SBIR Phase I: Multivariate Analysis of Heterologous Protein Expression

这项小企业创新研究（SBIR）第一阶段的研究将建立一种快速的方法来鉴定一种适合的异源蛋白生产系统。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。当发现一个生产系统时，由于时间和成本的限制以及我们目前对关键参数的有限理解，它通常远未优化。我们建议使用机器学习方法来识别最佳蛋白质表达的关键变量及其相关性。在许多相关参数中系统地变化的测试基因集将被合成并用于细菌系统中的蛋白质生产。然后，将对蛋白质生产进行调查，以生成多维序列表达景观，并对其进行建模。最终的模型将被整合到合成基因设计软件中。这项研究的更广泛的影响是使在易于培养和操作的生物体中以经济有效的方式生产有价值的治疗蛋白成为可能。包括基因合成在内的最新技术极大地提高了我们识别、分离、研究和设计有价值蛋白质的能力，扩大了候选蛋白质商业化的领域。为了抓住这个巨大且不断扩大的市场的潜力，我们必须有可靠的方法在异源系统中生产蛋白质。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。

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.