Computationally Designed Protein Libraries for the Selection of Novel Enzymes

描述（由申请人提供）：本研究的主要重点是开发用于生产新型酶的新蛋白质工程方法。新的酶可以执行生物学中未观察到的功能，并在非生物环境中执行功能。新型酶在化学、生物学、生物技术和医学中有着广泛的应用。 这项研究使用了一种创新的方法，将计算蛋白质设计方法与组合文库筛选和选择方法相结合，以产生大量的从头蛋白质。从头蛋白质是在实验室中完全从头开始工程化的蛋白质，不是源自天然蛋白质，并且与天然存在的蛋白质不具有序列同源性。从头蛋白作为新的酶是有吸引力的，因为它们不受天然蛋白进化历史的限制，因此它们可能更有可能执行非生物功能或在非生物环境中执行功能。为了实现这项研究的目标，我将通过计算设计和实验产生大量的从头蛋白质文库;我将使用高通量折叠报告基因分析来分离 我将使用筛选、选择和定向进化来鉴定和进化从头酶。 本研究中使用的混合计算和实验方法利用了计算蛋白质设计最强大的功能：快速识别有利序列空间的能力，以及文库筛选和选择方法最强大的功能：数百万序列的实验测试。本研究中产生的文库将用折叠报告绿色荧光蛋白（FR-GFP）标记。用FR-GFP标记的折叠良好的蛋白质具有明亮的荧光，而折叠不良的蛋白质具有低荧光。荧光激活的细胞分选然后可用于基于荧光分离良好折叠蛋白质的群体。为了从这些文库中鉴定功能蛋白，我将筛选lirary蛋白拯救条件致死性大肠杆菌的能力。coli基因缺失、营养缺陷型。拯救营养缺陷型的新生蛋白质具有使营养缺陷型存活的功能。为了改善弱表型，我将使用一种新的定向进化方案，同时选择功能和稳定的酶。 在这项研究中开发的计算和实验方法是直接使用和高度通用的，可以在研究中获得广泛的接受。在这项研究中获得的知识和开发的方法将使人们有可能快速，可靠地设计新的酶在化学，生物学，生物技术和医学中的应用。

DESCRIPTION (provided by applicant): The primary focus of this research is to develop new protein engineering methods for the production of novel enzymes. Novel enzymes could perform functions not observed in biology and perform functions in non-biological environments. Novel enzymes could find diverse applications in chemistry, biology, biotechnology and medicine. This research uses an innovative approach that couples computational protein design methods with combinatorial library screening and selection methods to produce large collections of de novo proteins. De novo proteins are proteins engineered completely from scratch in the laboratory, are not derived from natural proteins, and do not have sequence homology with naturally occurring proteins. De novo proteins are attractive as novel enzymes because they are not constrained by the evolutionary history of natural proteins and so they may be more likely to perform non-biological functions or to perform functions in non-biological environments. To achieve the goals of this research, I will computationally design and experimentally produce large libraries of de novo proteins; I will use a high-throughput folding reporter assay to isolate large sub-libraries of well-folded de novo proteins; and I will use screens, selections, and directed evolution to identify and evolve de novo enzymes. The mixed computational and experimental approach used in this research leverages the most powerful feature of computational protein design: the ability to rapidly identify favorable sequence space, with the most powerful feature of library screening and selection methods: the experimental testing of millions of sequences. Libraries generated in this research will be tagged with folding-reporter green fluorescent protein (FR-GFP). Well-folded proteins tagged with FR-GFP have bright fluorescence and poorly folded proteins have low fluorescence. Fluorescence activated cell-sorting can then be used to isolate populations of well-fold proteins based on fluorescence. To identify functional proteins from these libraries, I will screen the ability of lirary proteins to rescue conditionally lethal E. coli gene deletions, auxotrophs. De novo proteins that rescue auxotrophs possess a function that enables the auxotroph to live. To improve weak phenotypes, I will use a novel directed evolution scheme that simultaneous selects for functional and stable enzymes. The computational and experimental methods developed in this research are straightforward to use and highly general and could gain wide acceptance in research. The knowledge gained and methods developed in this research will make it possible to rapidly and reliably engineer novel enzymes for applications in chemistry, biology, biotechnology and medicine.