Articles by Deborah A. Sarkes in JoVE
Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates Deborah A. Sarkes1, Justin P. Jahnke1, Dimitra N. Stratis-Cullum1 1Sensors and Electron Devices Directorate, US Army Research Laboratory Biopanning bacterial display libraries is a proven technique for discovery of peptide affinity reagents, a robust alternative to antibodies. The semi-automated sorting method herein has streamlined biopanning to decrease the occurrence of false positives. Here we illustrate the thought process and techniques applied in evaluating candidates and minimizing downstream analysis.
Other articles by Deborah A. Sarkes on PubMed
Screening and Characterization of Anti-SEB Peptides Using a Bacterial Display Library and Microfluidic Magnetic Sorting Journal of Molecular Recognition : JMR. Dec, 2014 | Pubmed ID: 25319622 Bacterial peptide display libraries enable the rapid and efficient selection of peptides that have high affinity and selectivity toward their targets. Using a 15-mer random library on the outer surface of Escherichia coli (E.coli), high-affinity peptides were selected against a staphylococcal enterotoxin B (SEB) protein after four rounds of biopanning. On-cell screening analysis of affinity and specificity were measured by flow cytometry and directly compared to the synthetic peptide, off-cell, using peptide-ELISA. DNA sequencing of the positive clones after four rounds of microfluidic magnetic sorting (MMS) revealed a common consensus sequence of (S/T)CH(Y/F)W for the SEB-binding peptides R338, R418, and R445. The consensus sequence in these bacterial display peptides has similar amino acid characteristics with SEB peptide sequences isolated from phage display. The Kd measured by peptide-ELISA off-cell was 2.4 nM for R418 and 3.0 nM for R445. The bacterial peptide display methodology using the semiautomated MMS resulted in the discovery of selective peptides with affinity for a food safety and defense threat. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
Unraveling the Roots of Selectivity of Peptide Affinity Reagents for Structurally Similar Ribosomal Inactivating Protein Derivatives Molecules (Basel, Switzerland). Nov, 2016 | Pubmed ID: 27834872 Peptide capture agents have become increasingly useful tools for a variety of sensing applications due to their ease of discovery, stability, and robustness. Despite the ability to rapidly discover candidates through biopanning bacterial display libraries and easily mature them to Protein Catalyzed Capture (PCC) agents with even higher affinity and selectivity, an ongoing challenge and critical selection criteria is that the peptide candidates and final reagent be selective enough to replace antibodies, the gold-standard across immunoassay platforms. Here, we have discovered peptide affinity reagents against abrax, a derivative of abrin with reduced toxicity. Using on-cell Fluorescence Activated Cell Sorting (FACS) assays, we show that the peptides are highly selective for abrax over RiVax, a similar derivative of ricin originally designed as a vaccine, with significant structural homology to abrax. We rank the newly discovered peptides for strongest affinity and analyze three observed consensus sequences with varying affinity and specificity. The strongest (Tier 1) consensus was FWDTWF, which is highly aromatic and hydrophobic. To better understand the observed selectivity, we use the XPairIt peptide-protein docking protocol to analyze binding location predictions of the individual Tier 1 peptides and consensus on abrax and RiVax. The binding location profiles on the two proteins are quite distinct, which we determine is due to differences in pocket size, pocket environment (including hydrophobicity and electronegativity), and steric hindrance. This study provides a model system to show that peptide capture candidates can be quite selective for a structurally similar protein system, even without further maturation, and offers an in silico method of analysis for understanding binding and down-selecting candidates.