Facile, high-throughput protein purification enabled by high-efficiency protease elution.

Abstract

While advances in artificial intelligence have made protein design widely accessible, protein production and characterization remain a bottleneck. Protease-cleavable affinity tags are commonly used to improve yield and purity of recombinant proteins, but tag removal adds labor and complexity. Previous attempts to use a selective protease to elute cleaved fusion protein during affinity chromatography have suffered from poor digestion efficiency that greatly diminishes yield, or protease contamination in the eluate. Here, we describe a novel SUMO protease construct that supports rapid, high-yield protease elution. This is the keystone of an end-to-end DNA-to-protein workflow that we optimized for speed, parallelizability, and generalizability. While our workflow is intended for an automated liquid-handler, it can easily be performed manually, making it broadly accessible. We demonstrate the method by producing and characterizing 96 disparate proteins derived from mesophilic organisms. This provides a benchmark for methods development and a curated dataset useful for machine learning.