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

开发诱导拟合和化学遗传学药物设计的计算平台

• 批准号: 10680745
• 负责人: Benjamin Patrick Brown
• 金额: $ 47.55万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680745
• 关键词: Absence of pain sensation; Acute; Adopted; Adult; Agonist; Algorithms; Analgesics; Back; Cessation of life; Chronic; Clinical; Clinical Management; Collaborations; Computer Assisted; Dangerousness; Docking; Drug Design; Feedback; G-Protein-Coupled Receptors; Genetic; Ligands; Methods; Modeling; Molecular Conformation; Opiate Addiction; Opioid Receptor; Pain management; Pharmaceutical Preparations; Play; Property; Proteins; Rapid screening; Resources; Role; Signal Pathway; Signal Transduction; Signaling Protein; Structure; Therapeutic; Time; Ventilatory Depression; computational platform; deep learning; design; designer receptors exclusively activated by designer drugs; drug discovery; in silico; innovation; interest; lead optimization; mu opioid receptors; novel; opioid therapy; opioid use disorder; plasma protein Z; prescription opioid; protein structure prediction; receptor; recruit; side effect; small molecule; small molecule libraries; targeted treatment; therapeutic opioid; tool

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.

项目概要 处方阿片类药物治疗在多种急性和慢性疼痛的临床治疗中发挥着关键作用 美国有超过 200 万成年人和超过 12 名成年人面临有效疼痛管理的挑战。 全球每年有超过 12 万人因阿片类药物使用障碍 (OUD) 死亡。 治疗性阿片类药物的主要靶点是 µ-阿片受体 (MOR) MOR 的镇痛作用。 激动剂是由于 Gα,i/o/z 蛋白信号传导引起的，并且有人提出 MOR 的不良副作用 激动剂，如呼吸抑制和耐受性，可以通过部分补充 Gi/o/z- 来缓解 因此，确定 MOR 信号传导与蛋白质亚型之间的关系具有临床意义。 镇痛与副作用的比较，以指导选择性激活所需的治疗激动剂的设计 已知 G 蛋白偶联受体 (GPCR)，包括 MOR，采用一系列信号通路。 在接合正位调节剂和/或细胞内效应器时具有不同的功能不同的配置 这些诱导拟合的结构重排无法用现有的计算机辅助药物进行建模。 由于需要时间和资源，在对接或设计期间发现算法。 该提案的目标是开发一种可定制的、多用途的计算机辅助药物 设计（CADD）平台，可以有效地模拟大规模诱导拟合构象变化 在小分子和/或受体序列设计期间，该提案的完成将能够实现结构- 基于偏向激动剂和 DREADD（由设计师独家激活的设计师受体）的设计 该提案将包括利用深度学习蛋白质结构预测的创新算法。 方法和超大型按需生产化学库，可快速筛选合成可用的 那些可以诱导激活 G-i- 蛋白信号传导所需的构象变化的分子 MOR。在合作中，我将合成（Craig Lindsley 博士），并进行功能验证（Craig Lindsley 博士、Heidi） Hamm 和 Vsevolod Gurevich），并进行结构表征（Beili Wu 博士和 Matthias Elgeti 博士）设计 经实验验证的部分和偏向激动剂和 DREADD 将得到反馈。 进入计算平台作为后续轮次优化的起点。 我们将建立一个计算实验迭代反馈循环。