CRCNS US-German Research Proposal: Combining computational modeling and artificial intelligence to understand receptor function in physiology and disease

CRCNS 美德研究提案：结合计算模型和人工智能来了解生理学和疾病中的受体功能

• 批准号: 2113030
• 负责人: Rob Meijers
• 金额: $ 40.92万
• 依托单位: INSTITUTE FOR PROTEIN INNOVATION, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113030&HistoricalAwards=false
• 关键词: CRCNS; US; German; Research; Proposal

G-protein coupled receptors (GPCRs) are cell surface receptors that translate external signals (e.g. binding of drugs) into the activation of processes inside the cell. The three-dimensional shape of the receptor changes depending on the molecule that is binding to it and the external environment (e.g. injured versus normal tissue). To sample all possible conformations of a GPCR, a powerful computational simulation approach is used that combines traditional molecular dynamics simulations with artificial intelligence. This novel approach is applied to opioid receptors, a prominent subfamily of GPCRs. These receptors mediate pain relief in injured and inflamed tissue, but also have adverse side effects in healthy tissue, such as depression of breathing or sedation in the brain. The opioid receptors change their conformation as a result of the inflamed environment. If there were drugs that selectively targeted this "pathological" form of opioid receptors, they would treat acute pain without affecting receptors in a healthy environment, and thereby avoid the adverse side effects observed for conventional opioids. This approach can be used in the future to discover safer pain killers that only affect opioid receptors in injured tissues. The innovative combination of molecular dynamics simulations with artificial intelligence enables the sampling in silico of large numbers of opioid receptor conformations. Efficient simulation will take advantage of a recently developed artificial neural network approach that efficiently represents the dynamics of high-dimensional molecular interactions. The corresponding mathematical theory is not restricted to molecular simulation; in principle, it could apply to any generated Markov process. The molecular simulations will provide insight into the effects of environmental factors such as pH and the presence of free radicals and will be used to suggest experiments in the laboratory. Computational models will be tested in the lab using opioid receptor mutants and antibodies that can lock the receptors into an active state. Antibodies and miniproteins mimicking G-protein subunits will be generated using a combination of protein design and yeast display technologies. Once these methods have been established for opioid receptors, they may be extended to GPCRs involved in other nervous system disorders. Ultimately, the combination of novel computational methods with in vitro experiments will enable a systematic study of GPCR signaling in healthy versus injured environments.A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

G蛋白偶联受体（GPCR）是细胞表面受体，其将外部信号（例如药物的结合）翻译成细胞内过程的激活。受体的三维形状根据与其结合的分子和外部环境（例如受损组织与正常组织）而变化。为了对GPCR的所有可能构象进行采样，使用了一种强大的计算模拟方法，该方法将传统的分子动力学模拟与人工智能相结合。这种新的方法适用于阿片受体，一个突出的亚家族的GPCR。这些受体在受伤和发炎的组织中介导疼痛缓解，但在健康组织中也有不良副作用，如呼吸抑制或大脑镇静。阿片受体由于发炎的环境而改变其构象。如果有药物选择性地靶向这种“病理”形式的阿片受体，它们将在健康环境中治疗急性疼痛而不影响受体，从而避免常规阿片类药物观察到的不良副作用。这种方法可以在未来用于发现更安全的止痛药，只影响受伤组织中的阿片受体。分子动力学模拟与人工智能的创新结合使大量阿片受体构象的计算机采样成为可能。有效的模拟将利用最近开发的人工神经网络方法，有效地代表高维分子相互作用的动力学。相应的数学理论并不局限于分子模拟;原则上，它可以应用于任何生成的马尔可夫过程。分子模拟将深入了解pH值和自由基存在等环境因素的影响，并将用于建议实验室中的实验。计算模型将在实验室中使用阿片受体突变体和抗体进行测试，这些抗体可以将受体锁定在活跃状态。将使用蛋白质设计和酵母展示技术的组合产生模拟G蛋白亚基的抗体和微蛋白。一旦这些方法已经建立了阿片受体，他们可能会扩展到涉及其他神经系统疾病的GPCR。最终，新的计算方法与体外实验的结合将使健康与受伤环境中GPCR信号的系统研究成为可能。一个配套项目正在由德国联邦教育和研究部（BMBF）资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。