dissolution of gold nanoparticles in a redox environment inside a liquid cell within an electron microscope, controlling the particle dissolution with the electron beam. Tracking short-lived particle shapes revealed structures of greater or lesser stability. The findings suggest kinetic routes to particle sizes and shapes that would otherwise be difficult to generate. Science, this issue p. 874 Dissolution pathways and kinetic shapes of gold nanoparticles are observed inside an electron microscope. Chemists have developed mechanistic insight into numerous chemical reactions by thoroughly characterizing nonequilibrium species. Although methods to probe these processes are well established for molecules, analogous techniques for understanding intermediate structures in nanomaterials have been lacking. We monitor the shape evolution of individual anisotropic gold nanostructures as they are oxidatively etched in a graphene liquid cell with a controlled redox environment. Short-lived, nonequilibrium nanocrystals are observed, structurally analyzed, and rationalized through Monte Carlo simulations. Understanding these reaction trajectories provides important fundamental insight connecting high-energy nanocrystal morphologies to the development of kinetically stabilized surface features and demonstrates the importance of developing tools capable of probing short-lived nanoscale species at the single-particle level.

Transition-Tempered Metadynamics: Robust, Convergent Metadynamics via On-the-Fly Transition Barrier Estimation.

Well-tempered metadynamics has proven to be a practical and efficient adaptive enhanced sampling method for the computational study of biomolecular and materials systems. However, choosing its tunable parameter can be challenging and requires balancing a trade-off between fast escape from local metastable states and fast convergence of an overall free energy estimate. In this article, we present a new smoothly convergent variant of metadynamics, transition-tempered metadynamics, that removes that trade-off and is more robust to changes in its own single tunable parameter, resulting in substantial speed and accuracy improvements. The new method is specifically designed to study state-to-state transitions in which the states of greatest interest are known ahead of time, but transition mechanisms are not. The design is guided by a picture of adaptive enhanced sampling as a means to increase dynamical connectivity of a models state space until percolation between all points of interest is reached, and it uses the degree of dynamical percolation to automatically tune the convergence rate. We apply the new method to Brownian dynamics on 48 random 1D surfaces, blocked alanine dipeptide in vacuo, and aqueous myoglobin, finding that transition-tempered metadynamics substantially and reproducibly improves upon well-tempered metadynamics in terms of first barrier crossing rate, convergence rate, and robustness to the choice of tuning parameter. Moreover, the trade-off between first barrier crossing rate and convergence rate is eliminated: the new method drives escape from an initial metastable state as fast as metadynamics without tempering, regardless of tuning.

Structural asymmetry in a conserved signaling system that regulates division, replication, and virulence of an intracellular pathogen

Significance Brucella abortus is an intracellular bacterial pathogen that inflicts a significant health burden on both humans and their livestock on a global scale. We demonstrate that an essential regulatory system controls the growth and morphology of B. abortus, and that this system is required for survival inside mammalian host cells. Using experimental and computational tools of structural biology, we further define how the protein components of this regulatory pathway interact at the atomic scale. Our results provide evidence for multiple, asymmetric modes of binding between essential pathway proteins that control transcription. The multimodal molecular interactions we observe provide evidence for new layers of allosteric control of this conserved gene regulatory system. We have functionally and structurally defined an essential protein phosphorelay that regulates expression of genes required for growth, division, and intracellular survival of the global zoonotic pathogen Brucella abortus. Our study delineates phosphoryl transfer through this molecular pathway, which initiates from the sensor kinase CckA and proceeds through the ChpT phosphotransferase to two regulatory substrates: CtrA and CpdR. Genetic perturbation of this system results in defects in cell growth and division site selection, and a specific viability deficit inside human phagocytic cells. Thus, proper control of B. abortus division site polarity is necessary for survival in the intracellular niche. We further define the structural foundations of signaling from the central phosphotransferase, ChpT, to its response regulator substrate, CtrA, and provide evidence that there are at least two modes of interaction between ChpT and CtrA, only one of which is competent to catalyze phosphoryltransfer. The structure and dynamics of the active site on each side of the ChpT homodimer are distinct, supporting a model in which quaternary structure of the 2:2 ChpT–CtrA complex enforces an asymmetric mechanism of phosphoryl transfer between ChpT and CtrA. Our study provides mechanistic understanding, from the cellular to the atomic scale, of a conserved transcriptional regulatory system that controls the cellular and infection biology of B. abortus. More generally, our results provide insight into the structural basis of two-component signal transduction, which is broadly conserved in bacteria, plants, and fungi.

