Computationally Designed Protein Libraries for the Selection of Novel Enzymes

用于选择新型酶的计算设计蛋白质库

基本信息

批准号：
8782772
负责人：
Grant S Murphy
金额：
$ 5.33万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2015-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8782772
关键词：
Amino Acids Binding Biological Assay Biology Biosensor Biotechnology Chemistry Codon Nucleotides Collection Computing Methodologies Couples Development Engineering Environment Enzymes Escherichia coli Evolution Fluorescence Fluorescence-Activated Cell Sorting Gene Deletion Genes Goals Green Fluorescent Proteins Growth In Vitro Knowledge Laboratories Libraries Life Medicine Methods Modeling Mutate Natural History Periodicity Pharmaceutical Preparations Phenotype Population Production Protein Engineering Proteins Proteome Recording of previous events Reporter Research Sampling Scheme Sequence Homology Signal Transduction Staging Structure Synthetic Genes System Testing Therapeutic Variant base catalyst combat combinatorial cost design design and construction directed evolution expression vector frontier high throughput screening improved in vivo innovation knockout gene novel protein folding protein misfolding public health relevance scaffold screening small molecule success

项目摘要

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.

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。新型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。新型酶可以在化学、生物学、生物技术和医学领域找到多种应用。这项研究采用了一种创新方法，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，以产生大量从头蛋白质。从头蛋白质是在实验室中完全从头开始设计的蛋白质，并非源自天然蛋白质，并且与天然存在的蛋白质不具有序列同源性。从头蛋白质作为新型酶很有吸引力，因为它们不受天然蛋白质进化历史的限制，因此它们可能更有可能发挥非生物功能或在非生物环境中发挥功能。为了实现这项研究的目标，我将通过计算设计并实验产生大型从头蛋白质库；我将使用高通量折叠报告基因检测来分离良好折叠的从头蛋白质的大型子库；我将使用筛选、选择和定向进化来识别和进化从头酶。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特征：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特征：数百万个序列的实验测试。本研究中生成的文库将用折叠报告绿色荧光蛋白 (FR-GFP) 进行标记。带有 FR-GFP 标记的折叠良好的蛋白质具有明亮的荧光，折叠不良的蛋白质具有较低的荧光。然后，荧光激活的细胞分选可用于基于荧光分离良好折叠的蛋白质群体。为了从这些文库中鉴定功能蛋白，我将筛选文库蛋白拯救条件致死性大肠杆菌基因缺失、营养缺陷型的能力。拯救营养缺陷型的从头蛋白质具有使营养缺陷型能够生存的功能。为了改善弱表型，我将使用一种新颖的定向进化方案，同时选择功能性和稳定的酶。本研究开发的计算和实验方法易于使用且高度通用，可以在研究中获得广泛接受。这项研究中获得的知识和开发的方法将使快速可靠地设计用于化学、生物学、生物技术和医学应用的新型酶成为可能。