Developing a computational platform for induced-fit and chemogenetic drug design

项目总结 处方阿片类药物治疗在多种急慢性疼痛的临床治疗中起着至关重要的作用 设置。有效的疼痛管理的挑战已经导致美国200多万成年人和12多万人 全球有100万人患有阿片类药物使用障碍(OUD)。OUD每年在全球造成超过12万人死亡。 治疗类阿片的主要靶点是µ-阿片受体(MOR)。吗啡的镇痛作用 激动剂是由G-α、I/o/z-蛋白信号转导引起的，已有研究提出MOR的不良副作用 激动剂，如呼吸抑制和耐受，可以通过部分招募Gi/o/z-来缓解。 蛋白质亚型。因此，确定MOR信号与细胞周期的关系具有重要的临床意义。 止痛与副作用的对比，以指导选择性激活所需治疗激动剂的设计 信号通路。G蛋白偶联受体(GPCRs)，包括MOR，已知采用了一系列 在使用正构体调节器和/或细胞内效应器时不同的功能不同的配置 蛋白质。这些诱导配对的结构重排不能用现有的计算机辅助药物建模 由于所需的时间和资源，在对接或设计期间的发现算法。 这项提议的目标是开发一种可定制的、多用途的计算机辅助药物 可高效模拟大规模诱导匹配构象变化的设计(CADD)平台 在小分子和/或受体序列设计期间。建议的完成将使以下结构得以实现- 基于偏向激动剂和DREADD的设计(由Designer独家激活的Designer受体 毒品)。该提案将包括利用深度学习蛋白质结构预测的创新算法 快速筛选可综合访问的方法和超大型按需制作化学药库 那些可以诱导构象变化的分子，需要激活G-蛋白信号转导 莫尔。在合作中，我将合成(克雷格·林德斯利博士)，功能验证(克雷格·林德斯利博士，海蒂 哈姆和Vsevolod Gurevich)，并在结构上描述了(Dr.Beili Wu和Matthias Elgeti)设计的 分子和DREADD。实验验证的部分和偏向激动剂和DREADD将被反馈 进入计算平台，作为后续几轮优化的起点。就这样， 我们将建立一个计算-实验迭代反馈回路。

PROJECT SUMMARY Prescription opioid therapy plays a critical role in the clinical management of pain in multiple acute and chronic settings. The challenges of effective pain management have led to over 2 million adults in the US, and over 12 million globally, with an opioid use disorder (OUD). OUD accounts for over 120,000 deaths annually worldwide. The dominant target of therapeutic opioids is the µ-opioid receptor (MOR). The analgesic effects of MOR agonists are due to Gα,i/o/z-protein signaling, and it has been proposed that undesirable side-effects of MOR agonists, such as respiratory depression and tolerance, can be mitigated through partial recruitment of Gi/o/z- protein subtypes. Thus, it is of clinical interest to determine the relationship between MOR signaling and analgesia versus side-effects to guide the design of therapeutic agonists that selectively activate the desired signaling pathway. G-protein coupled receptors (GPCRs), including MOR, are known to adopt a range of different functionally distinct configurations upon engaging orthosteric modulators and/or intracellular effector proteins. These induced-fit structural rearrangements cannot be modeled with existing computer-aided drug discovery algorithms during docking or design due to the time and resources required. It is the objective of this proposal to develop a customizable, multi-purpose computer-aided drug design (CADD) platform that can efficiently model largescale induced-fit conformational changes during small molecule and/or receptor sequence design. Completion of the proposal will enable structure- based design of biased agonists and DREADDs (Designer Receptors Exclusively Activated by Designer Drugs). This proposal will include innovative algorithms that leverage deep learning protein structure prediction methods and ultra-large make-on-demand chemical libraries to rapidly screen synthetically accessible molecules for those that can induce conformational changes required to activate G¬i¬-protein signaling in MOR. In collaboration, I will synthesize (Dr. Craig Lindsley), functionally validate (Drs. Craig Lindsley, Heidi Hamm, and Vsevolod Gurevich), and structurally characterize (Drs. Beili Wu and Matthias Elgeti) designed molecules and DREADDs. Experimentally validated partial and biased agonists and DREADDs will be fed back into the computational platform to be used as starting points for subsequent rounds of optimization. In this way, we will establish a computational-experimental iterative feedback loop.