Embracing Biological Complexity in Atomistic Simulations of Cellular Membranes

Abstract

In all biological systems, complexity is a defining feature that is inherently coupled to function. For example, the function of cellular membranes relies, in part, on an intricate interplay between their geometries and their lipid and protein compositions. Molecular dynamics (MD) simulations have the power to reveal the atomistic structural basis of this interplay, but modeling cellular membranes has previously been intractable due to their inherent complexity and immense spatial scale. New software we have developed called xMAS (Experimentally-Derived Membranes of Arbitrary Shape) Builder overcomes these challenges. Starting from experimental data (e.g., structures from electron microscopy and lipid compositions from chromatography), xMAS Builder builds realistic cellular membrane models through the application of a series of automated, robust, and efficient modeling algorithms (e.g., to appropriately place lipids and proteins in the models). Using xMAS Builder, we have built the first atomistic cell-scale (∼1.9μm³) model of a helicoidal membrane structure from the endoplasmic reticulum called a Terasaki Ramp, and we have also built several models of a smaller synthetic system with equivalent complexity. From extensive MD simulations of these models, we have gained fundamental insights into the general behavior of cellular membranes, including, for example, the principles that determine the number of lipid molecules that can be accommodated by curved membranes and the natural response of cellular membranes to perturbations to their structures. As we apply xMAS Builder to other cellular membrane systems, MD simulations of the resulting models will continue to reveal how their constituent lipids and proteins work together in the context of crowded, curved environments. Complemented by other experimental and computational techniques, these simulations will enable detailed understanding of how the functions of cellular membranes are influenced by and derived from their innate biological complexity.