ABI Innovation: Tunable Perturbation of Proteins and Pathways

Baylor College Of Medicine, Houston TX

Investigators

Abstract

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.