Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY
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nihms786029.pdf
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1.35 MB
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Author(s)
Reich, Lothar
Dutta, Sanjib
Keating, Amy E.
Date Issued
April 2016
Journal
Computational Design of Ligand Binding Proteins
Publisher
Humana Press
Citation
Reich, Lothar “Luther,” Sanjib Dutta, and Amy E. Keating. “Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY.” Computational Design of Ligand Binding Proteins (2016): 233–247 © 2016 Springer Science+Business Media New York
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Author's final manuscript
Abstract
Library methods are widely used to study protein-protein interactions, and high-throughput screening or selection followed by sequencing can identify a large number of peptide ligands for a protein target. In this chapter, we describe a procedure called "SORTCERY" that can rank the affinities of library members for a target with high accuracy. SORTCERY follows a three-step protocol. First, fluorescence-activated cell sorting (FACS) is used to sort a library of yeast-displayed peptide ligands according to their affinities for a target. Second, all sorted pools are deep sequenced. Third, the resulting data are analyzed to create a ranking. We demonstrate an application of SORTCERY to the problem of ranking peptide ligands for the anti-apoptotic regulator Bcl-xL.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-1-4939-3569-7_14
