The emergence of pathogens resistant to available drug therapies is a pressing global
health problem. Antimicrobial peptides (AMPs) may potentially form new therapeutics to
counter these pathogens. AMPs are key components in the mammalian innate immune system
and are responsible for both direct killing and immunomodulatory effects in host defense
against pathogenic organisms. This thesis describes computational methods for the
identification of novel natural and synthetic AMPs.
A bioinformatic resource was constructed for classification and discovery of gene-
coded AMPs, consisting of a database of clustered known AMPs and a set of hidden Markov
models (HMMs). One set of 146 clusters was based on the mature peptide sequence, and one
set of 40 clusters was based on propeptide sequence. The bovine genome was analyzed using
the AMPer resources, and 27 of the 34 known bovine AMPs were identified with high
confidence and up to 69 AMPs were predicted to be novel peptides. One novel cathelicidin
AMP was experimentally verified as up-regulated in response to infection in bovine intestinal
tissue.
A chemoinformatic analysis was performed to model the antibacterial activity of short
synthetic peptides. Using high-throughput screening data for the activities of over 1400
peptides of diverse sequence, quantitative structure-activity relation (QSAR) models were
created using artificial neural networks and physical characteristics of the peptide that included
three-dimensional atomic structure. The models were used to predict the activity of a set of
approximately 100,000 peptide sequence variants. After ranking the predicted activity, the
models were shown to be very accurate. When 200 peptides were synthesized and screened
using four levels of expected activity, 94% of the top 50 peptides expected to have the highest
level of activity were found to be highly active. Several promising candidates were synthesized
with high quality and tested against several multi- antibiotic-resistant pathogens including
clinical strains of Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecalis and
Escherichia coli. These peptides were found to be highly active against these pathogens as
determined by minimal inhibitory concentration; this serves as independent confirmation of the
effectiveness of high-throughput screening and in silico analysis for identifying peptide
antibiotic drug leads.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/4613 |
| Date | 05 1900 |
| Creators | Fjell, Christopher David |
| Publisher | University of British Columbia |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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