SBIR Phase I: Multivariate Analysis of Heterologous Protein Expression

SBIR 第一阶段：异源蛋白表达的多变量分析

• 批准号: 0638333
• 负责人: Claes Gustafsson
• 金额: $ 10万
• 依托单位: DNA Twopointo Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0638333&HistoricalAwards=false
• 关键词: SBIR; Phase; Multivariate; Analysis; Heterologous

This Small Business Innovation Research (SBIR) Phase I reserach will establish a reapid method for identification of a suitable production system for a heterologous protein. This is often a time-consuming trial-and-error process and can significantly hinder commercialization of a protein. Commercial production of a protein generally requires the use of an organism other than the natural source, a so-called heterologous host. When a production system is found, it is often far from optimized due to prohibitive time and cost and our limited current understanding of the critical parameters. We propose to use a machine learning approach to identify the critical variables and their correlations for optimal protein expression. Test gene sets systematically varied in a number of relevant parameters will be synthesized and used for protein production in a bacterial system. Protein production will then be surveyed to generate a multidimensional sequence-expression landscape, which will be modeled. The resulting model will then be incorporated into synthetic gene design software. The broader impact of this research is to enable simple means for cost-effective production of valuable therapeutic proteins in easily cultured and manipulated organisms. Recent technologies, including gene synthesis, have greatly improved our ability to recognize, isolate, study, and engineer proteins of value, expanding the field of candidate proteins for commercialization. To capture the potential of this immense and expanding market, we must have reliable means to produce proteins in heterologous systems. Gene synthesis allows us to systematically approach this problem. We expect the tools and correlations we gain from this project to drastically improve the speed, reduce the cost and remove the uncertainties of modern protein manufacturing.

这项小型企业创新研究（SBIR）I期研究将建立一种快速方法，用于鉴定异源蛋白的合适生产系统。 这通常是一个耗时的试错过程，并可能严重阻碍蛋白质的商业化。 蛋白质的商业生产通常需要使用天然来源以外的生物体，即所谓的异源宿主。 当一个生产系统被发现时，由于时间和成本的限制以及我们目前对关键参数的了解有限，它往往远远没有优化。 我们建议使用机器学习方法来识别关键变量及其相关性，以实现最佳蛋白质表达。将合成在许多相关参数中系统地变化的测试基因集，并用于细菌系统中的蛋白质生产。 然后将调查蛋白质生产，以生成一个多维序列表达景观，这将被建模。然后将得到的模型并入合成基因设计软件中。 这项研究的更广泛的影响是使简单的方法，在容易培养和操纵的生物体中具有成本效益的生产有价值的治疗蛋白质。最近的技术，包括基因合成，大大提高了我们识别，分离，研究和工程蛋白质的价值，扩大了商业化候选蛋白质的领域。为了抓住这个巨大和不断扩大的市场的潜力，我们必须有可靠的方法来生产蛋白质在异源系统。 基因合成使我们能够系统地解决这个问题。 我们希望我们从这个项目中获得的工具和相关性能够大大提高速度，降低成本并消除现代蛋白质制造的不确定性。