Protein phosphorylation is a fundamental mechanism by which cells respond to environmental signals. In eukaryotes, protein phosphorylation is carried out by a large and diverse family of protein kinases, which display remarkable diversity and complexity in their modes of regulation. The complex modes of protein kinase regulation have evolved as a consequence of natural selection operating on genomic sequences for billions of years. Despite the availability of over 100,000 protein kinase sequences from diverse organisms, however, very little has been done to use the evolutionary information embedded in genomic sequences to understand the complexity and diversity of the protein kinase machinery. The overall goal of this project is to generate testable models of protein kinase evolution and regulation through quantitative and integrative analysis of protein kinase sequence, structure and functional data. Specific goals are to (i) quantify the sequence and structural similarities and differences between eukaryotic and distantly related eukaryotic-like kinases in prokaryotes, (ii) identify and experimentally characterize the evolutionary constraints acting on major protein kinase groups, in particular tyrosine kinases, which represent an important transition state in the evolution of the kinase domain, and (iii) provide conceptual representation of protein kinase sequence, structure and functional data in the form of an ontology. In order to accomplish these aims this project will use a novel evolutionary-systems approach in which mechanistic models of protein kinase regulation and evolution will be built and tested through quantitative and integrative analysis of sequence, structure and functional data. Accomplishing this project is expected to have a major impact by providing new testable hypotheses/models for experimental studies, a computational resource for large-scale integrative analysis of protein kinase data, and a conceptual framework for understanding the evolution of regulation in other signaling domains.
Broader Impacts: The major educational goal of this project is to help students develop an understanding of the importance of evolutionary and integrative approaches in biology. To accomplish this goal, the investigator will develop and assess new educational tools useful for teaching undergraduate and graduate students about data integration and data mining. These tools will be piloted, assessed, revised and broadly disseminated to undergraduate educators through web-resources and publications. The investigator will provide research training for undergraduate and high school students through existing training programs at the University of Georgia, and incorporate under-represented minorities in educational and research activities by partnering with the NSF REU Fungal Genomics and Computational Biology program. The educational activities of this project will promote inquiry-based learning at the high school and undergraduate level, and prepare the next generation of scientists for careers in integrative biology.
