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

首席研究员（PI）的研究计划提出了一个集成的计算框架，该框架将有助于增强通过定向进化方法生成的组合DNA文库的部分，这些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联系起来，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/或诱变在原始的小DNA序列文库之间混合遗传物质。这一多样性生成步骤产生了一个新的扩展文库，涵盖了更广泛的DNA序列空间。许多定向进化的成功故事已经被报道，从肽治疗到工业酶。人们普遍认为筛选是最昂贵、耗时、劳动密集的，因此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工程领域。通过提高组合文库中功能成员的百分比，识别新蛋白质的机会将大大提高，同时减少筛选的范围。

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.