Design of protease-resistant peptide ligands for the purification of antibodies from human plasma.

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2016-05

Authors

Menegatti, Stefano
Bobay, Benjamin G
Ward, Kevin L
Islam, Tuhidul
Kish, William S
Naik, Amith D
Carbonell, Ruben G

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Abstract

A strategy is presented for developing variants of peptide ligands with enhanced biochemical stability for the purification of antibodies from animal sera. Antibody-binding sequences HWRGWV, HYFKFD, and HFRRHL, previously discovered by our group, were modified with non-natural amino acids to gain resistance to proteolysis, while maintaining target affinity and selectivity. As trypsin and α-chymotrypsin were chosen as models of natural proteolytic enzymes, the basic (arginine and lysine) and aromatic (tryptophan, phenylalanine, and tyrosine) amino acids were replaced with non-natural analogs. Using the docking software HADDOCK, a virtual library of peptide variants was designed and screened in-silico against the known HWRGWV binding site on the pFc fragment of IgG. A pool of selected sequences with the highest predicted free energy of binding was synthesized on chromatographic resin, and the resulting adsorbents were tested for IgG binding and resistance to proteases. The ligand variants exhibited binding capacities and specificities comparable to the original sequences, yet with much higher proteolytic resistances. The sequences HWMetCitGWMetV and HFMetCitCitHL was used for purifying polyclonal IgG from IgG-rich fractions of human plasma, with yields and purity above 90%. Notably, due to electrical neutrality, the variant showed higher selectivity than the original sequence. Binding isotherms were also constructed, which confirmed the docking predictions. This method represents a general strategy for enhancing the biochemical stability as well as the affinity and selectivity of natural or synthetic peptide ligands for bioseparations.

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Journal article

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Subjects

Animals, Humans, Chymotrypsin, Trypsin, Peptides, Antibodies, Ligands, Blood Chemical Analysis, Chromatography, Affinity, Binding Sites, Protein Binding

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Published Version (Please cite this version)

10.1016/j.chroma.2016.03.087

Publication Info

Menegatti, Stefano, Benjamin G Bobay, Kevin L Ward, Tuhidul Islam, William S Kish, Amith D Naik and Ruben G Carbonell (2016). Design of protease-resistant peptide ligands for the purification of antibodies from human plasma. Journal of chromatography. A, 1445. pp. 93–104. 10.1016/j.chroma.2016.03.087 Retrieved from https://hdl.handle.net/10161/28905.

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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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