Identification, characterization and application of a new peptide against anterior gradient homolog 2 (AGR2).

The cancer-associated protein Anterior Gradient 2 (AGR2) has been described, predominantly in adenocarcinomas. Increased levels of extracellular AGR2 (eAGR2) have been correlated with poor prognosis in cancer patients, making it a potential biomarker. Additionally, neutralizing AGR2 antibodies showed preclinical effectiveness in murine cancer models suggesting eAGR2 may be a therapeutic target. We set out to identify a peptide by mRNA display that would serve as a theranostic tool targeting AGR2. This method enables the selection of peptides from a complex (>1011) library and incorporates a protease incubation step that filters the selection for serum stable peptides. We performed six successive rounds of enrichment using a 10-amino acid mRNA display library and identified several AGR2 binding peptides. One of these peptides (H10), demonstrated high affinity binding to AGR2 with a binding constant (KD) of 6.4 nM. We developed an AGR2 ELISA with the H10 peptide as the capture reagent. Our H10-based ELISA detected eAGR2 from cancer cell spent media with a detection limit of (20-50 ng/ml). Furthermore, we investigated the therapeutic utility of H10 and discovered that it inhibited cell viability at IC50 (9-12 µmoles/L) in cancer cell lines. We also determined that 10 µg/ml of H10 was sufficient to inhibit cancer cell migration in breast and prostate cancer cell lines. A control peptide did not show any appreciable activity in these cells. The H10 peptide showed promise as both a novel diagnostic and a potential therapeutic peptide.

Directed Evolution of Scanning Unnatural-Protease-Resistant (SUPR) Peptides for in Vivo Applications.

Peptides typically have poor biostabilities, and natural sequences cannot easily be converted into drug-like molecules without extensive medicinal chemistry. We have adapted mRNA display to drive the evolution of highly stable cyclic peptides while preserving target affinity. To do this, we incorporated an unnatural amino acid in an mRNA display library that was subjected to proteolysis prior to selection for function. The resulting "SUPR (scanning unnatural protease resistant) peptide" showed ≈500-fold improvement in serum stability (t1/2 =160 h) and up to 3700-fold improvement in protease resistance versus the parent sequence. We extended this approach by carrying out SUPR peptide selections against Her2-positive cells in culture. The resulting SUPR4 peptide showed low-nanomolar affinity toward Her2, excellent specificity, and selective tumor uptake in vivo. These results argue that this is a general method to design potent and stable peptides for in vivo imaging and therapy.

Serum stable natural peptides designed by mRNA display.

Peptides constructed with the 20 natural amino acids are generally considered to have little therapeutic potential because they are unstable in the presence of proteases and peptidases. However, proteolysis cleavage can be idiosyncratic, and it is possible that natural analogues of functional sequences exist that are highly resistant to cleavage. Here, we explored this idea in the context of peptides that bind to the signaling protein Gαi1. To do this, we used a two-step in vitro selection process to simultaneously select for protease resistance while retaining function-first by degrading the starting library with protease (chymotrypsin), followed by positive selection for binding via mRNA display. Starting from a pool of functional sequences, these experiments revealed peptides with 100-400 fold increases in protease resistance compared to the parental library. Surprisingly, selection for chymotrypsin resistance also resulted in similarly improved stability in human serum (~100 fold). Mechanistically, the decreases in cleavage results from both a lower rate of cleavage (kcat) and a weaker interaction with the protease (Km). Overall, our results demonstrate that the hydrolytic stability of functional, natural peptide sequences can be improved by two orders of magnitude simply by optimizing the primary sequence.