Abstract
Understanding the molecular mechanisms of pore formation is crucial for elucidating fundamental biological processes and developing therapeutic strategies, such as the design of drug delivery systems and antimicrobial agents. Although experimental methods can provide valuable information, they often lack the temporal and spatial resolution necessary to fully capture the dynamic stages of pore formation. In this study, we present two novel collective variables (CVs) designed to characterize membrane pore behavior, particularly its energetics, through molecular dynamics (MD) simulations. The first CV─termed Full-Path─effectively tracks both the nucleation and expansion phases of pore formation. The second CV─called Rapid─is tailored to accurately assess pore expansion in the limit of large pores, providing quick and reliable method for evaluating membrane line tension under various conditions. Our results clearly demonstrate that the line tension predictions from both our CVs are in excellent agreement. Moreover, these predictions align qualitatively with available experimental data. Specifically, they reflect higher line tension of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine (POPS) lipids compared to pure POPC, the decrease in line tension of POPC vesicles as the 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) content increases, and higher line tension when ionic concentration is increased. Notably, these experimental trends are accurately captured only by the all-atom CHARMM36 and prosECCo75 force fields. In contrast, the all-atom Slipids force field, along with the coarse-grained Martini 2.2, Martini 2.2 polarizable, and Martini 3 models, show varying degrees of agreement with experiments. Our developed CVs can be adapted to various MD simulation engines for studying pore formation, with potential implications in membrane biophysics. They are also applicable to simulations involving external agents, offering an efficient alternative to existing methodologies.