ACS Combinatorial ScienceSynthetic BiologyPeer-reviewed journal article
Isolation of Chemically Cyclized Peptide Binders Using Yeast Surface Display
Kaitlyn B. Bacon, Abigail Blain, Matthew R. BurroughsiD, Nikki McArthuriD, Balaji M. RaoiD, Stefano MenegattiiD
Abstract
Cyclic peptides with engineered protein-binding activity have gained increasing attention for use in therapeutic and biotechnology applications. We describe the efficient isolation and characterization of cyclic peptide binders from genetically encoded combinatorial libraries using yeast surface display. Here, peptide cyclization is achieved by disuccinimidyl glutarate-mediated crosslinking of amine groups within a linear peptide sequence that is expressed as a yeast cell surface fusion. Using this approach, we first screened a library of cyclic heptapeptides by magnetic selection and fluorescence activated cell sorting (FACS), to isolate binders for a model target (lysozyme) with low micromolar binding affinity (KD ~ 1.2 - 3.7 {micro}M). The isolated peptides bound lysozyme selectively, and only when cyclized. Importantly, we showed that yeast surface displayed cyclic peptides could be used to efficiently obtain quantitative estimates of binding affinity, without chemical synthesis of the selected peptides. Subsequently, to demonstrate broader applicability of our approach, we isolated cyclic heptapeptides that bind human interleukin-17 (IL-17) using yeast-displayed IL-17 as a target for magnetic selection, followed by FACS using recombinant IL-17. Molecular docking simulations and follow-up experimental analyses identified a candidate cyclic peptide that binds IL-17 in its receptor binding region with moderate affinity (KD ~ 300 nM). Taken together, our results show that yeast surface display can be used to efficiently isolate and characterize cyclic peptides generated by chemical modification from combinatorial libraries.
- organism: yeast
- assay: proteomics
- research focus: protein structure prediction
- approach: experimental assay
- Citations
- 21
- FWCI
- 1.13
- Field percentile
- top 24%
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