November 2015


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"Diane St. Germain" <[log in to unmask]>
Fri, 20 Nov 2015 20:56:01 +0000
"Diane St. Germain" <[log in to unmask]>
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Dissertation Defense Announcement
To:  The George Mason University Community

Candidate: Daniel P. Veltri

Program: PhD in Bioinformatics & Computational Biology

Date:   Tuesday, December 1, 2015

Time:   2:00 PM
Place:  George Mason University
             Fairfax Campus
            Nguyen Engineering Building, Room 4201

Title: "A Computational and Statistical Framework for Screening Novel Antimicrobial Peptides"
Committee Chair: Dr. Jeffrey Solka
Dissertation Director: Dr. Amarda Shehu
Committee Members:  Dr. Iosif Vaisman, Dr. Benjamin Matthews

A copy of the dissertation is available in the Gateway Library.  All are invited to attend the defense.
Bacterial resistance to antibiotics continues to be a serious concern worldwide. This has motivated a strong research focus on naturally-occurring antimicrobial peptides (AMPs) as templates for new drug development. To date, experiments in the wet laboratory have characterized thousands of AMPs while generally concentrating on measures of antibacterial activity for natural sequences or peptides designed using a limited number of site-directed mutations. Based on these findings, the computational AMP research community seeks to better understand how biological signals and features relate to antimicrobial activity through the use of machine learning and statistical approaches. In this dissertation, we advance our understanding of the determinants for antimicrobial activity by carefully constructing a set of descriptive features for use in AMP classification models. In addition to using physicochemical features, we also construct new sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveal our methods to be amongst of the top performers while still providing a transparent summary of relative feature importance. Moreover, this dissertation applies our features in a new computational setting to demonstrate for the first time a model to recognize if an AMP may perform better against a representative Gram-positive or Gram-negative bacteria. Work presented is a step forward for in silico research seeking to help guide AMP design in the wet laboratory and our predictive models are made publicly-available through the new AMP Scanner web server.