SBIR Phase I: Programmable Intracellular Sensors for Direct In Vivo Screening of Target Molecule Production in Yeast

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是开发一种工具，允许更快速地筛选工程酵母菌株，以生产所需的生化化合物。特种化学品、食品、能源、个人护理和制药等行业越来越多地使用工程微生物，特别是酵母，用于生物化学生产。菌株工程的一个关键挑战是筛选。 为了找到指导菌株有效生产目标分子的最佳遗传变化，公司必须构建和筛选大量菌株。目前使用自动化的最佳实践允许公司以每株约1 -5美元的成本筛选菌株，每天的吞吐量为数百至一千株。所提出的酵母传感器能够对酵母菌株进行超高通量筛选，允许每天以低于0.00002美元的成本测量数千万株菌株。这项技术不仅将大大提高应变工程项目的经济性和成功率，而且还将允许探索更复杂的设计空间，并实现其他棘手的项目。该SBIR第一阶段项目旨在创建一个平台，用于快速设计酵母中的生物传感器，这些生物传感器能够感测和响应任何所需的分子。细胞已经进化出大量的感觉蛋白，使它们能够与环境动态地相互作用。 拟议的技术是重新设计这些天然生物传感器，以感知和响应具有商业或科学意义的化学物质。所提出的方法在计算上模拟每个传感器和靶分子的相互作用，预测改善与所需分子结合的蛋白质突变。然后可以快速构建和测试大量这些预测的传感器，识别酵母中具有必要的传感和响应特性的传感器。由此产生的传感器将允许通过改变酵母细胞行为来响应目标化学物质来快速工程化酵母菌株，使其成为在菌株工程，诊断和合成生物学中具有广泛应用的强大工具。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to develop a tool to allow for more rapid screening of engineered yeast strains for the production of desirable biochemical compounds. Industries such as specialty chemicals, food, energy, personal care, and pharmaceuticals are increasingly using engineered microorganisms, especially yeast, for biochemical production. A key challenge in strain engineering is screening. In order to find the optimal genetic changes that direct a strain to produce the target molecule efficiently, companies have to build and screen large numbers of strains. Current best practices using automation allow companies to screen strains at a cost of approximately $1-5 per strain with a throughput of hundreds to a thousand strains per day. The proposed yeast sensors enable ultra high-throughput screening of yeast strains, allowing the measurement of tens of millions of strains per day at a cost below $0.00002 per strain. This technology will not only substantially improve the economics and success rate of strain engineering projects, but it will allow the exploration of much more complex design spaces and enable otherwise intractable projects. This SBIR Phase I project proposes to create a platform for the rapid engineering of designer biosensors in yeast that are capable of sensing and responding to any desired molecule. Cells have evolved a large number of sensory proteins that allow them to dynamically interact with their environment. The proposed technology is to re-engineer these natural biosensors to sense and respond to chemicals of commercial or scientific interest. The proposed approach computationally models the interaction of each sensor and target molecule, predicting protein mutations that improve binding to the desired molecule. It is possible to then rapidly construct and test vast numbers of these predicted sensors, identifying those with the requisite sensing and response characteristics in yeast. The resulting sensors will allow the rapid engineering of yeast strains by altering yeast cell behavior in response to the target chemical, making them powerful tools that have broad applications in strain engineering, diagnostics, and synthetic biology.