We propose the development of computational tools that promote the progress of antimicrobial peptide engineering. Drug-resistant pathogens are becoming a significant public health concern. The prolific use of antibiotics in the last few decades inevitably increased the bacterial species with resistance. Antimicrobial peptides (AMPs), recognized as potent components of eukaryotic innate immune response mechanisms, appear to be promising therapeutic anti-pathogen agents. A wide array of experiments suggests a complex interplay between the bacterial cell envelope components and the peptides. However, how exactly AMPs modulate membrane structure remains largely unclear. We leverage the high resolution, atomic level picture of molecular dynamics simulations, to understand the interactions of AMPs with bacterial and mammalian membranes. We also develop data mining algorithms that identify recurring sequence and structural patterns in known, naturally occurring AMPs. Importantly, active collaborations with leading research groups in the areas of antimicrobial peptides and peptide/membrane interactions provide the necessary feedback mechanism for validation and refinement of computational results. The three specific aims of this project are: 1. Quantify the interactions between AMPs and mammalian, bacterial and viral model membranes using high productivity computer simulations. 2. Recognize the sequence/structural elements that are responsible for cathelicidin and minidefensin antimicrobial activity 3. Establish a process of feedback mechanisms between experiments, computer models and new experimental design in order to promote rational peptide engineering. Experimentally investigate novel peptides based on model-driven design rules.
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