Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY
Author(s)
Reich, Lothar; Dutta, Sanjib; Keating, Amy E.
Downloadnihms786029.pdf (1.350Mb)
OPEN_ACCESS_POLICY
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2016-04Department
Massachusetts Institute of Technology. Department of BiologyJournal
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
Version: Author's final manuscript
ISBN
978-1-4939-3567-3
978-1-4939-3569-7
ISSN
1064-3745
1940-6029