Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM.

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as protein folding, interconversion between folded states, ligand binding, and allosteric regulation. Accurate quantification of these ensembles remains a challenge for conventional molecular simulations of all but the simplest molecules due to insufficient sampling. Enhanced sampling approaches, such as metadynamics, were designed to overcome this challenge; however, the nonuniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of nonuniform frame weights into a structural clustering method entitled shapeGMM. The result of frame-weighted shapeGMM is a high dimensional probability density and generative model for the unbiased system from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a shapeGMM probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the underlying free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantifying biomolecular ensembles from biased simulations.

Phenylpropanoids on the Inhibition of ß-Amyloid Aggregation and the Movement of These Molecules through the POPC Lipid Bilayer.

Alzheimer's disease (AD), caused by Aß aggregation, is a major concern in medical research. It is a neurodegenerative disorder, leading to a loss of cognitive abilities, which is still claiming the lives of many people all over the world. This poses a challenge before the scientific community to discover effective drugs which can prevent such toxic aggregation. Recent experimental findings suggest the potency of two naturally-occurring phenylpropanoids, Schizotenuin A (SCH) and Lycopic Acid B (LAB) which can effectively combat the deleterious effects of Aß aggregation, although nothing is known about their mechanism of inhibition. In this work, we deal with an extensive computational study on the inhibitory effects of these inhibitors by using an all-atom molecular dynamics simulation to interpret the underlying mechanism of their inhibitory processes. A series of investigations is carried out while studying the various structural and conformational changes of the peptide chains in the absence and presence of inhibitors. To investigate the details of the interactions between the peptide residues and inhibitors, nonbonding energy calculations, the radial distribution function, the coordination number of water and inhibitor molecules around the peptide residues, and hydrogen-bonding interactions are calculated. The potential of mean force (PMF) is calculated to estimate aggregate formation from their free-energy profiles. It is seen that the hydrophobic core of the KLVFFAE undergoes aggregation and that these inhibitors show great promise in preventing the onset of AD in the future by preventing Aß aggregation. Also, the translocation studies on these inhibitors through a model POPC lipid bilayer shed light on their permeation properties and biocompatibility.