[Soft Matter Café] A Python-implementation of the SDP-model for SAS-analysis of unilamellar lipid vesicles

Small-angle X-ray and neutron scattering have been popular methods to characterize trans-bilayer structures of phospholipid vesicles for several decades. The gradual improvement of data quality in large scale facilities as well as brought along models of increasing complexity, one of them being the SDP (scattering density profile)-model, published by Kučerka et al. (2008). It parses the lipid into quasimolecular fragments and describes their location within the bilayer in a probability-density based approach, which then determines the scattering length density profile for any X-ray or neutron contrast. This enables to jointly analyze X-ray and neutron data, and to include lipid-specific information from volumetric studies and molecular dynamics simulations. In a recent study (Frewein et al. 2021) we extended the original SDP-model to lower q-regions by including a vesicle form factor via the separated form factor method, as well as a headgroup-hydration layer of higher density. In this Soft Matter Café session we will present a Python software package including this extended SDP-model and the possibility to analyze data using either least-squares fitting or a Bayesian probability-based Markov chain algorithm.

About the speaker:  Moritz Frewein studied physics at the Technical University of Graz and started working in membrane biophysics in his bachelor thesis project about protein partitioning in heterogeneous lipid bilayers. In the following he was involved in the development of a theoretical model for SAXS-analysis of hexagonal lipid phases at the University of Graz. Currently, he is working on his PhD-project on the characterization of asymmetric lipid vesicles mainly by neutron and X-ray scattering under the supervision of Lionel Porcar (ILL) and Georg Pabst (Uni Graz).