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Transmembrane Ca v 2.2 (N-type) voltage-gated calcium channels are genetically and pharmacologically validated, clinically relevant pain targets. Clinical block of Ca v 2.2 (e.g., with Prialt/Ziconotide) or indirect modulation [e.g., with gabapentinoids such as Gabapentin (GBP)] mitigates chronic pain but is encumbered by side effects and abuse liability. The cytosolic auxiliary subunit collapsin response mediator protein 2 (CRMP2) targets Ca v 2.2 to the sensory neuron membrane and regulates their function via an intrinsically disordered motif. A CRMP2-derived peptide (CBD3) uncouples the Ca v 2.2-CRMP2 interaction to inhibit calcium influx, transmitter release, and pain. We developed and applied a molecular dynamics approach to identify the A 1 R 2 dipeptide in CBD3 as the anchoring Ca v 2.2 motif and designed pharmacophore models to screen 27 million compounds on the open-access server ZincPharmer. Of 200 curated hits, 77 compounds were assessed using depolarization-evoked calcium influx in rat dorsal root ganglion neurons. Nine small molecules were tested electrophysiologically, while one (CBD3063) was also evaluated biochemically and behaviorally. CBD3063 uncoupled Ca v 2.2 from CRMP2, reduced membrane Ca v 2.2 expression and Ca 2+ currents, decreased neurotransmission, reduced fiber photometry-based calcium responses in response to mechanical stimulation, and reversed neuropathic and inflammatory pain across sexes in two different species without changes in sensory, sedative, depressive, and cognitive behaviors. CBD3063 is a selective, first-in-class, CRMP2-based peptidomimetic small molecule, which allosterically regulates Ca v 2.2 to achieve analgesia and pain relief without negative side effect profiles. In summary, CBD3063 could potentially be a more effective alternative to GBP for pain relief.

Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [ J. Am. Chem. Soc. 2017 , 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.139 , 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.

Peptides and peptidomimetics represent the middle space between small molecules and large proteins-they retain the relatively small size and synthetic accessibility of small molecules while providing high binding specificity for biomolecular partners typically observed with proteins. During the course of our efforts to target intracellular protein-protein interactions in cancer, we observed that the cellular uptake of peptides is critically determined by the cell line-specifically, we noted that peptides show better uptake in cancer cells with enhanced macropinocytic indices. Here, we describe the results of our analysis of cellular penetration by different classes of conformationally stabilized peptides. We tested the uptake of linear peptides, peptide macrocycles, stabilized helices, β-hairpin peptides, and cross-linked helix dimers in 11 different cell lines. Efficient uptake of these conformationally defined constructs directly correlated with the macropinocytic activity of each cell line: high uptake of compounds was observed in cells with mutations in certain signaling pathways. Significantly, the study shows that constrained peptides follow the same uptake mechanism as proteins in macropinocytic cells, but unlike proteins, peptide mimics can be readily designed to resist denaturation and proteolytic degradation. Our findings expand the current understanding of cellular uptake in cancer cells by designed peptidomimetics and suggest that cancer cells with certain mutations are suitable mediums for the study of biological pathways with peptide leads.

Protein-protein interactions (PPIs) play a critical role in fundamental biological processes. Competitive inhibition of these interfaces requires compounds that can access discontinuous binding epitopes along a large, shallow binding surface area. Conformationally defined protein surface mimics present a viable route to target these interactions. However, the development of minimal protein mimics that engage intracellular targets with high affinity remains a major challenge because mimicry of a portion of the binding interface is often associated with the loss of critical binding interactions. Covalent targeting provides an attractive approach to overcome the loss of noncovalent contacts but have the inherent risk of dominating noncovalent contacts and increasing the likelihood of nonselective binding. Here, we report the iterative design of a proteolytically stable α 3 β chimeric helix mimic that covalently targets oncogenic Ras G12C as a model system. We explored several electrophiles to optimize preferential alkylation with the desired C12 on Ras. The designed lead peptide modulates nucleotide exchange, inhibits activation of the Ras-mediated signaling cascade, and is selectively toxic toward mutant Ras G12C cancer cells. The relatively high frequency of acquired cysteines as missense mutations in cancer and other diseases suggests that covalent peptides may offer an untapped therapeutic approach for targeting aberrant protein interactions.

β-Amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating non-natural amino acids typically requires representing their side-chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state-of-the-art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for β 3 -amino acids., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

We describe a modular approach to identify and inhibit protein-protein interactions (PPIs) that are mediated by protein secondary and tertiary structures with rationally designed peptidomimetics. Our analysis begins with entries of high-resolution complexes in the Protein Data Bank and utilizes conformational sampling, scoring, and design capabilities of advanced biomolecular modeling software to develop peptidomimetics.

Protein-protein interactions (PPIs) are emerging as attractive targets for drug design because of their central role in directing normal and aberrant cellular functions. These interactions were once considered "undruggable" because their large and dynamic interfaces make small molecule inhibitor design challenging. However, landmark advances in computational analysis, fragment screening and molecular design have enabled development of a host of promising strategies to address the fundamental molecular recognition challenge. An attractive approach for targeting PPIs involves mimicry of protein domains that are critical for complex formation. This approach recognizes that protein subdomains or protein secondary structures are often present at interfaces and serve as organized scaffolds for the presentation of side chain groups that engage the partner protein(s). Design of protein domain mimetics is in principle rather straightforward but is enabled by a host of computational strategies that provide predictions of important residues that should be mimicked. Herein we describe a workflow proceeding from interaction network analysis, to modeling a complex structure, to identifying a high-affinity sub-structure, to developing interaction inhibitors. We apply the design procedure to peptidomimetic inhibitors of Ras-mediated signaling., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)

Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, [Formula: see text]-peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic community's need for an automated and expandable modeling tool for noncanonical backbones.