ePACE: an automated system for high-throughput, closed-loop control of continuous molecular evolution to enable novel therapeutics

ePACE：一种自动化系统，用于高通量、闭环控制连续分子进化，以实现新型疗法

• 批准号: 9925776
• 负责人: Ahmad Samir Khalil
• 金额: $ 62.86万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-03 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9925776
• 关键词: Address; Algorithms; Amino Acyl-tRNA Synthetases; Apoptosis; Bacteriophage M13; Bacteriophages; Biological; Bontoxilysin; Botulinum Toxin Type A; CASP1 gene; CRISPR/Cas technology; Case Study; Caspase; Cleaved cell; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer software; Couples; DNA Binding; DNA-Directed RNA Polymerase; Data; Devices; Directed Molecular Evolution; Evolution; Family; Genetic Diseases; Genome; Goals; Individual; Laboratories; Life Cycle Stages; Liquid substance; Manuals; Medical; Methods; Molecular; Molecular Evolution; Mutagenesis; Nucleic Acids; Online Systems; Outcome; Peptide Hydrolases; Population; Positioning Attribute; Property; Proteins; Route; Site; Specificity; Standardization; System; Technology; Testing; Therapeutic; Time; Variant; Vial device; Viral; Work; cancer therapy; cell growth; cost; design; experience; experimental study; genome editing; human disease; method development; next generation; novel; novel therapeutics; open source; prevent; programs; promoter; rapid technique; real time monitoring; success; synthetic biology; therapeutic protein; therapeutic target

PROJECT SUMMARY/ABSTRACT The recent development of methods that allow continuous laboratory evolution of biomolecules has made it increasingly possible to generate proteins with new, tailored activities for next-generation therapeutics. In particular, phage-assisted continuous evolution (PACE), a method that allows proteins to undergo directed evolution at a rate of ~100-fold faster than conventional methods, has recently been used to evolve new activities in a number of proteins, including RNA polymerases, Cas9 proteins, and viral proteases. While these early applications illustrate the potential of the PACE system, there remain intrinsic technical barriers that limit the success rate, efficiency, and wider application of PACE for creating highly selective, designer molecular therapeutics. The first barrier is the exceedingly low throughput with which PACE experiments can be conducted in parallel, which greatly limits the number of evolutionary trajectories that can be assessed and prohibits large-scale evolution of variants with diverse specificities/activities. The second is an inability to precisely and dynamically control PACE selection conditions (positive and negative), which is critical for fine- tuning properties such as the selectivity of evolved proteins and for achieving successful PACE outcomes. We propose to overcome these barriers by developing an automated, high-throughput system for PACE with individual, real-time monitoring and control over selection conditions (ePACE). To accomplish this goal, we will adapt eVOLVER, a scalable do-it-yourself (DIY) framework we recently invented that uniquely enables scaling both throughput (>100 vials) and individual programmable control of culture conditions during continuous cell growth. Leveraging the highly modular and open source wetware, hardware, and web-based software of eVOLVER will allow us to develop ePACE with a projected throughput ~50-100-fold greater than current PACE technology, with setup costs of >10-fold lower, and the capability of programming real-time, algorithmically- driven modulation of selection conditions to comprehensively explore directed evolution landscapes. We will then demonstrate the ePACE system in two directed evolution case studies that specifically highlight and test the benefits of our enhanced functionalities. The first study will apply the high-throughput capabilities of ePACE to perform multiplex evolution of Cas9 (CRISPR) variants with compatibility for every possible PAM sequence, a large scale evolution that is impractical for traditional PACE. In the second study, we will apply adaptive (closed-loop) selection stringency modulation to the traditionally challenging problem of reprogramming proteases toward new, intracellular therapeutic targets. This effort will seek to acquire a Botulinum neurotoxin protease variant capable of selectively cleaving caspase-1, toward an ultimate goal of a deliverable, caspase- activing protease for potential cancer therapies. This work will provide a standardized, democratic, and powerful platform to streamline and expand the scope of directed evolution methods for rapidly creating new molecular entities and therapeutics.

项目概要/摘要 最近开发的允许生物分子在实验室中持续进化的方法已使其成为可能 越来越有可能为下一代疗法产生具有新的、定制的活性的蛋白质。在 特别是噬菌体辅助连续进化（PACE），一种允许蛋白质进行定向进化的方法 进化速度比传统方法快约 100 倍，最近已被用来进化新的方法 许多蛋白质的活性，包括 RNA 聚合酶、Cas9 蛋白质和病毒蛋白酶。虽然这些 早期应用说明了 PACE 系统的潜力，但仍然存在限制其的固有技术障碍 PACE 在创建高选择性、设计分子方面的成功率、效率和更广泛的应用 疗法。第一个障碍是 PACE 实验的通量极低 并行进行，这极大地限制了可以评估和评估的进化轨迹的数量 禁止具有不同特性/活性的变体的大规模进化。第二个是没有能力 精确、动态地控制 PACE 选择条件（正和负），这对于精细化至关重要 调整特性，例如进化蛋白质的选择性以及实现成功的 PACE 结果。我们 建议通过开发自动化、高通量的 PACE 系统来克服这些障碍 对选择条件进行单独、实时监控和控制 (ePACE)。为了实现这一目标，我们将 采用 eVOLVER，这是我们最近发明的一个可扩展的 DIY 框架，它能够以独特的方式实现扩展 连续细胞期间培养条件的吞吐量（> 100 瓶）和单独的可编程控制 生长。利用高度模块化和开源的湿件、硬件和基于网络的软件 eVOLVER 将使我们能够开发 ePACE，预计吞吐量比当前 PACE 高 50-100 倍 技术，设置成本降低 10 倍以上，并且具有实时编程的能力，算法 驱动选择条件的调节以全面探索定向进化景观。我们将 然后在两个定向进化案例研究中展示 ePACE 系统，这些案例特别强调和测试 我们增强功能的好处。第一项研究将应用 ePACE 的高通量功能 执行 Cas9 (CRISPR) 变体的多重进化，并兼容每个可能的 PAM 序列， 对于传统 PACE 来说是不切实际的大规模演变。在第二项研究中，我们将应用自适应 针对传统上具有挑战性的重编程问题的（闭环）选择严格性调制 蛋白酶针对新的细胞内治疗靶点。这项工作将寻求获得肉毒杆菌神经毒素 能够选择性裂解 caspase-1 的蛋白酶变体，以实现可交付的 caspase-1 的最终目标 激活蛋白酶用于潜在的癌症治疗。这项工作将提供一个规范的、民主的、 强大的平台，可简化和扩展定向进化方法的范围，以快速创建新的 分子实体和疗法。