Abstract by Kristi Bresciano

Personal Infomation

Presenter's Name

Kristi Bresciano

Degree Level



Brian Brown

Abstract Infomation


Computer Science

Faculty Advisor

Mark Clement


3M - Motif Finding in Antimicrobial Peptides


In a push to develop new antibiotics, peptides are a topic of interest. Peptides are sequences of DNA with varying lengths and a variety of properties. Some have shown to be effective in killing bacteria. Discovering the motifs, or patterns, in these toxic peptide sequences would allow us to quickly develop new antibiotics. We performed multiple analyses on peptide data to extract motifs corresponding to toxicity. Our methods used greedy algorithms, randomized algorithms, and machine learning algorithms. We compared the results of each analysis and found that all methods produced somewhat similar results; although, the machine learning algorithms could more efficiently handle the large data set while the greedy and randomized algorithms produced longer motifs.