Evolution of mechanical cooperativity among myosin II motors.

Myosin II is a biomolecular machine that is responsible for muscle contraction. Myosin II motors act cooperatively: during muscle contraction, multiple motors bind to a single actin filament and pull it against an external load, like people pulling on a rope in a tug-of-war. We model the dynamics of actomyosin filaments in order to study the evolution of motor-motor cooperativity. We find that filament backsliding-the distance an actin slides backward when a motor at the end of its cycle releases-is central to the speed and efficiency of muscle contraction. Our model predicts that this backsliding has been reduced through evolutionary adaptations to the motor's binding propensity, the strength of the motor's power stroke, and the force dependence of the motor's release from actin. These properties optimize the collective action of myosin II motors, which is not a simple sum of individual motor actions. The model also shows that these evolutionary variables can explain the speed-efficiency trade-off observed across different muscle tissues. This is an example of how evolution can tune the microscopic properties of individual proteins in order to optimize complex biological functions.

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA.

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA.

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.

Opposing Pressures of Speed and Efficiency Guide the Evolution of Molecular Machines.

Many biomolecular machines need to be both fast and efficient. How has evolution optimized these machines along the tradeoff between speed and efficiency? We explore this question using optimizable dynamical models along coordinates that are plausible evolutionary degrees of freedom. Data on 11 motors and ion pumps are consistent with the hypothesis that evolution seeks an optimal balance of speed and efficiency, where any further small increase in one of these quantities would come at great expense to the other. For FoF1-ATPases in different species, we also find apparent optimization of the number of subunits in the c-ring, which determines the number of protons pumped per ATP synthesized. Interestingly, these ATPases appear to more optimized for efficiency than for speed, which can be rationalized through their key role as energy transducers in biology. The present modeling shows how the dynamical performance properties of biomolecular motors and pumps may have evolved to suit their corresponding biological actions.

Mechanisms for achieving high speed and efficiency in biomolecular machines.

How does a biomolecular machine achieve high speed at high efficiency? We explore optimization principles using a simple two-state dynamical model. With this model, we establish physical principles-such as the optimal way to distribute free-energy changes and barriers across the machine cycle-and connect them to biological mechanisms. We find that a machine can achieve high speed without sacrificing efficiency by varying its conformational free energy to directly link the downhill, chemical energy to the uphill, mechanical work and by splitting a large work step into more numerous, smaller substeps. Experimental evidence suggests that these mechanisms are commonly used by biomolecular machines. This model is useful for exploring questions of evolution and optimization in molecular machines.

Communication: Adaptive boundaries in multiscale simulations.

Combined-resolution simulations are an effective way to study molecular properties across a range of length and time scales. These simulations can benefit from adaptive boundaries that allow the high-resolution region to adapt (change size and/or shape) as the simulation progresses. The number of degrees of freedom required to accurately represent even a simple molecular process can vary by several orders of magnitude throughout the course of a simulation, and adaptive boundaries react to these changes to include an appropriate but not excessive amount of detail. Here, we derive the Hamiltonian and distribution function for such a molecular simulation. We also design an algorithm that can efficiently sample the boundary as a new coordinate of the system. We apply this framework to a mixed explicit/continuum simulation of a peptide in solvent. We use this example to discuss the conditions necessary for a successful implementation of adaptive boundaries that is both efficient and accurate in reproducing molecular properties.

Molecular Motors: Power Strokes Outperform Brownian Ratchets.

Molecular motors convert chemical energy (typically from ATP hydrolysis) to directed motion and mechanical work. Their actions are often described in terms of "Power Stroke" (PS) and "Brownian Ratchet" (BR) mechanisms. Here, we use a transition-state model and stochastic thermodynamics to describe a range of mechanisms ranging from PS to BR. We incorporate this model into Hill's diagrammatic method to develop a comprehensive model of motor processivity that is simple but sufficiently general to capture the full range of behavior observed for molecular motors. We demonstrate that, under all conditions, PS motors are faster, more powerful, and more efficient at constant velocity than BR motors. We show that these differences are very large for simple motors but become inconsequential for complex motors with additional kinetic barrier steps.

Finite domain simulations with adaptive boundaries: accurate potentials and nonequilibrium movesets.

We extend the theory of hybrid explicit/implicit solvent models to include an explicit domain that grows and shrinks in response to a solute's evolving configuration. The goal of this model is to provide an appropriate but not excessive amount of solvent detail, and the inclusion of an adjustable boundary provides a significant computational advantage for solutes that explore a range of configurations. In addition to the theoretical development, a successful implementation of this method requires (1) an efficient moveset that propagates the boundary as a new coordinate of the system, and (2) an accurate continuum solvent model with parameters that are transferable to an explicit domain of any size. We address these challenges and develop boundary updates using Monte Carlo moves biased by nonequilibrium paths. We obtain the desired level of accuracy using a "decoupling interface" that we have previously shown to remove boundary artifacts common to hybrid solvent models. Using an uncharged, coarse-grained solvent model, we then study the efficiency of nonequilibrium paths that a simulation takes by quantifying the dissipation. In the spirit of optimization, we study this quantity over a range of simulation parameters.

Reducing the effect of Metropolization on mixing times in molecular dynamics simulations.

