ABI Innovation: Tunable Perturbation of Proteins and Pathways

贝勒医学院获得了一笔赠款，用于开发合理的方法来操纵细胞通路。这些途径是由蛋白质网络通过功能相互作用连接在一起的。一种典型的途径工程方法是敲除一个基因，这相当于立即切断连接该蛋白质与其途径的每一条链路--通常会导致没有信息的重大网络中断。更可取的分析方法是以受控的方式一次干扰一条特定的网络链路。这个项目介绍了几项创新。首先，对以前的工作进行了扩展，以确定蛋白质中的功能应力点。这使得能够选择氨基酸，在这些氨基酸上，取代可以有效地改变功能，而不会导致大规模的错误折叠。其次，介绍了模拟特定氨基酸取代所引起的功能重新校准的新的形式论。第三个创新是在大肠杆菌模型系统中，通过跟踪DNA修复和SOS反应途径中基因表达的变化，通过实验测试和提炼这种蛋白质重新校准。这项工作将加深我们对蛋白质功能的理解，它将把这些分子细节与整个细胞网络的更大规模的行为联系起来，从而将分子水平的扰动与系统水平的行为联系起来。它还将引入一种新的理论，有选择地控制和修改复杂网络的不同部分。尽管这将在特定的途径和特定的生物体中进行测试，但所产生的计算工具应该同样适用于任何其他细胞网络，并将使蛋白质网络的系统和受控扰动成为可能，从而更好地理解网络控制。所有这些工具都将在网站(http://mammoth.bcm.tmc.edu).)上提供最后，这项工作是朝着一个基本的生物学问题迈出的一步，即如何将高通量方法产生的大量原始数据转化为生物学见解。该项目将结合计算科学和生物学，培养高中生、本科生和研究生，包括支持少数民族教育和研究的项目。

A grant is awarded to Baylor College of Medicine to develop rational approaches to manipulate cellular pathways. These pathways are made of networks of proteins linked together by functional interactions. A typical pathway engineering approach would be to knock out a gene, which is equivalent to severing at once every link connecting that protein to its pathway -- often causing major network disruptions that are uninformative. A more desirable, analytical approach is to perturb specific network links in a controlled fashion, one at a time. This project introduces several innovations. First, previous work is expanded to identify functional stress points in proteins. This enables selection of amino acids at which substitutions efficiently modify function without causing wholesale misfolding. Second, novel formalisms are introduced that model functional recalibration due to specific amino acid substitutions. A third innovation is to test and refine this protein recalibration experimentally, in an E.coli model system, by following changes in DNA repair and in gene expression in the SOS response pathway. This work will deepen our understanding protein function, and it will connect these molecular details to the larger scale behavior of entire cellular networks, thereby bridging molecular level perturbation with systems level behavior. It will also introduce a new theory to control and modify selectively different parts of a complex network. Although this will be tested in a specific pathway, and in a specific organism, the computational tools produced should apply equally well to any other cell network, and will enable the systematic and controlled perturbation of protein networks, leading to better understanding of network control. All of these tools will be available on a website (http://mammoth.bcm.tmc.edu). Finally, this work is a step towards a fundamental biological problem of how to translate massive amounts of raw data produced by high-throughput methods into biological insights. This project, at the interface of computational science and biology, will train high school, undergraduate and graduate students, including programs that support minority education and research.