PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 13, 2026Synthesis0 citations

Development of Direct Liquid–Liquid Extraction (LLE) of an Enzymatic Cascade En Route to Enlicitide Using an Automated, High-Throughput Screening Platform

View Full Paper
WJWoo-Ok JungSMSarah R. MoorCAChihui An

Key Points

  • The research aims to develop an efficient method for isolating a macrocyclic peptide generated in an enzymatic cascade.
  • Developed a liquid-liquid extraction process for purification.
  • Employed an automated, vision-guided high-throughput screening platform.
  • Systematically evaluated hundreds of process conditions.
  • Achieved 93% recovery of the macrocyclic peptide product.
  • Obtained no detectable protein residue post-extraction.
  • Streamlined purification process for biocatalytic applications.

Abstract

Abstract A liquid–liquid extraction process was developed to isolate a macrocyclic peptide product generated via biocatalysis in the synthesis of enlicitide decanoate. Applying traditional purification methods for enzymatic cascades is challenging due to the presence of proteins, salts, and organic impurities that complicate downstream processing. To address this complexity, we employed a vision-guided, automated, high-throughput screening platform that enabled the systematic evaluation of hundreds of process conditions. Through comprehensive screening, we successfully identified optimal conditions for the selective isolation of the macrocyclic peptide product, achieving 93% recovery with no detectable protein residue. This approach served to streamline purification and enhanced overall process efficiency, demonstrating the utility of automated high-throughput screening for addressing complex purification challenges in biocatalytic process development.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jung et al. (2026) studied this question.

synapsesocial.com/papers/69b3aca302a1e69014cce825https://doi.org/10.1055/a-2790-8302
Ask AI
Helpful
Bookmark
Share
View Full Paper