Institute for Medical Physics and Biophysics, Leipzig University, Härtelstr. 16-18, D-04107 Leipzig, Germany.
Comput Biol Chem. 2022 Oct;100:107750. doi: 10.1016/j.compbiolchem.2022.107750. Epub 2022 Aug 3.
H 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 H and C NMR. We aim to remedy this situation by introducing a model based on graph neural networks (GNN) capable of predicting H 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/.
NMR 核磁各向异性序参数是磷脂膜中酰基链的重要指标，它反映了分子对膜有序性、流动性和通透性的影响。到目前为止，对于每种类型的小分子和特定类型的膜，评估程序都是逐个案例的研究。如果有可能对各种药物的影响进行快速筛选，那将是非常有价值的。不幸的是，到目前为止，还没有像 H 和 C NMR 等其他实验参数那样具有实用或理论方法。我们的目标是通过引入基于图神经网络 (GNN) 的模型来解决这一问题，该模型能够根据所学到的分子特征，预测不同分子存在时脂质膜的 H NMR 核磁各向异性序参数。这些参数的快速预测将允许快速评估药物对脂质膜的潜在影响，这对于进一步的药物开发很重要，并提供了对潜在副作用的深入了解。我们得出结论，本文提出的基于图网络的模型可以以足够的精度预测序参数，我们有信心所提出的概念是未来研究的合适基础。我们还将模型作为网络应用程序在 https://proteinformatics.uni-leipzig.de/g2r/ 上提供给公众。
