In biology, understanding the past can give us insights to better understand the present. The goal of the work proposed in this grant application is to develop integrated sequence, function, and structure approaches to study evolution of all three aspects of individual proteins and the networks they form. Unique tools for protein structure alignment and comparison developed in our group allow us not only to identify common structural elements of pairs and groups of structures, but also to describe and classify differences in the structures. Such analyses can provide us with insights not only into the function-related structural rearrangements, but also into evolutionary changes in the sequence caused by evolutionary drift or function-driven changes. At the same time, analysis of the evolution of protein networks can give us synergistic insights into evolution of functions of the same proteins. In this grant we propose to expand such analyses and integrate them across all three aspects with the help and new tools for such analyses we are planning to develop. Understanding of molecular details of the evolution of protein families, such as identification of regions that are strongly conserved in evolution versus malleable regions undergoing extensive changes with no effect on the function or identification of new functional regions emerging at specific moments in evolution, can give us insights into evolution of various cellular processes. At the same time, such analyses can help us answer practical questions such as effects of disease mutations or responses to drugs.
The goal of our proposed work is to study the evolution of structures, sequences, and functions in extended protein families and protein networks. In biology, The past can be used to understand the present-patterns of evolution in protein families can provide insights into easily mutated regions in these proteins, ways specific proteins react to mutations, and ways networks respond to changes in the proteins forming them. Successful implementation of the research plan described here would provide insights to better understanding the effects of disease mutations, as well as organisms' responses to drugs, including drug resistance of pathogens and in diseases such as cancer.
| Porta-Pardo, Eduard; Kamburov, Atanas; Tamborero, David et al. (2017) Comparison of algorithms for the detection of cancer drivers at subgene resolution. Nat Methods 14:782-788 |
