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Constructing Cyclic Peptides Using an On-Tether Sulfonium Center
Published on: September 28, 2022
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Synthetic Natural Product Inspired Cyclic Peptides
Matthew A Hostetler1, Chloe Smith1, Samantha Nelson2
1Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.
ACS Chemical Biology
|October 26, 2021
Summary
Synthetic Natural Product Inspired Cyclic Peptides (SNaPP) accelerates discovery of novel bioactive molecules. This method combines bioinformatics and chemical synthesis to identify and create cyclic peptides with antibiotic properties, including against resistant bacteria.
Area of Science:
- Biochemistry
- Synthetic Biology
- Medicinal Chemistry
Background:
- Natural products are vital sources of bioactive molecules, but their discovery is hindered by complex biosynthetic pathways and purification challenges.
- Novel bioactive molecule discovery remains a critical need, particularly for combating antibiotic resistance.
Purpose of the Study:
- To develop and validate a novel method, SNaPP (Synthetic Natural Product Inspired Cyclic Peptides), for expedited identification of NP-inspired bioactive peptides.
- To leverage bioinformatics and chemical synthesis to create a library of cyclic peptides with potential therapeutic applications.
Main Methods:
- SNaPP integrates bioinformatics predictions of nonribosomal peptide synthetases with chemical synthesis of predicted natural products (pNPs).
- A penicillin binding protein-like cyclase was employed for the synthesis of head-to-tail cyclic peptide pNPs.
- Solid-phase peptide synthesis and solution-phase cyclization were used to generate a library of 51 diverse pNPs from 131 identified novel candidates.
Main Results:
- Analysis of 500 biosynthetic gene clusters identified 131 novel pNPs.
- Fifty-one diverse pNPs were successfully synthesized.
- Fourteen synthesized pNPs exhibited significant antibiotic activity, including against multidrug-resistant Gram-negative bacteria.
Conclusions:
- The SNaPP method effectively accelerates the discovery of bioactive molecules by merging computational predictions with synthetic chemistry.
- This approach holds promise for identifying novel peptide-based therapeutics, particularly antibiotics to address the challenge of antimicrobial resistance.
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