Predicting 2H NMR acyl chain order parameters with graph neural networks.

2H NMR order parameters of the acyl chain of phospholipid membranes are an important indicator of the effects of molecules on membrane order, mobility, and permeability. So far, the evaluation procedures are case-by-case studies for every type of small molecule with certain types of membranes. Rapid screening of the effects of a variety of drugs would be invaluable if it were possible. Unfortunately, to date there is no practical or theoretical approach to this as there is with other experimental parameters, e.g., chemical shifts from 1H and 13C NMR. We aim to remedy this situation by introducing a model based on graph neural networks (GNN) capable of predicting 2H NMR order parameters of lipid membranes in the presence of different molecules based on learned molecular features. Rapid prediction of these parameters would allow fast assessment of potential effects of drugs on lipid membranes, which is important for further drug development and provides insight into potential side effects. We conclude that the graph network-based model presented in this work can predict order parameters with sufficient accuracy, and we are confident that the concepts presented are a suitable basis for future research. We also make our model available to the public as a web application at https://proteinformatics.uni-leipzig.de/g2r/. [Display omitted] • 2H NMR chain order are an important tool to characterize the membrane interaction. • molecular features can be extracted by graph neural networks. • machine learning on molecular features can be used to predict membrane impact. • graph neural networks can predict 2H NMR order parameters of lipid membranes. [ABSTRACT FROM AUTHOR]