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Predicting the effect of lipid structure on mesophase formation during in meso crystallization

机译：预测中相结晶过程中脂质结构对中间相形成的影响

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摘要

Bicontinuous cubic lipidic materials are increasingly used as crystallization media for in meso crystallization of membrane proteins (MPs). Varying the lipid architecture may assist with encapsulation of larger proteins and promote crystal growth. However, not all lipids are compatible with the components of typical crystallization screens, and compatibility must therefore be checked prior to crystallization trials. The method currently used, high-throughput small-angle X-ray scattering (HT SAXS), may be time-consuming and is costly in valuable MP. We have therefore employed a modeling approach using Bayesian regularized neural networks to accurately predict the complex phase behavior of lipid materials under the influence of the PACT crystallization screen and determine the lipid characteristics that allow a lipid to retain a cubic phase under the multiple components required during an in meso crystallization trial. This information will be used to select robust lipids for use in crystallization trials and may allow for the rational design of new lipids, specifically for in meso crystallization.
机译：双连续立方脂质材料越来越多地用作膜蛋白（MP）的中观结晶的结晶介质。改变脂质结构可有助于较大蛋白的包封并促进晶体生长。但是，并非所有脂质都与典型结晶筛的组分相容，因此必须在结晶试验之前检查相容性。当前使用的方法，高通量小角度X射线散射（HT SAXS），可能很耗时，而且在有价值的MP中成本很高。因此，我们采用了一种使用贝叶斯正则化神经网络的建模方法来准确预测在PACT结晶筛选的影响下脂质材料的复杂相行为，并确定允许脂质在过程中所需的多种组分下保留立方相的脂质特性中观结晶试验。该信息将用于选择用于结晶试验的坚固脂质，并可能允许合理设计新脂质，特别是用于中观结晶。

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