US-French Research Proposal: Collaborative Research: Predicting Odorant-dependent and Independent Olfactory Neuron Activation Based on Receptor

美法研究提案：合作研究：基于受体预测气味依赖和独立的嗅觉神经元激活

• 批准号: 1515801
• 负责人: Hiroaki Matsunami
• 金额: $ 27.07万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1515801&HistoricalAwards=false
• 关键词: US; French; Research; Proposal; Collaborative

Smell is a powerful sense that can trigger intense emotion, stereotyped behaviors and durable memories. The sense offers an extraordinary opportunity to connect atomic-level objects (odorant molecules and smell receptors in the nose) to neural responses. This project will predict which smell receptors in the nose are activated by a given odor. To accomplish this goal, the team of investigators will apply computational approaches to develop chemical structure-based receptor models and test these models using odor molecules interacting with olfactory receptors. The success of the project will enable the team to understand more precisely how the brain perceives the external environment. The results will also have widespread and diverse industrial applications, including rational design of new flavors and fragrances and development of new biosensors for detecting various chemicals. Furthermore, this project will make broader impacts in training and educating high school, undergraduate, and graduate students in various disciplines as well as in outreaching activities.The complexity of the odor molecules, the large number of the smell receptors and combinatorial activation of the receptors make understanding odor coding an enormous challenge. This collaborative proposal represents the first of its kind that combines computational approaches with experimental measurements at both the receptor and the neuron level. Affinity calculations between odorants and the receptors, as well as the receptors' activation, will be obtained by nanosecond-scale simulations. Atomic-level simulations, initially assessed by experiments, will predict which odors would activate the receptors of interest. Comparisons between experimental findings and computational predictions will lead to a comprehensive computational model that converges with experimental data. A companion project is being funded by the French National Research Agency (ANR).

回忆是一种强大的感觉，可以引发强烈的情绪，刻板的行为和持久的记忆。这种感觉提供了一个将原子级物体（气味分子和鼻子中的嗅觉受体）与神经反应联系起来的非凡机会。该项目将预测鼻子中的哪些嗅觉受体被给定的气味激活。为了实现这一目标，研究人员将应用计算方法来开发基于化学结构的受体模型，并使用与嗅觉受体相互作用的气味分子来测试这些模型。 该项目的成功将使团队能够更准确地了解大脑如何感知外部环境。 研究结果也将具有广泛和多样化的工业应用，包括合理设计新的香料和香料，以及开发用于检测各种化学物质的新生物传感器。此外，该项目还将在培养和教育高中生、本科生和研究生以及开展外展活动方面产生更广泛的影响。气味分子的复杂性、大量的气味受体以及受体的组合激活使理解气味编码成为一个巨大的挑战。这一合作提案是第一个将计算方法与受体和神经元水平的实验测量相结合的提案。气味物质和受体之间的亲和力计算，以及受体的激活，将通过纳秒级模拟获得。最初通过实验评估的原子级模拟将预测哪些气味会激活感兴趣的受体。实验结果和计算预测之间的比较将导致一个全面的计算模型，收敛与实验数据。一个配套项目正在由法国国家研究机构（ANR）资助。