Authors
Edson N Cárcamo Noriega, Ian S Truebridge, Frank D Teets, James W Bowman, Nathalia Rodriguez, Tessa A Howard, Christopher D Bahl
Published in
Protein science : a publication of the Protein Society. Volume 35. Issue 9. Pages e70746.
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.
PMID:
42555168
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.
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