An Integrated Computational Framework for Optimally Allocating Diversity in Directed Evolution Studies

在定向进化研究中优化分配多样性的综合计算框架

• 批准号: 0331047
• 负责人: Costas Maranas
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0331047&HistoricalAwards=false
• 关键词: Integrated; Computational; Framework; Optimally; Allocating

The Principal Investigator's (PI) research plan proposes an integrated computational framework that will aid in enhancing the portion of combinatorial DNA libraries, generated through directed evolution methods, that codes for proteins with the desired functionality. By linking genomic information in the form of protein family member sequence data with predictive modeling frameworks, the presence of improved protein hybrid phenotypes in the combinatorial libraries will be enhanced. Directed evolution methods rely on the mixing of genetic material between an original small library of DNA sequences through recombination and/or mutagenesis. This diversity generating step produces a new expanded library spanning a much wider range of DNA sequence space.Many directed evolution success stories have been reported, ranging from peptide therapeutics to industrial enzymes. It is widely accepted that screening is the most expensive, time consuming, labor intensive and thus limiting step in directed evolution studies. This project will help alleviate this bottleneck to discovery in the fertile area of combinatorial protein engineering. By boosting the percentage of functional members in the combinatorial library, the chances of identifying novel proteins will be substantially improved while simultaneously reducing the extent of screening.

主要研究者（PI）的研究计划提出了一个综合的计算框架，这将有助于增强通过定向进化方法产生的组合DNA文库的一部分，该文库编码具有所需功能的蛋白质。 通过将蛋白质家族成员序列数据形式的基因组信息与预测建模框架相关联，将增强组合文库中改进的蛋白质杂交表型的存在。 定向进化方法依赖于通过重组和/或突变在原始小DNA序列库之间混合遗传物质。这种多样性产生步骤产生了一个新的扩展文库，跨越了更广泛的DNA序列空间。许多定向进化成功的故事已经报道，从肽治疗到工业酶。 人们普遍认为，筛选是最昂贵的，耗时，劳动密集型，从而限制了定向进化研究的步骤。该项目将有助于缓解这一瓶颈，发现在肥沃的组合蛋白质工程领域。通过提高组合文库中功能成员的百分比，将大大提高鉴定新蛋白质的机会，同时减少筛选的程度。