Development of evolutionary technologies to reprogram protein-protein interactions

开发重新编程蛋白质-蛋白质相互作用的进化技术

• 批准号: 10536269
• 负责人: Matthew J Styles
• 金额: $ 6.72万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-16 至 2025-11-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10536269
• 关键词: Academia; Adopted; Adoption; Affinity; Antibodies; Area; Bacteriophages; Basic Science; Biological; Biological Assay; Biological Models; Biology; Biosensor; Cancer cell line; Cellular biology; Clinical; DNA-Directed RNA Polymerase; Data; Development; Diagnostic; Directed Molecular Evolution; Disease; Ensure; Evolution; Foundations; Future; Generations; Genetic; Genetic Transcription; Goals; Immune Evasion; Immune system; KRAS2 gene; Laboratories; Lead; Libraries; Link; MDM2 gene; Maintenance; Malignant Neoplasms; Mammalian Cell; Mass Spectrum Analysis; Medicine; Mentors; Mentorship; Methods; Molecular; Molecular Probes; Oncogenic; Output; Peptide Library; Play; Postdoctoral Fellow; Problem Solving; Process; Property; Proteins; Protocols documentation; RAF1 gene; Research; Research Personnel; Signal Transduction; System; TP53 gene; Techniques; Technology; Testing; Therapeutic; Time; Training; Translating; Translational Research; Validation; Variant; Work; base; c-myc Genes; career; career development; clinical development; cost; design; drug development; drug discovery; experience; fitness; inhibitor; innovation; member; notch protein; novel; novel therapeutics; prevent; professor; programs; protein protein interaction; skills; small molecule; success; synthetic biology; theories; therapeutic protein; therapeutic target; tool

Project Summary Alterations in protein-protein interactions (PPI) can result in dysregulated biological signaling. PPIs are critical drivers of a plethora of disease states and are increasingly recognized as important therapeutic targets. However, PPIs have been difficult to target with traditional therapeutics, and are often considered “undruggable”, because traditional small molecule discovery strategies are not well suited to identify molecules with suitable properties to disrupt PPIs. When a disease-associated PPI target is identified, translating that information into a validated molecular probe can take years, development of a clinically useful therapeutic can take decades, and even worse, in most cases these efforts fail entirely. The costs associated with developing therapeutic leads limits pursuits towards only thoroughly validated targets. A faster, less expensive, and more efficacious pipeline for PPI inhibitor discovery would allow researchers to quickly identify probes for directly perturbing PPIs in relevant model systems to assess the validity of the PPI as a therapeutic target and to generate candidate inhibitors for clinical development. While almost all areas of basic and translational biology research have benefitted from 21st century technological advances in genetic sequencing, mass spectrometry, and other diagnostics, drug discovery still largely relies on 20th century methods, which have proven to be frustratingly slow and unsuccessful in critical ways—our proposed technology aims to solve these problems. We hypothesize that continuous evolution techniques will allow us to rapidly evolve PPI inhibitors for a wide range of cytosolic protein targets. Specifically, we will pursue two key advancements to realize this broad goal. In Aim 1, we will demonstrate that non-continuous selection followed by phage-assisted continuous evolution (PACE) allows us to rapidly evolve protein binders for diverse proteins using a library-of-libraries approach. This approach will eliminate a crucial bottleneck in PACE, optimization of initial selection stringency, and bring PACE much closer to automated plug-and-play allowing for broader adoption of this technique. In Aim 2, we will construct and validate a PACE compatible biosensor linking disruption of a specific PPI to phage fitness allowing for us to directly select for PPI inhibitor function. Our goal is to demonstrate generation and validation of high potency PPI inhibitors for a given target in under 1 month. We will validate these evolution platforms using three well-studied oncogenic protein-protein interactions that have been the focus of small molecule drug development for decades: MDM2-p53, KRAS-RAF, and Myc-MAX. While this is a lofty goal, the power of evolution has long been recognized as a promising solution to this problem; we are hopeful that by merging innovative biosensor designs and continuous evolution, we can unlock the full potential of laboratory evolution. If successful, these platforms have the potential to revolutionize the drug discovery paradigm and accelerate the discovery of novel PPI inhibitors.

项目摘要 蛋白质蛋白质相互作用（PPI）的改变会导致生物信号失调。 PPI很关键 众多疾病状态的驱动因素越来越被认为是重要的治疗靶标。 但是，PPI很难针对传统疗法，并且经常被认为 “不可用”，因为传统的小分子发现策略不适合识别分子 具有合适的特性来破坏PPI。当发现与疾病相关的PPI靶标时，翻译说 进入经过验证的分子探针可能需要数年的时间，开发临床有用的疗法可以 需要数十年，甚至更糟糕的是，在大多数情况下，这些努力完全失败了。与开发相关的成本 治疗牵引线的限制仅针对彻底验证的目标。更快，更便宜，更多 PPI抑制剂发现的有效管道将使研究人员可以快速识别直接的问题 在相关模型系统中扰动PPI，以评估PPI作为治疗目标的有效性和 生成候选抑制剂进行临床发育。虽然几乎所有基本和转化生物学领域 研究受益于21世纪遗传测序的技术进步，质谱， 以及其他诊断，药物发现仍然很大程度上依赖于20世纪的方法，这些方法已被证明是 令人沮丧的缓慢和不成功的方式 - 我们提出的技术旨在解决这些问题。 我们假设连续进化技术将使我们能够快速进化PPI抑制剂 胞质蛋白靶标的范围。具体来说，我们将追求两个关键的进步来实现这一广泛目标。 在AIM 1中，我们将证明非连续选择，然后进行噬菌体辅助连续进化 （速度）使我们能够使用上库库方法快速进化为潜水蛋白的蛋白质粘合剂。 这种方法将以速度消除至关重要的瓶颈，优化初始选择的严格度，并带来 步伐更接近自动插件，从而可以更广泛地采用此技术。在AIM 2中，我们 将构建和验证一个兼容的生物传感器，将特定PPI的破坏连接到噬菌体适应性 允许我们直接选择PPI抑制剂功能。我们的目标是展示产生和验证 在1个月以下给定目标的高效力PPI抑制剂的高效力。我们将验证这些进化平台 使用三种良好的致癌蛋白 - 蛋白质相互作用，这些蛋白质相互作用已成为小分子药物的重点 几十年来开发：MDM2-P53，KRAS-RAF和MYC-MAX。虽然这是一个崇高的目标，但 长期以来，进化一直被认为是解决这个问题的希望解决方案。我们希望通过合并 创新的生物传感器设计和持续进化，我们可以释放实验室进化的全部潜力。 如果成功，这些平台有可能彻底改变药物发现范式并加速 新型PPI抑制剂的发现。