Efficient prediction of transmembrane binding sites for anesthetic ligands

许多麻醉剂通过与包裹细胞的脂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，使细胞能够协调其在全身的活动。 在原子水平上解释与这些蛋白质结合如何导致麻醉需要知道在哪里 配基实际结合的蛋白质。确定这是一个困难的问题，可以通过各种方法来解决 方法：实验和计算相结合。当真正的结合位点是 在蛋白质的一部分，实际上是在脂膜(跨膜区)上，因为 脂质环境的复杂性。计算方法来预测这些位置，可以准确地处理 膜(如泛洪分子动力学)也是低效的。但更有效的方法，特别是 分子对接时，没有适当融入膜的效果。 该项目寻求改进特定的对接，以便它可以预测麻醉配体结合部位 跨膜结构域。总体目标是创建和校准一个对接评分函数，该函数采用 通过执行某些一次性的前处理步骤，将脂质考虑在内。这将通过以下方式完成： 1)预测复合脂膜的微结构。脂膜由以下成分组成 许多不同的脂类，虽然这些脂类的比例是已知的，但它们的排列方式 在原子水平上，他们自己并不是。这将使用长时间尺度的分子动力学进行预测。 模拟。 2)计算选择的麻醉药在这些药物中的插入自由能分布 微体系结构。有必要知道有问题的配体存在于 脂膜与蛋白质分开，所以配基自由能随深度的变化而变化 膜，以及配体的旋转，将被计算。 3)鉴定目的蛋白上的疏水区。传统的对接假定 蛋白质在水中完全溶解。非均匀溶剂化理论将被用来识别 不含水的疏水区域，因此可以适当处理。 4)构造一个修正的对接评分函数，该函数由该数据进行参数化。数据 上述计算结果将适用于有效的多项式函数，以补充现有的对接 计分功能。 该项目完成后，将大大改进对接方法，用于这一特定但 重要的用例。这也将有助于提高PI在未来应对类似问题的能力， 为他作为一名独立内科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.