Abstract 1990 Investigating Peptide Sorting Signal Recognition and Binding of Streptoccus pyogenes Class A Sortase by Metadynamics Simulation

Enzyme-peptide binding structures can be determined through X-ray crystallography; however, the need to make mutations or covalent bonds to trap the peptide in the catalytically active structure for crystallization and analysis is time-consuming and isn't always successful. Atomistic molecular dynamics (MD) simulations can be used to sample multiple peptide-binding conformational dynamics and provide important information about the role of molecular interactions. In this work, we performed metadynamics MD simulations of class A/B sortase enzymes with a peptide comprising its recognition sequence. Sortases recognize their substrate by an amino acid sequence, known as a motif, cleaving at this site before ligating to a fragment of the cell wall that has an amine nucleophile. This ligation process is known as sortase-mediated ligation and is utilized in protein engineering. The sortase enzymes are grouped in classes A-F, with class A being the most studied due to their higher catalytic activity. Mutational studies of this class, in addition to the other classes, are being performed to further increase the activity, but this is limited due to the lack of peptide-bound crystal structures needed to determine important interactions. With funnel metadynamics, we tested and predicted a peptide binding conformation comparable to that of the peptide-bound X-ray crystallography structure of Streptococcus pyogenese class A sortase (spySrtA). This method was replicated and identified metastable binding conformations of peptides with Streptococcus pyogenes sortase A (saSrtA) and Bacillus anthracis sortase B (baSrtB) that have yet to be structurally characterized. By performing MD simulations of binding and unbinding events, we can determine the sequence of events for sortase recognition and the role of amino acids in the variable loop region that are important for target recognition. This work was supported by funding from the NSF (CHE-2102189)