Efficient prediction of transmembrane binding sites for anesthetic ligands

许多麻醉剂通过与包埋在包裹细胞的脂质膜中的蛋白质结合来发挥作用。 这些蛋白质，包括受体和离子通道，允许细胞协调它们在全身的活动。 要在原子水平上解释与这些蛋白质的结合如何导致麻醉，就需要知道 配体所结合的蛋白质确定这是一个困难的问题，可以通过各种方法来解决。 方法，实验和计算。当真正的结合位点是 在蛋白质的一部分，实际上是在脂质膜（跨膜结构域），因为 脂质环境的复杂性。计算方法来预测这些网站，可以准确地治疗 膜（例如溢流分子动力学）也是低效的。但更有效的方法，特别是 分子对接，不适当地纳入膜的效果。 该项目旨在专门改善对接，以便它可以预测麻醉配体结合位点， 跨膜结构域。总体目标是创建和校准对接评分函数， 通过进行某些一次性预处理步骤，将脂质考虑在内。这将通过以下方式实现： 1)预测复杂脂质膜的微结构。脂质膜由以下物质组成： 许多不同的脂质类型，虽然这些脂质的比例是已知的，但它们的排列方式 在原子水平上，它不是。这将使用长时间尺度的分子动力学来预测 模拟 2)计算所选麻醉剂在这些中的插入的自由能分布， 微架构有必要知道所讨论的配体存在于配体中的有利程度。 脂质膜与蛋白质分开，因此配体自由能曲线作为深度的函数， 膜，以及配体旋转，将被计算。 3)鉴定目标蛋白上的疏水区域。传统的对接假设， 蛋白质在水中完全溶剂化。非均相溶剂化理论将用于识别 疏水区域不含水，因此它们可以被适当地处理。 4)构建由该数据参数化的修改的对接评分函数。数据 将被拟合到有效的多项式函数 评分功能 该项目完成后，将大大改进这一具体但 重要用例。它也将有助于提高PI在未来解决类似问题的能力， 为他作为一名独立的物理学家和科学家的成功职业生涯做好准备。

Many anesthetics exert their action by binding to proteins embedded in the lipid membranes that encase cells. These proteins, including receptors and ion channels, allow cells to coordinate their action across the body. Explaining at the atomic level how binding to these proteins results in anesthesia requires knowing where on the protein the ligand actually binds. Determining this is a difficult problem that can be addressed with various methods, experimental and computational. The problem is made more difficult when the true binding sites are on a part of the protein that is actually in the lipid membrane (transmembrane domains), because of the complexity of the lipid environment. Computational methods to predict these sites that can accurately treat the membrane (e.g. flooding molecular dynamics) are also inefficient. But more efficient methods, particularly molecular docking, do not properly incorporate the effect of the membrane. This project seeks to improve docking specifically so it can predict anesthetic ligand binding sites in transmembrane domains. The overall goal is to create and calibrate a docking scoring function that takes the lipids into account, by conducting certain one-time preprocessing steps. This will be done by: 1) Predicting the microarchitecture of complex lipid membranes. Lipid membranes are composed of many different lipid types, and while the proportions of these lipids are known, the way they arrange themselves at the atomic level is not. This will be predicted using long-timescale molecular dynamics simulations. 2) Calculating the free energy profiles of insertion of selected anesthetics in these microarchitectures. It is necessary to know how favorable it is for the ligand in question to exist in the lipid membrane separately from the protein, so ligand free energy profiles as a function of depth in the membrane, as well as ligand rotation, will be calculated. 3) Identifying hydrophobic regions on the protein of interest. Traditional docking assumes that the protein is entirely solvated in water. Inhomogeneous solvation theory will be used to identify hydrophobic regions that do not contain water so they may be treated appropriately. 4) Constructing a modified docking scoring function that is parameterized by this data. The data calculated above will be fit to an efficient polynomial function for supplementing an existing docking scoring function. The project, by its completion, will have substantially improved docking methodology for this specific but important use case. It also will have served to improve the PI's ability to attack similar problems in the future, preparing him for a successful career as an independent physician-scientist.