A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study.

Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently, the methods Sands et al. developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors' code, which is computationally expensive and requires technological expertise to run. We present a reimplementation and validation of the integrity of the original authors' code by reproducing the endo-Phenotyping Using Polysomnography (PUP) method of Sands et al. The original MATLAB methods were reprogrammed in Python; efficient algorithms were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p < 10-6 for all): ventilation at eupnea V̇ passive (ICC = 0.97), ventilation at arousal onset V̇ active (ICC = 0.97), loop gain (ICC = 0.96), and arousal threshold (ICC = 0.90). We successfully implemented the original PUP method by Sands et al. providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.

Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling.

The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained by taking the position-dependent diffusion coefficient into account, thus placing greater emphasis on regions diffusing slowly. Although some promising examples of applications of this approach exist, the practical usefulness of the method has been hindered by the difficulty in obtaining sufficiently accurate estimates of the diffusion coefficient. Here, we present a simple, yet robust solution to this problem. Compared to current state-of-the-art procedures, the new estimation method requires an order of magnitude fewer data to obtain reliable estimates, thus broadening the potential scope in which this technique can be applied in practice.

Local unfolding and aggregation mechanisms of SOD1: a Monte Carlo exploration.

Copper, zinc superoxide dismutase 1 (SOD1) is a ubiquitous homodimeric enzyme, whose misfolding and aggregation play a potentially key role in the neurodegenerative disease amyotrophic lateral sclerosis (ALS). SOD1 aggregation is thought to be preceded by dimer dissociation and metal loss, but the mechanisms by which the metal-free monomer aggregates remain incompletely understood. Here we use implicit solvent all-atom Monte Carlo (MC) methods to investigate the local unfolding dynamics of the ß-barrel-forming SOD1 monomer. Although event-to-event variations are large, on average, we find clear differences in dynamics among the eight strands forming the ß-barrel. Most dynamic is the eighth strand, ß8, which is located in the dimer interface of native SOD1. For the four strands in or near the dimer interface (ß1, ß2, ß7, and ß8), we perform aggregation simulations to assess the propensity of these chain segments to self-associate. We find that ß1 and ß2 readily self-associate to form intermolecular parallel ß-sheets, whereas ß8 shows a very low aggregation propensity.

Aggregate geometry in amyloid fibril nucleation.

We present and study a minimal structure-based model for the self-assembly of peptides into ordered ß-sheet-rich fibrils. The peptides are represented by unit-length sticks on a cubic lattice and interact by hydrogen bonding and hydrophobicity forces. Using Monte Carlo simulations with >10(5) peptides, we show that fibril formation occurs with sigmoidal kinetics in the model. To determine the mechanism of fibril nucleation, we compute the joint distribution in length and width of the aggregates at equilibrium, using an efficient cluster move and flat-histogram techniques. This analysis, based on simulations with 256 peptides in which aggregates form and dissolve reversibly, shows that the main free-energy barriers that a nascent fibril has to overcome are associated with changes in width.