SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands

Abstract

Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein-protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction, but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting (FACS) of a library, deep sequencing of sorted pools, and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY’s utility by ranking 1026 BH3 peptides with respect to their affinities for the anti-apoptotic protein Bcl-xL. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60 nM. High-resolution ranking can be used to improve our understanding of sequence-function relationships, and to support the development of computational models for predicting and designing novel interactions.

Protein stability: computation, sequence statistics, and new experimental methods

“…), but rather than collapse down to a small number of “best” selectants, the entire library can be sequenced over rounds of selection. Fields [80] and Bolon [81] have used this approach to examine protein fitness landscapes, and Sidhu [82] and Keating [83] have used it to examine binding. Kelly, Fields and colleagues attempted to identify stabilizing mutations from site that result in positive epistasis scores in multiple mutant backgrounds [84].…”

Generating High-Accuracy Peptide-Binding Data in High Throughput with Yeast Surface Display and SORTCERY

“…The selection process involves two-color cell sorting of a yeast-surface displayed library based on the expression levels of displayed peptides and levels of binding to a target (Figure 1 B). Our sorting protocol builds on reports that two-color FACS can accurately distinguish between binders of different affinities [15–19] and agrees with a theoretical model describing the expected signals for clones expressing peptides with a range of binding strengths [20]. This model can guide sorting of a library into pools according to binding affinity, and the pools can then be deep-sequenced to obtain information about individual library member affinities.…”

“…On a log( F b ) vs . log( F e ) plot, points of equal binding strength lie on a line with a slope of 1 [20]. Subdivide the log/log plot accordingly into areas (gates) of different affinities by dissecting it with lines of slopes of 1 (red lines in Figure 3).…”

“…Our study is described in reference [20] and the reader is referred to that paper for in-depth exposition of the theory underlying SORTCERY, the results when applied to Bcl-x L , and further discussion of strengths and limitations of this method. A special variant of our approach is described here (Figure 2 B, Note 9), that can potentially be used to analyze much larger libraries.…”

Affinity Maturation of a Cyclic Peptide Handle for Therapeutic Antibodies Using Deep Mutational Scanning

“…1B) is particularly suitable for such affinity maturation (13). In contrast to in vitro molecular display technologies such as phage-or ribosome display, which require panning as a means of selection, Fluorescence Activated Cell Sorting (FACS) is used in yeast display, which offers more refined control of the selection pressure (14). Yeast display is also less vulnerable to unintended selection of the most infectious or fastest-replicating clones (15).…”