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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要蛋白质-蛋白质相互作用 (PPI) 的改变可能导致生物信号传导失调，这一点至关重要。多种疾病状态的驱动因素，并越来越被认为是重要的治疗目标。然而，质子泵抑制剂（PPI）很难用传统疗法来靶向，并且通常被认为是 “不可成药”，因为传统的小分子发现策略不太适合识别分子具有破坏 PPI 的适当特性当确定与疾病相关的 PPI 目标时，将其转化为将信息转化为经过验证的分子探针可能需要数年时间，但开发出临床上有用的治疗方法可以需要几十年的时间，更糟糕的是，在大多数情况下，这些努力完全失败，与开发相关的成本。领先限制了对治疗验证目标的追求，更快、更少、更多。 PPI 抑制剂发现的有效管道将使研究人员能够快速识别直接用于治疗的探针扰动相关模型系统中的 PPI，以评估 PPI 作为治疗目标的有效性并产生用于临床开发的候选抑制剂，同时几乎涵盖基础生物学和转化生物学的所有领域。研究受益于 21 世纪基因测序、质谱、和其他诊断学一样，药物发现仍然在很大程度上依赖于 20 世纪的方法，这些方法已被证明是令人沮丧的是缓慢且在关键方面不成功——我们提出的技术旨在解决这些问题。我们寻求持续进化技术将使我们能够快速进化 PPI 抑制剂，以适应广泛的应用。具体来说，我们将追求两项关键进展来实现这一广泛目标。在目标 1 中，我们将证明非连续选择和噬菌体辅助连续进化 (PACE) 使我们能够使用文库方法快速开发多种蛋白质的蛋白质结合剂。这种方法将消除 PACE 中的一个关键瓶颈，优化初始选择严格性，并带来 PACE 更接近自动化即插即用，允许更广泛地采用该技术。在目标 2 中，我们。将构建并验证 PACE 兼容的生物传感器，将特定 PPI 的破坏与噬菌体适应性联系起来让我们能够直接选择 PPI 抑制剂功能。我们的目标是演示生成和验证。我们将在 1 个月内验证这些进化平台。使用三种经过充分研究的致癌蛋白质-蛋白质相互作用，这些相互作用一直是小分子药物的焦点几十年来的发展：MDM2-p53、KRAS-RAF 和 Myc-MAX 虽然这是一个崇高的目标，但它的力量却很大。进化长期以来被认为是解决这个问题的一个有希望的解决方案；我们希望通过融合创新的生物传感器设计和不断的进化，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。