Bacteria are an important type of human pathogen that can cause life-threatening infections. Increasingly, these microorganisms can survive the effects of antibiotics previously used to kill them. As bacteria become resistant to multiple kinds of antibiotics, the diseases they cause become ever more difficult to cure. Accordingly, infections caused by ‘multidrug-resistant’ (MDR) pathogens are associated with frequent treatment failures, high hospitalization costs, and substantial mortality. New therapeutics are needed to treat infections caused by MDR bacteria. Towards developing these critical countermeasures, our group has discovered a unique peptide that efficiently kills many of the most challenging antibiotic-resistant pathogens and also demonstrates therapeutic efficacy in pre-clinical animal models of bacterial infection. Interested in investigating the effect of replacing the canonical-amino acids by non-canonical amino acids (NCAA) to increase the efficacy of the peptide, a DAC team member has created a computational strategy to perform the screening of multiple-peptide positions using a NCAA peptide library and the high-performance computing capabilities of Research Computing. With good agreement between computational predictions and bench-top experiments, our collaboration with the DAC led to the following key points:
- Determination of NCAA-containing preferred peptides with better predicted binding (under experimental testing)
- Construction of the first structural model of the peptide (both canonical and non-canonical variants) with the potential bacterial target
- Structure-function insights in search of optimized antimicrobial peptides towards better therapeutics (by utilizing NCAAs)
These investigations have set the stage for our continued collaboration with the DAC team member in this area including multiple state and federal grants that we will be targeting in 2025.
PIs: Matthew A Crawford, PhD (Department of Medicine, Division of Infectious Diseases & International Health) and Molly A Hughes, PhD (Department of Medicine, Division of Infectious Diseases & International Health)
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projects
data, data-science, drug-discovery, hpc, parallel-computing