Insulin aspart dimer dissociation in water.

The insulin hormone is stored in the hexameric form and dissociates to the dimeric form and finally to the active monomeric form. When insulin secretion is impaired, thereby affecting various metabolic processes, as a final response, insulin analogs are subcutaneously injected before meals to facilitate glucose metabolism. Depending on the molecular details, analogs are rapid or slow acting based on the dissociation rate of the dimer to monomer. Insulin aspart is a recombinant human insulin analog, acting faster than regular human insulin. Despite its practical and elementary importance, the process of insulin aspart dimer dissociation is relatively unknown. Here, we combined molecular dynamics simulations and umbrella sampling to characterize the energetic and structural features of dissociation of the insulin aspart dimer. Like previous studies on human insulin (another well studied analog), insulin aspart can also display a wide spectrum of pathways for dimer dissociation from dissociation happening without a major change in the monomer structure to dissociation that is coupled with unfolding of a protein. Additionally, water plays a vital role in the dissociation of the insulin aspart by stabilizing the monomers in the dissociated state. Our study shows the molecular details, such as the variation in the structure and orientation and conformational changes along the minimum energy pathways in the process of dissociation of the insulin aspart dimer.

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations.

The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a Δ-NetFF machine learning model, where the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields, was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.

Mechanistic insights from a quantitative analysis of pollen tube guidance.

BACKGROUND: Plant biologists have long speculated about the mechanisms that guide pollen tubes to ovules. Although there is now evidence that ovules emit a diffusible attractant, little is known about how this attractant mediates interactions between the pollen tube and the ovules. RESULTS: We employ a semi-in vitro assay, in which ovules dissected from Arabidopsis thaliana are arranged around a cut style on artificial medium, to elucidate how ovules release the attractant and how pollen tubes respond to it. Analysis of microscopy images of the semi-in vitro system shows that pollen tubes are more attracted to ovules that are incubated on the medium for longer times before pollen tubes emerge from the cut style. The responses of tubes are consistent with their sensing a gradient of an attractant at 100-150 mum, farther than previously reported. Our microscopy images also show that pollen tubes slow their growth near the micropyles of functional ovules with a spatial range that depends on ovule incubation time. CONCLUSIONS: We propose a stochastic model that captures these dynamics. In the model, a pollen tube senses a difference in the fraction of receptors bound to an attractant and changes its direction of growth in response; the attractant is continuously released from ovules and spreads isotropically on the medium. The model suggests that the observed slowing greatly enhances the ability of pollen tubes to successfully target ovules. The relation of the results to guidance in vivo is discussed.

Model for how retrograde actin flow regulates adhesion traction stresses.

Cells from animals adhere to and exert mechanical forces on their surroundings. Cells must control these forces for many biological processes, and dysfunction can lead to pathologies. How the actions of molecules within a cell are coordinated to regulate the adhesive interaction with the extracellular matrix remains poorly understood. It has been observed that cytoplasmic proteins that link integrin cell-surface receptors with the actin cytoskeleton flow with varying rates from the leading edge toward the center of a cell. Here, we explore theoretically how measurable subcellular traction stresses depend on the local speed of retrograde actin flow. In the model, forces result from the stretching of molecular complexes in response to the drag from the flow; because these complexes break with extension-dependent kinetics, the flow results in a decrease in their number when sufficiently large. Competition between these two effects naturally gives rise to a clutch-like behavior and a nonmonotonic trend in the measured stresses, consistent with recent data for epithelial cells. We use this basic framework to evaluate slip and catch bond mechanisms for integrins; better fits of experimental data are obtained with a catch bond representation. Extension of the model to one comprising multiple molecular interfaces shifts the peak stress to higher speeds. Connections to other models and cell movement are discussed.

How the nature of an observation affects single-trajectory entropies.

Projection of a Markov process with constant rates of transition to a small number of observable aggregated states can result in complex kinetics with memory. Here, we define the entropy production along a single sequence of aggregated states and show that it obeys detailed and integral fluctuation theorems. More importantly, we prove that projection shifts the distribution of entropy production over the ensemble of paths for a nonequilibrium process toward one characteristic of a system at equilibrium. This statement represents an analog of the second law of thermodynamics for path-dependent entropies and thus a new form of constraint of irreversible systems. Numeric examples are presented to illustrate these ideas.

Umbrella sampling for nonequilibrium processes.

The authors introduce an algorithm for determining the steady-state probability distribution of an ergodic system arbitrarily far from equilibrium. By enforcing equal sampling of different regions of phase space, as in umbrella sampling simulations of systems at equilibrium, low probability regions are explored to a much greater extent than in physically weighted simulations. The algorithm can be used to accumulate joint statistics for an arbitrary number of order parameters for a system governed by any stochastic dynamics. They demonstrate the efficiency of the algorithm by applying it to a model of a genetic toggle switch which evolves irreversibly according to a continuous time Monte Carlo procedure.

Elucidating the mechanism of nucleation near the gas-liquid spinodal.

We have constructed a multidimensional free energy surface of nucleation of the liquid phase from the parent supercooled and supersaturated vapor phase near the gas-liquid spinodal. In particular, we remove the Becker-Doring constraint of having only one growing cluster in the system. Close to the spinodal, the free energy, as a function of the size of the largest cluster, develops surprisingly a minimum at a subcritical cluster size. It is this minimum at intermediate size that is found to be responsible for the barrier towards further growth of the nucleus at large supersaturation. An alternative free energy pathway involving the participation of many subcritical clusters is found near the spinodal where the growth of the nucleus is promoted by a coalescence mechanism. The growth of the stable phase becomes collective and spatially diffuse, and the significance of a "critical nucleus" is lost for deeper quenches.

Orientation-dependent potential of mean force for protein folding.

We present a solvent-implicit minimalistic model potential among the amino acid residues of proteins, obtained by using the known native structures [deposited in the Protein Data Bank (PDB)]. In this model, the amino acid side chains are represented by a single ellipsoidal site, defined by the group of atoms about the center of mass of the side chain. These ellipsoidal sites interact with other sites through an orientation-dependent interaction potential which we construct in the following fashion. First, the site-site potential of mean force (PMF) between heavy atoms is calculated [following F. Melo and E. Feytsman, J. Mol. Biol. 267, 207 (1997)] from statistics of their distance separation obtained from crystal structures. These site-site potentials are then used to calculate the distance and the orientation-dependent potential between side chains of all the amino acid residues (AAR). The distance and orientation dependencies show several interesting results. For example, we find that the PMF between two hydrophobic AARs, such as phenylalanine, is strongly attractive at short distances (after the obvious repulsive region at very short separation) and is characterized by a deep minimum, for specific orientations. For the interaction between two hydrophilic AARs, such a deep minimum is absent and in addition, the potential interestingly reveals the combined effect of polar (charge) and hydrophobic interactions among some of these AARs. The effectiveness of our potential has been tested by calculating the Z-scores for a large set of proteins. The calculated Z-scores show high negative values for most of them, signifying the success of the potential to identify the native structure from among a large number of its decoy states.