The ASPIRE Path in Molecular Medicine

Ribosomally synthesized and Post-translationally modified Peptides (RiPPs) offer a promising avenue for antibiotic development due to their diverse structures and bioactivities. RiPPs take advantage of ribosomal translational machinery to generate linear peptides that are then subsequently modified by “tailoring modifications”. RiPP modifying enzymes are responsible for these post-translational tailoring modifications and generally identify their peptide substrates via RiPP precursor peptide recognition elements (RREs) that recognize peptide leader sequences. Modifying enzymes are selective for these leader sequences and modify RiPP cores nonspecifically, making it possible to produce RiPPs with altered core sequences. Understanding RRE-leader interactions is time consuming with traditional experimental methods but can allow for novel combinations of multiple RiPP classes. Machine learning algorithms can elucidate relationships between RREs and modifying enzymes, enabling the rapid rational design and biosynthesis of RiPP-based antibiotics. My project aims to generate a large dataset of curated RREs with matching leader peptide sequences in order to computationally design leader sequences for a variety of RiPP modifying enzymes. I will ultimately apply my computational methodology to the development of purpose-built synthetic antibiotics tailored to combat antibiotic-resistant pathogens and I hope to use the training of APMM to better integrate my research within the clinical setting.