β-Bracelets: Macrocyclic Cross-β Epitope Mimics Based on a Tau Conformational Strain

Beyond N-Alkylation: Synthesis, Structure, and Function of N-Amino Peptides

The growing list of physiologically important protein-protein interactions (PPIs) has amplified the need for compounds to target topologically complex biomolecular surfaces. In contrast to small molecules, peptide and protein mimics can exhibit three-dimensional shape complementarity across a large area and thus have the potential to significantly expand the "druggable" proteome. Strategies to stabilize canonical protein secondary structures without sacrificing side-chain content are particularly useful in the design of peptide-based chemical probes and therapeutics.Substitution of the backbone amide in peptides represents a subtle chemical modification with profound effects on conformation and stability. Studies focused on N-alkylation have already led to broad-ranging applications in peptidomimetic design. Inspired by nonribosomal peptide natural products harboring amide N-oxidations, we envisioned that main-chain hydrazide and hydroxamate bonds would impose distinct conformational preferences and offer unique opportunities for backbone diversification. This Account describes our exploration of peptide N-amination as a strategy for stabilizing canonical protein folds and for the structure-based design of soluble amyloid mimics.We developed a general synthetic protocol to access N-amino peptides (NAPs) on solid support. In an effort to stabilize β-strand conformation, we designed stitched peptidomimetics featuring covalent tethering of the backbone N-amino substituent to the preceding residue side chain. Using a combination of NMR, X-ray crystallography, and molecular dynamics simulations, we discovered that backbone N-amination alone could significantly stabilize β-hairpin conformation in multiple models of folding. Our studies revealed that the amide NH2 substituent in NAPs participates in cooperative noncovalent interactions that promote β-sheet secondary structure. In contrast to Cα-substituted α-hydrazino acids, we found that N-aminoglycine and its N'-alkylated derivatives instead stabilize polyproline II (PPII) conformation. The reactivity of hydrazides also allows for late-stage peptide macrocyclization, affording novel covalent surrogates of side-chain-backbone H-bonds.The pronounced β-sheet propensity of Cα-substituted α-hydrazino acids prompted us to target amyloidogenic proteins using NAP-based β-strand mimics. Backbone N-amination was found to render aggregation-prone lead sequences soluble and resistant to proteolysis. Inhibitors of Aβ and tau identified through N-amino scanning blocked protein aggregation and the formation of mature fibrils in vitro. We further identified NAP-based single-strand and cross-β tau mimics capable of inhibiting the prion-like cellular seeding activity of recombinant and patient-derived tau fibrils.Our studies establish backbone N-amination as a valuable addition to the peptido- and proteomimetic tool kit. α-Hydrazino acids show particular promise as minimalist β-strand mimics that retain side-chain information. Late-stage derivatization of hydrazides also provides facile entry into libraries of backbone-edited peptides. We anticipate that NAPs will thus find applications in the development of optimally constrained folds and modulators of PPIs.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.