A cell-active cyclic peptide targeting the Nrf2/Keap1 protein–protein interaction

Iegre, Jessica; Krajcovicova, Sona; Gunnarsson, Anders; Wissler, Lisa; Käck, Helena; Luchniak, Anna; Tångefjord, Stefan; Narjes, Frank; Spring, David R.

doi:10.1039/d3sc04083f

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Oxidative stress, a major contributor to various pathological conditions, underscores the importance of disrupting the Nrf2/Keap1 PPI. This disruption is considered a promising strategy to upregulate Nrf2 levels and enhance cellular protection against oxidative stress 23 – 25 .…”

Section: Introductionmentioning

confidence: 99%

De novo antioxidant peptide design via machine learning and DFT studies

Hesamzadeh,

Seif,

Mahmoudzadeh

et al. 2024

Sci Rep

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Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1–GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.

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Section: Introductionmentioning

confidence: 99%

De novo antioxidant peptide design via machine learning and DFT studies

Hesamzadeh,

Seif,

Mahmoudzadeh

et al. 2024

Sci Rep

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“…49 Interfering with this interaction has been demonstrated to modulate responses to oxidative stress signals, 50 offering a promising avenue for discovering inhibitors to treat diseases associated with inflammatory responses and neurodegeneration. 51,52 hKeap1, featuring an N-terminal His tag, was expressed (Fig. S15†) and immobilized on a plate for screening the KX7C cyclic peptide library (Fig.…”

Section: Introductionmentioning

confidence: 99%

A facile strategy for the construction of a phage display cyclic peptide library for the selection of functional macrocycles

Xiang,

Bai,

Zhang

et al. 2024

Chem. Sci.

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Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics

Swenson,

Mandava,

Thomas

et al. 2024

Chem. Rev.

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The development of potent, specific, and pharmacologically viable chemical probes and therapeutics is a central focus of chemical biology and therapeutic development. However, a significant portion of predicted disease-causal proteins have proven resistant to targeting by traditional small molecule and biologic modalities. Many of these so-called "undruggable" targets feature extended, dynamic protein−protein and protein−nucleic acid interfaces that are central to their roles in normal and diseased signaling pathways. Here, we discuss the development of synthetically stabilized peptide and protein mimetics as an everexpanding and powerful region of chemical space to tackle undruggable targets. These molecules aim to combine the synthetic tunability and pharmacologic properties typically associated with small molecules with the binding footprints, affinities and specificities of biologics. In this review, we discuss the historical and emerging platforms and approaches to design, screen, select and optimize synthetic "designer" peptidomimetics and synthetic biologics. We examine the inspiration and design of different classes of designer peptidomimetics: (i) macrocyclic peptides, (ii) side chain stabilized peptides, (iii) non-natural peptidomimetics, and (iv) synthetic proteomimetics, and notable examples of their application to challenging biomolecules. Finally, we summarize key learnings and remaining challenges for these molecules to become useful chemical probes and therapeutics for historically undruggable targets.

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A cell-active cyclic peptide targeting the Nrf2/Keap1 protein–protein interaction

Abstract: The disruption of the protein-protein interaction (PPI) between Nrf2 and Keap1 is an attractive strategy to counteract the oxidative stress that characterises a variety of severe diseases. Peptides represent a...

Cited by 3 publications

References 44 publications

De novo antioxidant peptide design via machine learning and DFT studies

De novo antioxidant peptide design via machine learning and DFT studies

A facile strategy for the construction of a phage display cyclic peptide library for the selection of functional macrocycles

Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics

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