Abstract

Generating motifs from known active sites and matching those motifs to an uncharacterized protein is a classic way of determining protein function. Until now, the generation of motifs has been based purely on enzymatic function. This approach does not account for situations where highly different active sites can arrive at the same function by processes like convergent evolution. As such, a secondary metric on which to base the generation of motifs is necessary. This metric exists in the form of UniProt designation for homologous proteins on a global scale or PFam for designation of homologous proteins at the active site level.

Here, we describe a tool to generate highly selective motifs using the aforementioned metrics. We were able to collapse a large number of proteins into their representative motifs with little loss in sensitivity, creating an “average” representation of each motif. These motifs will aid the characterizing proteins of known structure but unknown function.

Publication Date

9-24-2015

Document Type

Thesis

Student Type

Graduate

Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)

Advisor

Paul A. Craig

Advisor/Committee Member

Gary Skuse

Advisor/Committee Member

Feng Cui

Campus

RIT – Main Campus

Share

COinS