Dataset Information

In vitro selection of macrocyclic peptide inhibitors containing cyclic γ^2,4-amino acids targeting the SARS-CoV-2 main protease.

ABSTRACT: γ-Amino acids can play important roles in the biological activities of natural products; however, the ribosomal incorporation of γ-amino acids into peptides is challenging. Here we report how a selection campaign employing a non-canonical peptide library containing cyclic γ^2,4-amino acids resulted in the discovery of very potent inhibitors of the SARS-CoV-2 main protease (M^pro). Two kinds of cyclic γ^2,4-amino acids, cis-3-aminocyclobutane carboxylic acid (γ¹) and (1R,3S)-3-aminocyclopentane carboxylic acid (γ²), were ribosomally introduced into a library of thioether-macrocyclic peptides. One resultant potent M^pro inhibitor (half-maximal inhibitory concentration = 50 nM), GM4, comprising 13 residues with γ¹ at the fourth position, manifests a 5.2 nM dissociation constant. An M^pro:GM4 complex crystal structure reveals the intact inhibitor spans the substrate binding cleft. The γ¹ interacts with the S1' catalytic subsite and contributes to a 12-fold increase in proteolytic stability compared to its alanine-substituted variant. Knowledge of interactions between GM4 and M^pro enabled production of a variant with a 5-fold increase in potency.

SUBMITTER: Miura T

PROVIDER: S-EPMC10322702 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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In vitro selection of macrocyclic peptide inhibitors containing cyclic γ2,4-amino acids targeting the SARS-CoV-2 main protease.

Miura Takashi T Malla Tika R TR Owen C David CD Tumber Anthony A Brewitz Lennart L McDonough Michael A MA Salah Eidarus E Terasaka Naohiro N Katoh Takayuki T Lukacik Petra P Strain-Damerell Claire C Mikolajek Halina H Walsh Martin A MA Kawamura Akane A Schofield Christopher J CJ Suga Hiroaki H

Nature chemistry 20230522 7

γ-Amino acids can play important roles in the biological activities of natural products; however, the ribosomal incorporation of γ-amino acids into peptides is challenging. Here we report how a selection campaign employing a non-canonical peptide library containing cyclic γ2,4-amino acids resulted in the discovery of very potent inhibitors of the SARS-CoV-2 main protease (Mpro). Two kinds of cyclic γ2,4-amino acids, cis-3-aminocyclobutane carboxylic acid (γ1 ...[more]

PMID: 37217786

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Project description:In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.

Dataset Information

In vitro selection of macrocyclic peptide inhibitors containing cyclic γ^2,4-amino acids targeting the SARS-CoV-2 main protease.

Publications

In vitro selection of macrocyclic peptide inhibitors containing cyclic γ<sup>2,4</sup>-amino acids targeting the SARS-CoV-2 main protease.

Similar Datasets

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