Narrow Results

The free-energy landscape of two peptides is evaluated at various temperatures and an estimate for its fractal dimension at these temperatures calculated. We show that monitoring this quantity as a function of temperature allows to determine the glass transition temperature.

We have simulated, as a showcase, the pentapeptide Met-enkephalin (Tyr-Gly-Gly-Phe-Met) to visualize the energy landscape and to investigate the conformational coverage by the multicanonical method. We obtained a three-dimensional topographic picture of the whole energy landscape by plotting the histogram with respect to energy (temperature) and the order parameter, which gives the degree of resemblance of any created conformation with the global energy minimum.

The conformational transitions of the heptapeptide Deltorphin (H-Tyr¹-D-Met²-Phe³-His⁴-Leu⁵-Met⁶-Asp⁷-NH₂) are predicted by using the detailed representation of the energy landscape. The visualization of the whole rugged landscape covering the entire energy and temperature ranges is found useful for the determination of the conformational transitions and the glassy behavior of peptides.

We perform Brownian dynamics simulation (BDS) of catch to slip transition over a model energy landscape. Through our BDS we demonstrate that for forces below the critical force the bond rupture occurs mostly through the catch pathway while for forces above the critical force the bond rupture occurs mostly through the slip pathway. We also demonstrate that the shoulder in the bond rupture force distribution switches to peak as the loading rate increases progressively and the bond lifetime is maximized at the model dependent critical force. The force dependent bond lifetime obtained via transforming the bond rupture force distribution at a given loading rate is in excellent agreement with that obtained from our BDS at constant forces. An alternative to the current mechanism of catch to slip transition is presented and validated through BDS.

Molecular dynamics (MD) simulation software allows probing the equilibrium structural dynamics of a molecule of interest, revealing how a molecule navigates its structure space one structure at a time. To obtain a broader view of dynamics, typically one needs to launch many such simulations, obtaining many trajectories. A summarization of the equilibrium dynamics requires integrating the information in the various trajectories, and Markov State Models (MSM) are increasingly being used for this task. At its core, the task involves organizing the structures accessed in simulation into structural states, and then constructing a transition probability matrix revealing the transitions between states. While now considered a mature technology and widely used to summarize equilibrium dynamics, the underlying computational process in the construction of an MSM ignores energetics even though the transition of a molecule between two nearby structures in an MD trajectory is governed by the corresponding energies. In this paper, we connect theory with simulation and analysis of equilibrium dynamics. A molecule navigates the energy landscape underlying the structure space. The structural states that are identified via off-the-shelf clustering algorithms need to be connected to thermodynamically-stable and semi-stable (macro)states among which transitions can then be quantified. Leveraging recent developments in the analysis of energy landscapes that identify basins in the landscape, we evaluate the hypothesis that basins, directly tied to stable and semi-stable states, lead to better models of dynamics. Our analysis indicates that basins lead to MSMs of better quality and thus can be useful to further advance this widely-used technology for summarization of molecular equilibrium dynamics.