CARPE: Computationally-Aided Rapid Protein Engineering
Directed evolution is a common wet lab technique used for generating protein active sites that can bind with high affinity to a target ligand, but it involves a substantial amount of time and wet lab resources. One of the main bottlenecks in current directed evolution pipelines is they rely solely on random mutagenesis. This requires researchers to generate vast libraries of different mutant receptors and screen each one to identify mutations that improve binding affinity of a receptor to a target ligand. However, motivated by recent advances in computational tools, our project aims to build a generalizable computational pipeline that can identify mutations to improve a protein’s binding to a target ligand without the need for wet lab work.
By integrating computational insights into a directed evolution pipeline, we can bias a mutagenesis library to a particular group of mutations that have a high likelihood of generating high-affinity proteins. This tool would give researchers the ability to simplify their directed evolution protocols, reducing the amount of time, wet lab resources, and cost needed to generate high-affinity protein receptors. Such an advancement would significantly decrease the price of generating novel protein receptors and increase the accessibility of directed evolution approaches within research and manufacturing.
As a proof of concept for our pipeline, our project involves employing computational tools to speed up the directed evolution process of G-protein coupled receptors Ste2 in yeast to detect disease biomarkers i.e. short peptides. These engineered receptors will be implemented in yeast cells to serve as a biosensor for early diagnosis. To this end, we will use the cystatin-C biomarker, a canonical biomarker for kidney disease.
We hope to develop this computational platform by using protein structure prediction tools through the recent advances of AlphaFold, validated by the recently determined Ste2 crystal structure. We will computationally screen for mutations that would confer increased affinity and specificity for a target ligand. We will use site-directed mutagenesis to engineer these in Ste2 and use fluorescent reporters to measure signal transduction.
Overall, we hope to demonstrate that, by integrating these computational tools into our wet lab directed evolution protocol, we can generate a kidney disease biosensor more rapidly and at a lower cost.