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.

项目摘要 处方阿片类药物疗法在多重急性和慢性的疼痛临床管理中起着至关重要的作用 设置。有效疼痛管理的挑战导致了美国超过200万成年人，超过12个成年人 全球百万，有阿片类药物使用障碍（OUD）。 OUD每年在全球范围内占120,000多人死亡。 治疗性阿片类药物的主要靶标是阿片受体（MOR）。 MOR的镇痛作用 激动剂归因于Gα，I/O/Z-蛋白信号，并且已经提出了MOR的不良副作用 激动剂，例如呼吸道抑郁和耐受性，可以通过部分募集GI/O/Z-来缓解。 蛋白质亚型。这是临床感兴趣的是确定MOR信号传导与 镇痛与副作用，以指导选择性激活所需的治疗性激动剂的设计 信号通路。已知包括MOR在内的G蛋白偶联受体（GPCR）采用一系列 引人入胜的直角调节器和/或细胞内效应器时，功能上不同的配置不同 蛋白质。这些诱导的拟合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，在对接或设计过程中发现算法。 该提案的目的是开发可自定义的多功能计算机辅助药物 设计（CADD）平台，可以有效地对大规模建模诱导拟合咨询更改 在小分子和/或接收器序列设计中。提案的完成将使结构 - 基于偏见的激动剂和Dreadds的设计（设计师专门由设计师激活的设计师受体 药物）。该建议将包括利用深度学习蛋白结构预测的创新算法 方法和超大型制成化学库，可快速筛选合成的筛选 对于那些可以诱导激活g的蛋白信号传导所需构象变化的分子的分子 莫在合作中，我将合成（Craig Lindsley博士），功能验证（Heidi Craig Lindsley博士 HAMM和VSEVOLOD GUREVICH），并在结构上进行了特征（Dr.Beili Wu和Matthias Elgeti） 分子和恐怖。经过实验验证的部分和偏见的激动剂和恐惧将被喂养 进入计算平台，用作后续优化的起点。这样， 我们将建立一个计算实验性迭代反馈循环。