Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers

Abstract

In this work, we present the first example of the self-assembly of phospholipid monolayers at the interface between air and an ionic solvent. Deep eutectic solvents are a novel class of environmentally friendly, non-aqueous, room temperature liquids with tunable properties, that have wide-ranging potential applications and are capable of promoting the self-assembly of surfactant molecules. We use a chemically-consistent Bayesian modelling of X-ray and neutron reflectometry measurements to show that these monolayers broadly behave as they do on water. This method allows for the monolayer structure to be determined, alongside the molecular volumes of the individual monolayer components, without the need for water-specific constraints to be introduced. Furthermore, using this method we are able to better understand the correlations present between parameters in the analytical model. This example of a non-aqueous phospholipid monolayer has important implications for the potential uses of these solvents and for our understanding of how biomolecules behave in the absence of water.

Assessing molecular simulation for the analysis of lipid monolayer reflectometry

“…To compare with the simulation-derived reflectometry profiles, a modified version of the chemically-consistent surfactant monolayer model previously used in the group was applied [10,36]. This model is implemented as a class that is compatible with the Python package refnx [37,38] and is made up of two layers; the head-layer at the interface with the solvent and the tail-layer at the interface with the air.…”

“…Typically, the analysis of a neutron or x-ray reflectometry profile is achieved by the application of the Abelès matrix formalism for stratified media [8,9] to a model layer structure. These layer structures are usually defined by the underlying chemistry of the system, for example, the chemically-consistent method that we previously used [10], which accounts for the chemical linkage between the phospholipid head and tail layers. However, there has been growing interest in the use of molecular dynamics (MD) simulations to inform the development of these layer structures.…”

Advice on describing Bayesian analysis of neutron and X-ray reflectometry

“…This has led to the use of Bayesian analysis, where some prior understanding of the system is used to aid our understanding of some reflectivity profile [3][4][5]. Recently, developments in the availability of computer software for reflectometry analysis that include Bayesian functionality, such as Refl1d, refnx, anaklasis, and RasCAL [6][7][8][9], which implement sampling methods from bumps, emcee, and dynesty [10][11][12], have led to an increase in the utilisation of Bayesian methods by the reflectometry community [13,14]. This work will focus on the best practice for reporting the results from Bayesian and sampling-based analysis of neutron and X-ray reflectivity data.…”

“…This can range from not reporting the priors applied to each parameter (e.g. the lower/upper limits for a uniform distribution that applies box bounds), to failing to share the complete sampling chain of a Markov Chain Monte Carlo sampling, or details of any autocorrelation analysis (the last of which, the authors of this work admit to being guilty of [13]). In this letter, we outline the best practice for reporting the results of Bayesian analysis for neutron and X-ray reflectometry, we hope that this work will engage others to carefully consider how they report this information.…”

Using Bayesian Model Selection to Advise Neutron Reflectometry Analysis from Langmuir-Blodgett Monolayers

“…There is a growing interest in the application of data science and information theory methods in reflectometry measurements; in particular for the improvement of experimental design [21] and analysis of collected data [22,23]. However, these recent works, in particular when Bayesian methods are applied, represent a resurgence of methods previously applied to reflectometry by Sivia and others in the 1990s [24][25][26][27].…”