Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY.
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.
Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm 68-622A, Cambridge, MA, 02139, USA.
Lothar Luther
LL
Reich
Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm 68-622A, Cambridge, MA, 02139, USA.
Sanjib
S
Dutta
Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Rm 68-622A, Cambridge, MA, 02139, USA. keating@mit.edu.
Amy E
AE
Keating
D002469:Cell Separation; D005434:Flow Cytometry; D005656:Fungal Proteins / Q000378:metabolism*; D008024:Ligands; D010455:Peptides / Q000378:metabolism*; D011485:Protein Binding; D015003:Yeasts / Q000378:metabolism*
D005656:Fungal Proteins; D008024:Ligands; D010455:Peptides
Bcl-2 family; Deep sequencing; High-throughput assay; Protein–protein interaction; Yeast surface display