Predictive simulations of molecules in membranes

膜中分子的预测模拟

• 批准号: 2600186
• 金额: --
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2600186
• 关键词: Predictive; simulations; molecules; membranes

Knowledge of how a molecule interacts with biological membranes is essential in evaluating its safety and suitability for inclusion in the formulation of household products, such as those developed by the project's industrial partner, Unilever. The membrane-water partitioning coefficient (the logarithm of the equilibrium constant for division of a given solute between the membrane and aqueous solution) is used within risk assessments for estimating hydrophobic properties of molecules. However, accurate laboratory measurement of membrane-solute interactions is challenging and time-consuming. Moreover, membranes differ in composition (e.g. the mixture of phospholipids and the cholesterol content), which affects the interactions with the solute and multiplies the task of characterisation. Predictive computer simulations are an attractive alternative to experimental measurement. Simulations that represent all atoms explicitly are too slow for such complex systems, but coarse-grained (CG) models, which work at a judiciously chosen lower level of detail, can bridge the gap. This project will harness and further develop recent advances in CG modelling to produce scalable, predictive simulations of membrane-solute interactions. We will (1) exploit the increased accuracy and chemical space captured by next-generation CG force fields to build predictive models for membrane-solute partitioning, (2) systematically characterise the effect of membrane composition on membrane-solute interactions, (3) predict how membranes mediate interactions between species embedded with them. We will engage with regulatory bodies to help shape the priorities of the project and to ensure that developments from the research are exploited as widely as possible.

了解分子如何与生物膜相互作用，对于评估其安全性和是否适合纳入家用产品配方至关重要，例如该项目的工业合作伙伴联合利华开发的产品。膜-水分配系数（膜和水溶液之间分配给定溶质的平衡常数的对数）在风险评估中用于估计分子的疏水特性。然而，精确的实验室测量膜-溶质相互作用是具有挑战性的和耗时的。此外，膜的组成不同（例如磷脂和胆固醇含量的混合物），这会影响与溶质的相互作用，并增加表征的任务。预测性计算机模拟是实验测量的一种有吸引力的替代方法。明确表示所有原子的模拟对于这样复杂的系统来说太慢了，但是粗粒度（CG）模型可以在明智选择的较低细节级别上工作，可以弥合差距。该项目将利用并进一步发展CG建模的最新进展，以产生可扩展的，预测性的膜-溶质相互作用模拟。我们将（1）利用下一代CG力场捕获的更高精度和化学空间来构建膜-溶质分配的预测模型，（2）系统地验证膜组成对膜-溶质相互作用的影响，（3）预测膜如何介导嵌入它们的物种之间的相互作用。我们将与监管机构合作，帮助确定项目的优先事项，并确保尽可能广泛地利用研究成果。