SAS_MoCa: a software for small-angle scattering data analysis of large unilamellar vesicles

Abstract

Small-angle X-ray or neutron scattering (SAXS/SANS) analysis of large unilamellar vesicles (LUVs) is often limited by high-dimensional bilayer models and the lack of dedicated, statistically rigorous workflows. Here, we introduce SAS_MoCa, an open-source Python package that integrates a compositional scattering density profile (SDP) description of lipid bilayers with a separated form factor (SFF) treatment of vesicle size and polydispersity, and couples these highly parameterized models to an adaptive ther-modynamic simulated annealing algorithm formulated within a constrained Bayesian framework. SAS_MoCa enables users to incorporate quantitative prior information from, e.g., previous SAXS/SANS studies, dynamic light scattering, NMR, or molecular simulations, and returns full posterior parameter distributions, uncertainties (reported as medians and median absolute deviations) and correlations even from single SAXS curves. Validation on POPC, POPE and DMPC SAXS-only data demonstrates that the method yields reproducible structural parameters with uncertainties comparable to joint SAXS/contrast-variation SANS analyses. The modular architecture of SAS_MoCa facilitates extension to additional lipid systems and future joint SAXS/SANS or SANS-only applications. SynopsisSAS_MoCa is an open-source Python package for quantitative analysis of small-angle scattering (SAS) data from large unilamellar vesicles, combining a compositional scattering density profile model of lipid bilayers with a separated form factor description of vesicle size and polydispersity in a Bayesian framework. It yields full posterior parameter distributions, uncertainties and correlations from single SAS curves, enabling reproducible and extensible high-throughput membrane structural characterization.