Engineering Sortase; Activity and Selectivity of New Hybrid and Ancestral Variants of Sortase A

Bacterial sortase enzymes are a beneficial tool in innovative mechanisms of protein engineering. However, important limitations to utilization of sortases for engineered purposes exist; namely, that sortase A (SrtA) is a relatively poor enzyme and very specific for the substrate containing LPATXG motif. Exciting previous work from our collaborators reveals that sortases from different species recognize different sequences and that activity can vary. Therefore, we wanted to create and investigate hybrid sortase enzymes between SrtA from S. aureus and Streptococcus pneumoniae, wherein we swapped a substrate-interacting loop between the beta-E and beta-F strands. Our hypothesis is that these residues are responsible for the differing specificities of these two enzymes and that our loop-swapped S. aureus enzyme will show S. pneumoniae sequence selectivity, and vice versa. In addition to the creation of loop swapped complexes, we have also used ancestral sequence reconstruction (ASR) to investigate the specificity and activity of ancestral sortase sequences in gram negative bacteria. We have engineered two ancestral sortase complexes, ancestral SrtAstaph and SrtAstrep using ASR of sequences obtained from NCBI. Our hypothesis is that SrtAstaph and SrtAstrep will be more active, promiscuous enzymes than their extant relatives. Here, we present activity and selectivity data for our loop swapped variants, as well as our ancestral enzymes, in comparison to the two native S. aureus and S. pneumoniae SrtA enzymes. The discovery or development of a more promiscuous sortase enzyme could lead to more efficient mechanisms of protein engineering then are currently available to researchers

Engineering Class A Sortases: Activity and Selectivity of Hybrid and Ancestral Variants

Bacterial sortases are cysteine transpeptidases that anchor virulence factors to the surface of bacterial cells. Sortases are a powerful tool utilized for protein engineering that allow researchers to modify proteins at the protein level, not the DNA level. However, important limitations to utilization of sortases for engineering purposes exist; namely, SrtA from S. aureus is a relatively modest enzyme compared to other SrtA enzymes and is very specific for the LPXTG motif. Previous work from our collaborators and others revealed that sortases from different species can recognize alternative sequences and that activities can vary widely. We were curious about how natural sequence variation in class A sortases affects activity and selectivity. To that end, a principle component analysis revealed that the structurally conserved beta7-beta8 substrate-interacting loop region may be a key component in substrate recognition and activity. We investigated this in two ways, by engineering eight S. pneumoniae beta7-beta8 loop variants with loop sequences from different bacterial species and by performing ancestral sequence reconstruction on extant class A sortase sequences. We then assayed all of our variants and found a SrtA construct, SPSfaec (S. pneumoniae core with a beta7-beta8 substrate-interacting loop from E. faecalis) which not only possessed an enhanced substrate promiscuity profile, recognizing seven 5th position substrates LPATGG, LPATSG, LPATAG, LPATVG, LPATTG, LPATNG, and LPATFG, but also displayed improved catalytic efficiency for all six of these substrates compared to the WT enzymes SrtA from S. aureus and SrtA from S. pneumoniae. Overall our engineered constructs provide further insight into the role of this beta7-beta8 substrate-interacting loop in class A sortases and provide additional framework for the design of sortases for future engineering purposes

Advanced imaging techniques for detection of Barrett’s neoplasia

Patients with Barrett’s esophagus, in which the normal lining of the esophageal wall has been replaced under influence of chronic acid reflux, have an increased risk of developing esophageal adenocarcinoma. Barrett’s patients therefore undergo regular endoscopic surveillance. When esophageal cancer is detected in an early stage it can be treated endoscopically with an excellent prognosis. However, early cancer has a subtle appearance and can therefore be missed. To increase neoplasia detection in Barrett’s esophagus (BE), many advanced endoscopic imaging techniques have been developed over the past decades. Several of these techniques have been discussed in this thesis, including optical chromoscopy, magnification endoscopy and volumetric laser endomicroscopy (VLE). In this thesis, we have investigated the application of these novel imaging techniques for detection and characterization of BE neoplasia. We described three major challenges of Barrett surveillance: 1) a subjective interpretation of endoscopic imagery by endoscopists, 2) the increase of available information within a Barrett segment, and 3) sampling error associated with random biopsies. To address these challenges, application of artificial intelligence (AI) was explored for enhanced endoscopic image interpretation. Additionally, VLE was studied, which offers the possibility to visualize the different esophageal wall layers and thereby guide targeted biopsies. Finally, we developed an AI-tool to aid in the interpretation of the complex information within VLE imagery in order to improve BE neoplasia detection. Future studies should evaluate our AI-tools during live clinical procedures

Investigation of the evolution of bacterial sortase and PDZ domain selectivity determinants

Our research group is broadly interested in peptide-binding domains and how only a small number of amino acids are recognized in a given interaction. Specifically, we are focused on the PDZ domain, which is important in signaling and trafficking pathways in the cell. There are over 200 PDZ domains in the human proteome, making it the largest family of peptide-binding domains. Defined motifs include only a couple of positions along the peptide-binding cleft, and do not accurately define the overlapping yet distinct preferences among family members. Our research group will work to understand the selectivity determinants of PDZ domains throughout evolution. We will use biochemistry and structural biology to investigate PDZ domains from extant species, as well as by using ancestral protein reconstruction. We are also interested in the selectivity determinants of other peptide-binding domains, e.g., the SH2 domain, which binds phosphorylated tyrosine-containing peptides