Exploring a predictive paradigm for olfactory receptor-odorant interactions

描述（由申请人提供）：除了实验挑战之外，嗅觉受体的功能分析中的主要缺点之一是气味分子与嗅觉受体相互作用的机制未知。 为了解决这个问题，这是一个黑盒子（实验），计算方法被使用，这本身是充满了技术困难，主要的困难是缺乏一个实验衍生结构的嗅觉受体。 我们将： 使用计算方法学来研究OR和气味配体之间的相互作用，以探索这些相互作用的预测范式：1）应用严格的OR蛋白质模型创建协议; 2）在OR的结合区域内对接气味配体; 3）对OR和气味配体之间的相互作用进行分子动力学模拟。4）研究OR结合区的电子性质，预测哪些气味分子可能与哪些OR相互作用。 二. 创建一个数据库OR-ModelDB，它将包含与OR的计算模型以及OR和气味之间的相互作用相关的信息。 该资源将利用成功的ORDB和OdorDB的数据库和Web架构，并位于耶鲁大学医学院的SenseLab数据库套件中。 OR-ModelDB将作为OR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中提交他们已发表和正在发展的模型。 在支持期间，我们建议在人嗅觉受体OR 17 -210上测试我们的上述方案。 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其激发。 PI领导的一项研究表明，这种OR保持功能，对哺乳动物的OR进化具有潜在的重要性，并且具有在其他OR中未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：我们建议使用计算机模拟研究嗅觉受体和气味配体之间的长程，动态的相互作用。 我们还将研究OR绑定口袋的电子特性。 我们将开发Odor-ModelDB，一个OR计算结构模型的存储库。

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.