Molecular Simulation Workflows as Parallel Algorithms : The Execution Engine of Copernicus, a Distributed High-Performance Computing Platform

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

Necessity of capillary modes in a minimal model of nanoscale hydrophobic solvation

Significance Hydrophobic effects, which play a crucial role in many chemical and biological processes, originate in the statistics of microscopic density fluctuations in liquid water. Chandler has established the foundation for a simple and unified understanding of these effects, by identifying essential aspects of water’s intermolecular structure while accounting for its proximity to phase coexistence. Here, we show that coarse-grained models based on this perspective, when constructed to include the statistics of capillary waves at interfaces, can achieve remarkable agreement with results of atomistically detailed simulations. Highly efficient and lacking adjustable parameters, such models hold promise as powerful tools for studying multiscale problems in hydrophobic solvation. Modern theories of the hydrophobic effect highlight its dependence on length scale, emphasizing the importance of interfaces in the vicinity of sizable hydrophobes. We recently showed that a faithful treatment of such nanoscale interfaces requires careful attention to the statistics of capillary waves, with significant quantitative implications for the calculation of solvation thermodynamics. Here, we show that a coarse-grained lattice model like that of Chandler [Chandler D (2005) Nature 437(7059):640–647], when informed by this understanding, can capture a broad range of hydrophobic behaviors with striking accuracy. Specifically, we calculate probability distributions for microscopic density fluctuations that agree very well with results of atomistic simulations, even many SDs from the mean and even for probe volumes in highly heterogeneous environments. This accuracy is achieved without adjustment of free parameters, because the model is fully specified by well-known properties of liquid water. As examples of its utility, we compute the free-energy profile for a solute crossing the air–water interface, as well as the thermodynamic cost of evacuating the space between extended nanoscale surfaces. These calculations suggest that a highly reduced model for aqueous solvation can enable efficient multiscale modeling of spatial organization driven by hydrophobic and interfacial forces.

Inferring dissipation from current fluctuations

Complex physical dynamics can often be modeled as a Markov jump process between mesoscopic configurations. When jumps between mesoscopic states are mediated by thermodynamic reservoirs, the time-irreversibility of the jump process is a measure of the physical dissipation. We rederive a recently introduced inequality relating the dissipation rate to current fluctuations in jump processes. We then adapt these results to diffusion processes via a limiting procedure, reaffirming that diffusions saturate the inequality. Finally, we study the impact of spatial coarse-graining in a two-dimensional model with driven diffusion. By observing fluctuations in coarse-grained currents, it is possible to infer a lower bound on the total dissipation rate, including the dissipation associated with hidden dynamics. The tightness of this bound depends on how well the spatial coarse-graining detects dynamical events that are driven by large thermodynamic forces.

Efficiency and large deviations in time-asymmetric stochastic heat engines

In a stochastic heat engine driven by a cyclic non-equilibrium protocol, fluctuations in work and heat give rise to a fluctuating efficiency. Using computer simulations and tools from large deviation theory, we have examined these fluctuations in detail for a model two-state engine. We find in general that the form of efficiency probability distributions is similar to those described by Verley et al (2014 Nat. Commun. 5 4721), in particular featuring a local minimum in the long-time limit. In contrast to the time-symmetric engine protocols studied previously, however, this minimum need not occur at the value characteristic of a reversible Carnot engine. Furthermore, while the local minimum may reside at the global minimum of a large deviation rate function, it does not generally correspond to the least likely efficiency measured over finite time. We introduce a general approximation for the finite-time efficiency distribution, , based on large deviation statistics of work and heat, that remains very accurate even when deviates significantly from its large deviation form.

Near-optimal protocols in complex nonequilibrium transformations

Significance Classical thermodynamics was developed to help design the best protocols for operating heat engines that remain close to equilibrium at all times. Modern experimental techniques for manipulating microscopic and mesoscopic systems routinely access far-from-equilibrium states, demanding new theoretical tools to describe the optimal protocols in this more complicated regime. Prior studies have sought, in simple models, the protocol that minimizes dissipation. We use computational tools to investigate the diversity of low-dissipation protocols. We show that optimal protocols can be accompanied by a vast set of near-optimal protocols, which still offer the substantive benefits of the optimal protocol. Although solving for the optimal protocol is typically difficult, computationally identifying a near-optimal protocol can be comparatively easy. The development of sophisticated experimental means to control nanoscale systems has motivated efforts to design driving protocols that minimize the energy dissipated to the environment. Computational models are a crucial tool in this practical challenge. We describe a general method for sampling an ensemble of finite-time, nonequilibrium protocols biased toward a low average dissipation. We show that this scheme can be carried out very efficiently in several limiting cases. As an application, we sample the ensemble of low-dissipation protocols that invert the magnetization of a 2D Ising model and explore how the diversity of the protocols varies in response to constraints on the average dissipation. In this example, we find that there is a large set of protocols with average dissipation close to the optimal value, which we argue is a general phenomenon.

Geometric approach to optimal nonequilibrium control: Minimizing dissipation in nanomagnetic spin systems

Optimal control of nanomagnets has become an urgent problem for the field of spintronics as technological tools approach thermodynamically determined limits of efficiency. In complex, fluctuating systems, such as nanomagnetic bits, finding optimal protocols is challenging, requiring detailed information about the dynamical fluctuations of the controlled system. We provide a physically transparent derivation of a metric tensor for which the length of a protocol is proportional to its dissipation. This perspective simplifies nonequilibrium optimization problems by recasting them in a geometric language. We then describe a numerical method, an instance of geometric minimum action methods, that enables computation of geodesics even when the number of control parameters is large. We apply these methods to two models of nanomagnetic bits: a Landau-Lifshitz-Gilbert description of a single magnetic spin controlled by two orthogonal magnetic fields, and a two-dimensional Ising model in which the field is spatially controlled. These calculations reveal nontrivial protocols for bit erasure and reversal, providing important, experimentally testable predictions for ultra-low-power computing.