Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.

Motivation: Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.

Motivation: Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Rapid Evaluation of Staple Placement in Stabilized α Helices Using Bacterial Surface Display.

There are a wealth of proteins involved in disease that cannot be targeted by current therapeutics because they are inside cells, inaccessible to most macromolecules, and lack small-molecule binding pockets. Stapled peptides, where two amino acids are covalently linked, form a class of macrocycles that have the potential to penetrate cell membranes and disrupt intracellular protein-protein interactions. However, their discovery relies on solid-phase synthesis, greatly limiting queries into their complex design space involving amino acid sequence, staple location, and staple chemistry.

Directed Evolution Using Stabilized Bacterial Peptide Display.

Chemically stabilized peptides have attracted intense interest by academics and pharmaceutical companies due to their potential to hit currently "undruggable" targets. However, engineering an optimal sequence, stabilizing linker location, and physicochemical properties is a slow and arduous process. By pairing non-natural amino acid incorporation and cell surface click chemistry in bacteria with high-throughput sorting, we developed a method to quantitatively select high affinity ligands and applied the Stabilized Peptide Evolution by Display technique to develop disrupters of the therapeutically relevant MDM2-p53 interface.