ScRiPPt: Computational and Experimental Characterization of RiPP Graspetide Synthetases

Protein-protein interactions (PPIs) are the basis of several human diseases. The characterization and engineering of peptides can expand our ability to target these interactions, thus providing a platform for the design of treatments for various diseases. Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a large class of naturally occurring compounds that can be exploited for these purposes. Specifically, the RiPP subclass known as graspetides has been shown to display various biological activities and topologies that can be exploited for downstream drug design. Previous efforts to explore the graspetide synthetases and their products have been hindered by a lack of structural information regarding the active site and core sequence residue interactions. In this project, we utilized a bifold in silico and experimental approach for the structural elucidation and functional characterization of graspetides and their synthetases. Machine learning prediction algorithms and molecular dynamics simulations were used to guide the experimental generation of computationally designed mutants in an attempt to characterize the cyclization mechanisms of graspetide synthetase ThtC. The dual-fold computational and experimental approach revealed contacts that play key roles in the modification of the precursor peptide substrate. Furthermore, a combinatorial design algorithm aimed at improving protein stability was utilized to explore the relationship between graspetide synthetase nucleotide sequence and soluble expression. This approach allowed for the identification of three variant sequences that can be assessed for large-scale production of enzymes for applications in engineering peptide products for the perturbation of disease-associated PPIs.