Research

Bioinformatics is by nature a collection of diverse by interconnected biological paradigms, each interwoven into the others. Sequence alignments form the fabric of comparative genomics, protein structure prediction, phylogenetic studies, and interaction networks, each of which provide unique perspectives of the others. It can be understood that no one area of bioinformatics, can be studied without learning something of the others. By the same token, studying alternate areas of bioinformatics reinforces ones core studies. With this in mind, my core research interests lie in structural bioinformatics, more specifically protein interactions.

Proteins are involved in nearly all aspects of biological life. How, when, and with what they interact influences and often determines the characteristics of life and its subsystems. In my studies I attempted to address the first question – How do these proteins interact?

Zinc finger protein domains provide an excellent model system for studying protein/DNA interactions. They are the most abundant DNA binding motif in eukaryotes, have many solved three-dimensional structures, seem to follow a basic although somewhat discordant rule-set, and hold extensive promise in the treatment of disease.

C2H2 zinc fingers, name so for their two Cysteine and two Histidine residues which coordinate their zinc atoms, are a particularly well studied subfamily of the zinc finger family. It’s DNA binding motif appears to be modular with largely 1:1 amino acid/nucleic acid interactions. Better understanding the properties of these modules, how they interact, and the ideal properties of a zinc finger protein, will provide us with the ability to design many zinc finger proteins capable of binding unique DNA sites as well as distinguish target DNA sites which would provide characteristics appropriate for a given task.

While zinc finger proteins provide insight into one mode of protein/DNA interactions, it is only a small piece of a very large puzzle. To gain a better understanding of the larger picture of protein/nucleic acid interactions, I am using machine-learning techniques to identify which amino acid residues interact using both sequence and structural information. As we enhance our understanding of protein interactions, we will be able to better understand biological systems, pathogenic infections, genetic diseases, and their cures.

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