Efficient prediction of transmembrane binding sites for anesthetic ligands

有效预测麻醉配体的跨膜结合位点

基本信息

批准号：
10678957
负责人：
Thomas Thenganpallil Joseph
金额：
$ 19.69万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

项目摘要

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.

许多麻醉剂通过与嵌入细胞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，使细胞能够协调其在全身的行动。在原子水平上解释与这些蛋白质的结合如何导致麻醉需要知道在哪里配体实际结合的蛋白质。确定这个问题是一个难题，可以通过多种方式解决方法，实验和计算。当真正的结合位点是在实际上位于脂膜（跨膜结构域）的蛋白质部分上，因为脂质环境的复杂性。预测这些位点的计算方法可以准确地治疗膜（例如淹没分子动力学）也效率低下。但更有效的方法，特别是分子对接，没有正确结合膜的作用。该项目旨在专门改进对接，以便可以预测麻醉配体的结合位点跨膜结构域。总体目标是创建并校准一个对接评分函数，该函数采用通过进行某些一次性预处理步骤，将脂质考虑在内。这将通过以下方式完成： 1）预测复杂脂质膜的微结构。脂膜的组成是许多不同的脂质类型，虽然这些脂质的比例是已知的，但它们的排列方式它们本身在原子水平上不是。这将使用长期分子动力学来预测模拟。 2) 计算插入所选麻醉剂的自由能曲线微架构。有必要知道所讨论的配体存在于脂质膜与蛋白质分开，因此配体自由能分布作为深度的函数将计算膜以及配体旋转。 3) 识别感兴趣的蛋白质上的疏水区域。传统对接假设蛋白质完全溶解在水中。非均匀溶剂化理论将用于识别疏水区域不含水，因此可以对其进行适当处理。 4) 构建由该数据参数化的修改后的对接评分函数。数据上面计算的结果将适合一个有效的多项式函数，以补充现有的对接评分功能。该项目完成后，将大大改进针对这一特定但重要的用例。它还将有助于提高 PI 未来解决类似问题的能力，为他作为一名独立的医师科学家的成功职业生涯做好准备。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The blobulator: a webtool for identification and visual exploration of hydrophobic modularity in protein sequences.

blobulator：一种用于识别和视觉探索蛋白质序列中疏水模块性的网络工具。

DOI：
发表时间：
2024-01-22
期刊：
影响因子：
0
作者：
Pitman, Connor;Santiago;Lohia, Ruchi;Bassi, Kaitlin;Joseph, Thomas T;Hansen, Matthew E B;Brannigan, Grace
通讯作者：
Brannigan, Grace