Accurate de novo design of membrane-traversing macrocycles

Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

De novo development of small cyclic peptides that are orally bioavailable

Cyclic peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, as with biological drugs, most cyclic peptides cannot be applied orally because they are rapidly digested and/or display low absorption in the gastrointestinal tract, hampering their development as therapeutics. In this study, we developed a combinatorial synthesis and screening approach based on sequential cyclization and one-pot peptide acylation and screening, with the possibility of simultaneously interrogating activity and permeability. In a proof of concept, we synthesized a library of 8,448 cyclic peptides and screened them against the disease target thrombin. Our workflow allowed multiple iterative cycles of library synthesis and yielded cyclic peptides with nanomolar affinities, high stabilities and an oral bioavailability (%F) as high as 18% in rats. This method for generating orally available peptides is general and provides a promising push toward unlocking the full potential of peptides as therapeutics.

Expansive discovery of chemically diverse structured macrocyclic oligoamides

Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

Requirements for efficient endosomal escape by designed mini-proteins

ZF5.3 is a compact, rationally designed mini-protein that escapes efficiently from the endosomes of multiple cell types. Despite its small size (27 amino acids), ZF5.3 can be isolated intact from the cytosol of treated cells and guides multiple classes of proteins into the cytosol and/or nucleus. In the best cases, delivery efficiencies reach or exceed 50% to establish nuclear or cytosolic concentrations of 500 nM or higher. But other than the requirement for unfoldable cargo and an intact HOPS complex, there is little known about how ZF5.3 traverses the limiting endocytic membrane. Here we delineate the attributes of ZF5.3 that enable efficient endosomal escape. We confirm that ZF5.3 is stable at pH values between 5.5 and 7.5, with no evidence of unfolding even at temperatures as high as 95 °C. The high-resolution NMR structure of ZF5.3 at pH 5.5, also reported here, shows a canonical p zinc-finger fold with the penta-arg motif integrated seamlessly into the C-terminal α-helix. At lower pH, ZF5.3 unfolds cooperatively as judged by both circular dichroism and high-resolution NMR. Unfolding occurs upon protonation of a single Zn(II)-binding His side chain whose pKa corresponds almost exactly to that of the late endosomal lumen. pH-induced unfolding is essential for endosomal escape, as a ZF5.3 analog that remains folded at pH 4.5 fails to efficiently reach the cytosol, despite high overall uptake. Finally, using reconstituted liposomes, we identify a high-affinity interaction of ZF5.3 with a specific lipid-BMP-that is selectively enriched in the inner leaflet of late endosomal membranes. This interaction is 10-fold stronger at low pH than neutral pH, providing a molecular picture for why escape occurs preferentially and in a HOPS-dependent manner from late endosomal compartments. The requirements for programmed endosomal escape identified here should aid and inform the design of proteins, peptidomimetics, and other macromolecules that reach cytosolic or nuclear targets intact and at therapeutically relevant concentrations.

CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development.

Cyclization Scaffolding for Improved Vaccine Immunogen Stability: Application to Tau Protein in Alzheimer's Disease

Effective scaffolding of immunogens is crucial for generating conformationally selective antibodies through active immunization, particularly in the treatment of protein misfolding diseases such as Alzheimer's and Parkinson's disease. Previous computational work has revealed that a disorder-prone region of the tau protein, when in a stacked form, is predicted to structurally resemble a small, soluble protofibril, having conformational properties similar to those of experimental in vitro tau oligomers. Such an oligomeric structural mimic has the potential to serve as a vaccine immunogen design for Alzheimer's disease. In this study, we developed a cyclization scaffolding method in Rosetta, in which multiple cyclic peptides are stacked into a protofibril. Cyclization results in significant stabilization of protofibril-like structures by constraining the conformational space. Applying this method to the disorder-prone region of the tau fibril, we evaluated the metastability of the cyclized tau immunogen using molecular dynamics simulations, and we identified sequences of two cyclic constructs having high metastability in the protofibril. We then assessed their thermodynamic stability by computing the free energy required to separate a distal chain from the rest of the stacked structure. Our computational results, based on molecular dynamics simulations and free energy calculations, demonstrate that two cyclized constructs, cyclo-(VKSEKLDFKDRVQSKIFyN) and cyclo-(VKSEKLDFKDRVQSKIYvG) (lowercase letters indicate d-form amino acids), possess significantly increased thermodynamic stability in the protofibril over an uncyclized linear construct VKSEKLDFKDRVQSKI. The cyclization scaffolding approach proposed here holds promise as a means to effectively design immunogens for protein misfolding diseases, particularly those involving liposome-conjugated peptide constructs.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Membrane Permeability in a Large Macrocyclic Peptide Driven by a Saddle-Shaped Conformation

