Translating antibody-binding peptides into peptoid ligands with improved affinity and stability.

Abstract

A great number of protein-binding peptides are known and utilized as drugs, diagnostic reagents, and affinity ligands. Recently, however, peptide mimetics have been proposed as valuable alternative to peptides by virtue of their excellent biorecognition activity and higher biochemical stability. This poses the need to develop a strategy for translating known protein-binding peptides into peptoid analogues with comparable or better affinity. This work proposes a route for translation utilizing the IgG-binding peptide HWRGWV as reference sequence. An ensemble of peptoid analogues of HWRGWV were produced by adjusting the number and sequence arrangement of residues containing functional groups that resemble both natural and non-natural amino acids. The variants were initially screened via IgG binding tests in non-competitive mode to select candidate ligands. A set of selected peptoids were studied in silico by docking onto putative binding sites identified on the crystal structures of human IgG1, IgG2, IgG3, and IgG4 subclasses, returning values of predicted binding energy that aligned well with the binding data. Selected peptoids PL-16 and PL-22 were further characterized by binding isotherm analysis to determine maximum capacity (Qmax ˜ 48-57 mg of IgG per mL of adsorbent) and binding strength on solid phase (KD ˜ 5.4-7.8 10-7 M). Adsorbents PL-16-Workbeads and PL-22-Workbeads were used for purifying human IgG from a cell culture supernatant added with bovine serum, affording high values of IgG recovery (up to 85%) and purity (up to 98%) under optimized binding and elution conditions. Both peptoid ligands also proved to be stable against proteolytic enzymes and strong alkaline agents. Collectively, these studies form a method guiding the design of peptoid variants of cognate peptide ligands, and help addressing the challenges that, despite the structural similarity, the peptide-to-peptoid translation presents.

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Published Version (Please cite this version)

10.1016/j.chroma.2019.05.047

Publication Info

Bordelon, Tee, Benjamin Bobay, Andrew Murphy, Hannah Reese, Calvin Shanahan, Fuad Odeh, Amanda Broussard, Chad Kormos, et al. (2019). Translating antibody-binding peptides into peptoid ligands with improved affinity and stability. Journal of chromatography. A, 1602. pp. 284–299. 10.1016/j.chroma.2019.05.047 Retrieved from https://hdl.handle.net/10161/28913.

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Scholars@Duke

Bobay

Benjamin Bobay

Assistant Professor in Radiology

I am the Assistant Director of the Duke University NMR Center and an Assistant Professor in the Duke Radiology Department. I was originally trained as a structural biochemist with an emphasis on utilizing NMR and continue to use this technique daily helping collaborators characterize protein structures and small molecules through a diverse set of NMR experiments. Through the structural characterization of various proteins, from both planta and eukaryotes, I have developed a robust protocol of utilizing computational biology for describing binding events, mutations, post-translations modifications (PTMs), and/or general behavior within in silico solution scenarios. I have utilized these techniques in collaborations ranging from plant pathologists at the Swammerdam Institute for Life Sciences department at the University of Amsterdam to biomedical engineers at North Carolina State University to professors in the Pediatrics department at Duke University. These studies have centered around the structural and functional consequences of PTMs (such as phosphorylation), mutation events, truncation of multi-domain proteins, dimer pulling experiments, to screening of large databases of ligands for potential binding events. Through this combination of NMR and computational biology I have amassed 50 peer-reviewed published articles and countless roles on scientific projects, as well as the development of several tutorials concerning the creation of ligand databases and high-throughput screening of large databases utilizing several different molecular dynamic and computational docking programs.


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