Sequential Water and Headgroup Merger: Membrane Poration Paths and Energetics from MD Simulations.

Membrane topology changes such as poration, stalk formation, and hemifusion rupture are essential to cellular function, but their molecular details, energetics, and kinetics are still not fully understood. Here, we present a unified energetic and mechanistic picture of metastable pore defects in tensionless lipid membranes. We used an exhaustive committor analysis to test and select optimal reaction coordinates and also to determine the nucleation mechanism. These reaction coordinates were used to calculate free-energy landscapes that capture the full process and end states. The identified barriers agree with the committor analysis. To enable sufficient sampling of the complete transition path for our molecular dynamics simulations, we developed a "gizmo" potential biasing scheme. The simulations suggest that the essential step in the nucleation is the initial merger of lipid headgroups at the nascent pore center. To facilitate this event, an indentation pathway is energetically preferred to a hydrophobic defect. Continuous water columns that span the indentation were determined to be on-path transients that precede the nucleation barrier. This study gives a quantitative description of the nucleation mechanism and energetics of small metastable pores and illustrates a systematic approach to uncover the mechanisms of diverse cellular membrane remodeling processes.

Exploiting Lipid Permutation Symmetry to Compute Membrane Remodeling Free Energies.

A complete physical description of membrane remodeling processes, such as fusion or fission, requires knowledge of the underlying free energy landscapes, particularly in barrier regions involving collective shape changes, topological transitions, and high curvature, where Canham-Helfrich (CH) continuum descriptions may fail. To calculate these free energies using atomistic simulations, one must address not only the sampling problem due to high free energy barriers, but also an orthogonal sampling problem of combinatorial complexity stemming from the permutation symmetry of identical lipids. Here, we solve the combinatorial problem with a permutation reduction scheme to map a structural ensemble into a compact, nondegenerate subregion of configuration space, thereby permitting straightforward free energy calculations via umbrella sampling. We applied this approach, using a coarse-grained lipid model, to test the CH description of bending and found sharp increases in the bending modulus for curvature radii below 10 nm. These deviations suggest that an anharmonic bending term may be required for CH models to give quantitative energetics of highly curved states.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans.

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.