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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.

项目总结