Peptide design by optimization on a data-parameterized protein interaction landscape

Abstract

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein–peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2–binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Generating quantitative binding landscapes through fractional binding selections, deep sequencing and data normalization

“…DDGbind values. Very recently, a similar direction has been taken by Keating and colleagues to design mutations and improve affinity in peptide/protein interactions 32 . Unlike our study, the authors used IC50 value of yeast titrations to normalize NGS data and to predict DDGbind for various peptide mutants with Kds in the medium affinity range.…”

Selective Affimers Recognize BCL-2 Family Proteins Through Non-Canonical Structural Motifs

“…All known biological partners, 4 and indeed all designed peptides thus far, 13,14,[53][54][55] that interact with BCL-2 family proteins at the BH3 groove do so with peptide in a helical conformation. Crucially, the VRs do not adopt an a-helical conformation to make interactions with the BH3 binding cleft (presumably in part because they are constrained from doing so).…”

Tertiary structural motif sequence statistics enable facile prediction and design of peptides that bind anti-apoptotic Bfl-1 and Mcl-1

“…To evaluate the potential of dTERMen for designing peptide ligands for Bcl-2 family targets, we tested its performance on a variety of prediction tasks. We used a dataset consisting of 4488, 4648 and 3948 measurements of BH3 peptides binding to Bcl-xL, Mcl-1 and Bfl-1, respectively [39]. The peptides were 23 residues in length and contained between 1 and 8 mutations made in the background of the BH3 sequences of human pro-apoptotic proteins BIM or PUMA.…”

“…The peptides were 23 residues in length and contained between 1 and 8 mutations made in the background of the BH3 sequences of human pro-apoptotic proteins BIM or PUMA. Affinity values were obtained using amped SORTCERY, a high-throughput method for quantifying dissociation constants of peptides displayed on the surface of Saccharomyces cerevisiae [39,40]. Using this assay, thousands of peptides were determined to have apparent cell-surface dissociation constants ranging from 0.1 to 320 nM, with some peptides classified simply as binding tighter or weaker than the extremes of this range.…”