Mixed Stereochemistry Macrocycle Acts as a Helix-Stabilizing Peptide N-Cap.

Interactions between proteins and α-helical peptides have been the focus of drug discovery campaigns. However, the large interfaces formed between multiple turns of an α-helix and a binding protein represent a significant challenge to inhibitor discovery. Modified peptides featuring helix-stabilizing macrocycles have shown promise as inhibitors of these interactions. Here, we tested the ability of N-terminal to side-chain thioether-cyclized peptides to inhibit the α-helix binding protein Mcl-1, by screening a trillion-scale library. The enriched peptides were lariats featuring a small, four-amino-acid N-terminal macrocycle followed by a short linear sequence that resembled the natural α-helical Mcl-1 ligands. These "Heliats" (helical lariats) bound Mcl-1 with tens of nM affinity, and inhibited the interaction between Mcl-1 and a natural peptide ligand. Macrocyclization was found to stabilize α-helical structures and significantly contribute to affinity and potency. Yet, the 2nd and 3rd positions within the macrocycle were permissible to sequence variation, so that a minimal macrocyclic motif, of an N-acetylated d-phenylalanine at the 1st position thioether connected to a cysteine at the 4th, could be grafted into a range of peptides and stabilize helical conformations. We found that d-stereochemistry is more helix-stabilizing than l- at the 1st position in the motif, as the d-amino acid can utilize polyproline II torsional angles that allow for more optimal intrachain hydrogen bonding. This mixed stereochemistry macrocyclic N-cap is synthetically accessible, requiring only minor modifications to standard solid-phase peptide synthesis, and its compatibility with peptide screening can provide ready access to helix-focused peptide libraries for de novo inhibitor discovery.

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.