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，将外部信号（例如，药物结合）转化为细胞内部过程的激活。受体的三维形状根据与之结合的分子及其外部环境（例如受伤与正常组织与正常组织）变化。为了采样GPCR的所有可能构象，使用了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结合在一起。这种新型方法应用于阿片类受体，这是GPCR的突出亚科。这些受体介导受伤和发炎的组织中的疼痛缓解，但在健康组织中也有不良的副作用，例如大脑呼吸或镇静。阿片类药物受体因发炎的环境而改变其构象。如果有选择性地针对这种“病理”形式的阿片类药物受体，它们会在健康环境中治疗急性疼痛而不会影响受体，从而避免对常规阿片类药物产生的不良副作用。将来可以使用这种方法来发现仅影响受伤组织中阿片类药物受体的更安全的止痛药。分子动力学模拟与人工智能的创新组合可以使大量阿片受体构象的硅中进行采样。有效的仿真将利用最近开发的人工神经网络方法，该方法有效地代表了高维分子相互作用的动力学。相应的数学理论不仅限于分子模拟。原则上，它可以适用于任何生成的马尔可夫流程。分子模拟将洞悉环境因素（例如pH和自由基的存在）的影响，并将用于暗示实验室中的实验。计算模型将使用阿片受体突变体和可以将受体锁定到活性状态的抗体进行测试。使用蛋白质设计和酵母显示技术的组合，将生成模仿G蛋白亚基的抗体和微动蛋白。一旦建立了阿片类药物受体的方法，它们可能会扩展到参与其他神经系统疾病的GPCR。最终，新型计算方法与体外实验的结合将使对健康与受伤环境中的GPCR信号进行系统的研究。一个伴侣项目由联邦教育和研究部德国（BMBF）（BMBF）提供资金。该奖项反映了NSF的法定任务，并通过评估范围来进行评估，并通过评估商标进行了支持和宽广的基础。