Exploring a predictive paradigm for olfactory receptor-odorant interactions

描述(申请人提供)：除实验挑战外，嗅觉感受器功能分析的主要缺陷之一是气味分子与嗅觉感受器相互作用的机制尚不清楚。为了解决这个问题，这是一个黑箱(实验)，使用了计算方法，这本身就充满了技术困难，主要困难是缺乏一个实验得出的嗅觉感受器结构。我们将：1.使用计算方法研究OR与气味配体之间的相互作用，以探索这些相互作用的预测范例：1)应用严格的OR蛋白质模型创建协议；2)将气味配体对接在OR的结合区内；3)对OR与气味配体之间的相互作用进行分子动力学模拟；以及4)研究OR结合区的电子特性，以预测哪些气味分子可能与哪些OR相互作用。创建一个数据库，OR-ModelDB，它将存储与OR的计算模型以及OR与气味之间的相互作用相关的信息。这一资源将利用成功的ORDB和OdorDB的数据库和网络架构，并存放在耶鲁大学医学院的SenseLab数据库套件中。OR-ModelDB将作为OR的计算结构模型的全球存储库。我们将鼓励其他计算结构生物学家在OR-ModelDB中提交他们已发表的和不断发展的模型。在支持期间，我们建议在人类嗅觉感受器OR17-210上测试我们的上述协议。这种基因组假基因已被证明与几种气味配体相互作用并被激发。PI领导的一项研究表明，这种OR维持功能，对哺乳动物之间的OR进化具有潜在的重要意义，并且具有其他OR中没有观察到的序列结构特征。这里开发和测试的方法将扩展到其他OR。 公共卫生相关性：我们建议使用计算模拟研究来研究嗅觉受体和气味配体之间的远程动态相互作用。我们还将研究OR捆绑口袋的电子特性。我们将开发Odor-ModelDB，这是一个OR的计算结构模型库。

DESCRIPTION (provided by applicant): One of the major drawbacks in the functional analysis of olfactory receptors, other than experimental challenges, is that the mechanism by which an odor molecule interacts with the olfactory receptor is not known. To address this issue, which is a black-box (experimentally), computational methodologies are used, which themselves are fraught with technical difficulties, the primary difficulty being the lack of an experimentally derived structure of olfactory receptors. We will: I. Use computational methodologies to study the interactions between ORs and odor ligands in order to explore a predictive paradigm for these interactions by: 1) applying a rigorously OR protein model creating protocol; 2) docking odor ligands within the binding region of the OR; 3) performing molecular dynamics simulations of the interactions between the ORs and odor ligands.; and, 4) studying the electronic character of the binding region of OR to predict which odorant molecules are likely to interact with which ORs. II. Create a database, OR-ModelDB, which will house information related to computational models of ORs and interactions between OR and odors. This resource will leverage the database and web architecture of the successful ORDB and OdorDB and be housed within the SenseLab suite of databases at the Yale University School of Medicine. OR-ModelDB will serve as a worldwide repository for computational structural models of ORs. We will encourage other computational structural biologists to submit their published and evolving models in OR-ModelDB. During the period of support, we propose to test our above protocols on a human olfactory receptor OR17-210. This genomic pseudogene has been shown to interact with and is excited by several odor ligands. The PI led a study, which showed that this OR maintains function, is potentially important to OR-evolution among mammals and has sequence structural features not observed in other ORs. The methodologies developed and tested here will be extended to other ORs. PUBLIC HEALTH RELEVANCE: We propose to use computational simulation studies to study the long-range, dynamic interactions between olfactory receptors and odor ligands. We will also study the electronic character of OR binding pockets. We will develop Odor-ModelDB, a repository of computational structural models of ORs.