A Platform for Deep Sequence-Activity Mapping and Engineering Antimicrobial Peptides

Matthew P. Dejong, Seth C. Ritter, Katharina A. Fransen, Daniel Tresnak, Alexander W. Golinski, Benjamin J. Hackel

Research output: Contribution to journalArticlepeer-review

Abstract

Developing potent antimicrobials, and platforms for their study and engineering, is critical as antibiotic resistance grows. A high-throughput method to quantify antimicrobial peptide and protein (AMP) activity across a broad continuum would be powerful to elucidate sequence-activity landscapes and identify potent mutants. Yet the complexity of antimicrobial activity has largely constrained the scope and mechanistic bandwidth of AMP variant analysis. We developed a platform to efficiently perform sequence-activity mapping of AMPs via depletion (SAMP-Dep): a bacterial host culture is transformed with an AMP mutant library, induced to intracellularly express AMPs, grown under selective pressure, and deep sequenced to quantify mutant depletion. The slope of mutant growth rate versus induction level indicates potency. Using SAMP-Dep, we mapped the sequence-activity landscape of 170 »000 mutants of oncocin, a proline-rich AMP, for intracellular activity against Escherichia coli. Clonal validation supported the platform's sensitivity and accuracy. The mapped landscape revealed an extended oncocin pharmacophore contrary to earlier structural studies, clarified the C-terminus role in internalization, identified functional epistasis, and guided focused, successful synthetic peptide library design, yielding a mutant with 2-fold enhancement in both intracellular and extracellular activity. The efficiency of SAMP-Dep poises the platform to transform AMP engineering, characterization, and discovery.

Original languageEnglish (US)
JournalACS Synthetic Biology
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (R01GM121777). We thank the University of Minnesota Genomics Center and the University of Illinois Roy J. Carver Biotechnology Center for assistance with deep sequencing. We thank the Minnesota Supercomputing Institute (MSI) for computational support. We also thank Dr. Pin-Kuang Lai for helpful discussions for oncocin sequence–function relationships and library design, Dr. Justin Klesmith for guidance with nicking mutagenesis, and Dr. Jorden Johnson for helpful discussions and feedback on the manuscript.

Publisher Copyright:
© 2021 American Chemical Society.

Keywords

  • antimicrobial peptide
  • deep mutational scanning
  • oncocin
  • protein engineering
  • ribosome

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