Molecular dynamics algorithms are subject to some amount of error dependent on the size of the time step that is used. This error can be corrected by periodically updating the system with a Metropolis criterion, where the integration step is treated as a selection probability for candidate state generation. Such a method, closely related to generalized hybrid Monte Carlo (GHMC), satisfies the balance condition by imposing a reversal of momenta upon candidate rejection. In the present study, we demonstrate that such momentum reversals can have a significant impact on molecular kinetics and extend the time required for system decorrelation, resulting in an order of magnitude increase in the integrated autocorrelation times of molecular variables for the worst cases. We present a simple method, referred to as reduced-flipping GHMC, that uses the information of the previous, current, and candidate states to reduce the probability of momentum flipping following candidate rejection while rigorously satisfying the balance condition. This method is a simple modification to traditional, automatic-flipping, GHMC methods and significantly mitigates the impact of such algorithms on molecular kinetics and simulation mixing times.

A smoothly decoupled particle interface: new methods for coupling explicit and implicit solvent.

A common theme of studies using molecular simulation is a necessary compromise between computational efficiency and resolution of the forcefield that is used. Significant efforts have been directed at combining multiple levels of granularity within a single simulation in order to maintain the efficiency of coarse-grained models, while using finer resolution in regions where such details are expected to play an important role. A specific example of this paradigm is the development of hybrid solvent models, which explicitly sample the solvent degrees of freedom within a specified domain while utilizing a continuum description elsewhere. Unfortunately, these models are complicated by the presence of structural artifacts at or near the explicit/implicit boundary. The presence of these artifacts significantly complicates the use of such models, both undermining the accuracy obtained and necessitating the parameterization of effective potentials to counteract the artificial interactions. In this work, we introduce a novel hybrid solvent model that employs a smoothly decoupled particle interface (SDPI), a switching region that gradually transitions from fully interacting particles to a continuum solvent. The resulting SDPI model allows for the use of an implicit solvent model based on a simple theory that needs to only reproduce the behavior of bulk solvent rather than the more complex features of local interactions. In this study, the SDPI model is tested on spherical hybrid domains using a coarse-grained representation of water that includes only Lennard-Jones interactions. The results demonstrate that this model is capable of reproducing solvent configurations absent of boundary artifacts, as if they were taken from full explicit simulations.

Assessing the performance of implicit solvation models at a nucleic acid surface.

Implicit solvation models are popular alternatives to explicit solvent methods due to their ability to "pre-average" solvent behavior and thus reduce the need for computationally-expensive sampling. Previously, we have demonstrated that Poisson-Boltzmann models for polar solvation and integral-based models for nonpolar solvation can reproduce explicit solvation forces in a low-charge density protein system. In the present work, we examine the ability of these continuum models to describe solvation forces at the surface of a RNA hairpin. While these models do not completely describe all of the details of solvent behavior at this highly-charged biomolecular interface, they do provide a reasonable description of average solvation forces and therefore show significant promise for developing more robust implicit descriptions of solvent around nucleic acid systems for use in biomolecular simulation and modeling. Additionally, we observe fairly good transferability in the nonpolar model parameters optimized for protein systems, suggesting its robustness for modeling general nonpolar solvation phenomena in biomolecular systems.

Optimizing the Poisson Dielectric Boundary with Explicit Solvent Forces and Energies: Lessons Learned with Atom-Centered Dielectric Functions.

Accurate implicit solvent models require parameters that have been optimized in a system- or atom-specific manner on the basis of experimental data or more rigorous explicit solvent simulations. Models based on the Poisson or Poisson-Boltzmann equation are particularly sensitive to the nature and location of the boundary which separates the low dielectric solute from the high dielectric solvent. Here, we present a novel method for optimizing the solute radii, which define the dielectric boundary, on the basis of forces and energies from explicit solvent simulations. We use this method to optimize radii for protein systems defined by AMBER ff99 partial charges and a spline-smoothed solute surface. The spline-smoothed surface is an atom-centered dielectric function that enables stable and efficient force calculations. We explore the relative performance of radii optimized with forces alone and those optimized with forces and energies. We show that our radii reproduce the explicit solvent forces and energies more accurately than four other parameter sets commonly used in conjunction with the AMBER force field, each of which has been appropriately scaled for spline-smoothed surfaces. Finally, we demonstrate that spline-smoothed surfaces show surprising accuracy for small, compact systems but may have limitations for highly solvated protein systems. The optimization method presented here is efficient and applicable to any system with explicit solvent parameters. It can be used to determine the optimal continuum parameters when experimental solvation energies are unavailable and the computational costs of explicit solvent charging free energies are prohibitive.

Assessing implicit models for nonpolar mean solvation forces: the importance of dispersion and volume terms.

Continuum solvation models provide appealing alternatives to explicit solvent methods because of their ability to reproduce solvation effects while alleviating the need for expensive sampling. Our previous work has demonstrated that Poisson-Boltzmann methods are capable of faithfully reproducing polar explicit solvent forces for dilute protein systems; however, the popular solvent-accessible surface area model was shown to be incapable of accurately describing nonpolar solvation forces at atomic-length scales. Therefore, alternate continuum methods are needed to reproduce nonpolar interactions at the atomic scale. In the present work, we address this issue by supplementing the solvent-accessible surface area model with additional volume and dispersion integral terms suggested by scaled particle models and Weeks-Chandler-Andersen theory, respectively. This more complete nonpolar implicit solvent model shows very good agreement with explicit solvent results and suggests that, although often overlooked, the inclusion of appropriate dispersion and volume terms are essential for an accurate implicit solvent description of atomic-scale nonpolar forces.