The effort to modulate challenging protein targets has stimulated interest in ligands that are larger and more complex than typical small-molecule drugs. While combinatorial techniques such as mRNA display routinely produce high-affinity macrocyclic peptides against classically undruggable targets, poor membrane permeability has limited their use toward primarily extracellular targets. Understanding the passive membrane permeability of macrocyclic peptides would, in principle, improve our ability to design libraries whose leads can be more readily optimized against intracellular targets. Here, we investigate the permeabilities of over 200 macrocyclic 10-mers using the thioether cyclization motif commonly found in mRNA display macrocycle libraries. We identified the optimal lipophilicity range for achieving permeability in thioether-cyclized 10-mer cyclic peptide-peptoid hybrid scaffolds and showed that permeability could be maintained upon extensive permutation in the backbone. In one case, changing a single amino acid from d-Pro to d-NMe-Ala, representing the loss of a single methylene group in the side chain, resulted in a highly permeable scaffold in which the low-dielectric conformation shifted from the canonical cross-beta geometry of the parent compounds into a novel saddle-shaped fold in which all four backbone NH groups were sequestered from the solvent. This work provides an example by which pre-existing physicochemical knowledge of a scaffold can benefit the design of macrocyclic peptide mRNA display libraries, pointing toward an approach for biasing libraries toward permeability by design. Moreover, the compounds described herein are a further demonstration that geometrically diverse, highly permeable scaffolds exist well beyond conventional drug-like chemical space.

Cyclic peptides discriminate BCL-2 and its clinical mutants from BCL-XL by engaging a single-residue discrepancy

Overexpressed pro-survival B-cell lymphoma-2 (BCL-2) family proteins BCL-2 and BCL-XL can render tumor cells malignant. Leukemia drug venetoclax is currently the only approved selective BCL-2 inhibitor. However, its application has led to an emergence of resistant mutations, calling for drugs with an innovative mechanism of action. Herein we present cyclic peptides (CPs) with nanomolar-level binding affinities to BCL-2 or BCL-XL, and further reveal the structural and functional mechanisms of how these CPs target two proteins in a fashion that is remarkably different from traditional small-molecule inhibitors. In addition, these CPs can bind to the venetoclax-resistant clinical BCL-2 mutants with similar affinities as to the wild-type protein. Furthermore, we identify a single-residue discrepancy between BCL-2 D111 and BCL-XL A104 as a molecular "switch" that can differently engage CPs. Our study suggests that CPs may inhibit BCL-2 or BCL-XL by delicately modulating protein-protein interactions, potentially benefiting the development of next-generation therapeutics.

Cyclic Peptides for Drug Development

Cyclic peptides are fascinating molecules abundantly found in nature and exploited as molecular format for drug development as well as other applications, ranging from research tools to food additives. Advances in peptide technologies made over many years through improved methods for synthesis and drug development have resulted in a steady stream of new drugs, with an average of around one cyclic peptide drug approved per year. Powerful technologies for screening random peptide libraries, and de novo generating ligands, have enabled the development of cyclic peptide drugs independent of naturally derived molecules and now offer virtually unlimited development opportunities. In this review, we feature therapeutically relevant cyclic peptides derived from nature and discuss the unique properties of cyclic peptides, the enormous technological advances in peptide ligand development in recent years, and current challenges and opportunities for developing cyclic peptides that address unmet medical needs.

Molecular chameleons in drug discovery

Molecular chameleons possess a flexibility that allows them to dynamically shield or expose polar functionalities in response to the properties of the environment. Although the concept of molecular chameleons was introduced already in 1970, interest in them has grown considerably since the 2010s, when drug discovery has focused to an increased extent on new chemical modalities. Such modalities include cyclic peptides, macrocycles and proteolysis-targeting chimeras, all of which reside in a chemical space far from that of traditional small-molecule drugs. Both cell permeability and aqueous solubility are required for the oral absorption of drugs. Engineering these properties, and potent target binding, into the larger new modalities is a more daunting task than for traditional small-molecule drugs. The ability of chameleons to adapt to different environments may be essential for success. In this Review, we provide both general and theoretical insights into the realm of molecular chameleons. We discuss why chameleons have come into fashion and provide a do-it-yourself toolbox for their design; we then provide a glimpse of how advanced in silico methods can support molecular chameleon design.

Representing structures of the multiple conformational states of proteins

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Addressing Challenges of Macrocyclic Conformational Sampling in Polar and Apolar Solvents: Lessons for Chameleonicity

We evaluated a workflow to reliably sample the conformational space of a set of 47 peptidic macrocycles. Starting from SMILES strings, we use accelerated molecular dynamics simulations to overcome high energy barriers, in particular, the cis-trans isomerization of peptide bonds. We find that our approach performs very well in polar solvents like water and dimethyl sulfoxide. Interestingly, the protonation state of a secondary amine in the ring only slightly influences the conformational ensembles of our test systems. For several of the macrocycles, determining the conformational distribution in chloroform turns out to be considerably more challenging. Especially, the choice of partial charges crucially influences the ensembles in chloroform. We address these challenges by modifying initial structures and the choice of partial charges. Our results suggest that special care has to be taken to understand the configurational distribution in apolar solvents, which is a key step toward a reliable prediction of membrane permeation of macrocycles and their chameleonic properties.

Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations

Cyclic peptides are an emerging therapeutic modality that can target protein-protein interaction sites with high affinity and selectivity. A common medicinal chemistry strategy for the optimization of peptide hits is conformational stabilization through macrocyclization. We present a method based on explicit solvent enhanced sampling molecular dynamics simulations for estimating the impact of varying linker lengths and chemistry on the conformational stability of a peptide. The method is demonstrated on three cyclic peptide series that bind to proteins PCSK9, trypsin, and MDM2 adopting loop, β-sheet, and helical secondary structures. In general, the simulations show greater solution stability of the receptor-bound conformation for the higher-affinity peptides, consistent with the idea that preorganizing a ligand for binding can enhance binding affinity. The impact of the force field and sampling is discussed for one series that does not follow this trend. We have successfully applied this method to internal discovery programs to design peptides with increased potency and chemical stability.

Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders

The Nrf2 transcription factor is a master regulator of the cellular response to oxidative stress, and Keap1 is its primary negative regulator. Activating Nrf2 by inhibiting the Nrf2-Keap1 protein-protein interaction has shown promise for treating cancer and inflammatory diseases. A loop derived from Nrf2 has been shown to inhibit Keap1 selectively, especially when cyclized, but there are no reliable design methods for predicting an optimal macrocyclization strategy. In this work, we employed all-atom, explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the relative degree of preorganization for a series of peptides cyclized with a set of bis-thioether "staples". We then correlated these predictions to experimentally measured binding affinities for Keap1 and crystal structures of the cyclic peptides bound to Keap1. This work showcases a computational method for designing cyclic peptides by simulating and comparing their entire solution-phase ensembles, providing key insights into designing cyclic peptides as selective inhibitors of protein-protein interactions.

Validation of a New Methodology to Create Oral Drugs beyond the Rule of 5 for Intracellular Tough Targets

Establishing a technological platform for creating clinical compounds inhibiting intracellular protein-protein interactions (PPIs) can open the door to many valuable drugs. Although small molecules and antibodies are mainstream modalities, they are not suitable for a target protein that lacks a deep cavity for a small molecule to bind or a protein found in intracellular space out of an antibody's reach. One possible approach to access these targets is to utilize so-called middle-size cyclic peptides (defined here as those with a molecular weight of 1000-2000 g/mol). In this study, we validated a new methodology to create oral drugs beyond the rule of 5 for intracellular tough targets by elucidating structural features and physicochemical properties for drug-like cyclic peptides and developing library technologies to afford highly N-alkylated cyclic peptide hits. We discovered a KRAS inhibitory clinical compound (LUNA18) as the first example of our platform technology.

An amide to thioamide substitution improves the permeability and bioavailability of macrocyclic peptides

Solvent shielding of the amide hydrogen bond donor (NH groups) through chemical modification or conformational control has been successfully utilized to impart membrane permeability to macrocyclic peptides. We demonstrate that passive membrane permeability can also be conferred by masking the amide hydrogen bond acceptor (>C = O) through a thioamide substitution (>C = S). The membrane permeability is a consequence of the lower desolvation penalty of the macrocycle resulting from a concerted effect of conformational restriction, local desolvation of the thioamide bond, and solvent shielding of the amide NH groups. The enhanced permeability and metabolic stability on thioamidation improve the bioavailability of a macrocyclic peptide composed of hydrophobic amino acids when administered through the oral route in rats. Thioamidation of a bioactive macrocyclic peptide composed of polar amino acids results in analogs with longer duration of action in rats when delivered subcutaneously. These results highlight the potential of O to S substitution as a stable backbone modification in improving the pharmacological properties of peptide macrocycles.

Controlling the Chameleonic Behavior and Membrane Permeability of Cyclosporine Derivatives via Backbone and Side Chain Modifications

Some macrocycles exhibit enhanced membrane permeability through conformational switching in different environmental polarities, a trait known as chameleonic behavior. In this study, we demonstrate specific backbone and side chain modifications that can control chameleonic behavior and passive membrane permeability using a cyclosporin O (CsO) scaffold. To quantify chameleonic behavior, we used a ratio of the population of the closed conformation obtained in polar solvent and nonpolar solvent for each CsO derivative. We found that β-hydroxylation at position 1 (1 and 3) can encode chameleonicity and improve permeability. However, the conformational stabilization induced by adding an additional transannular H-bond (2 and 5) leads to a much slower rate of membrane permeation. Our CsO scaffold provides a platform for the systematic study of the relationship among conformation, membrane permeability, solubility, and protein binding. This knowledge contributes to the discovery of potent beyond the rule of five (bRo5) macrocycles capable of targeting undruggable targets.

PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction

The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be fully explored. In this study, an integrated AI framework called PepScaf was developed to extract the critical scaffold relative to bioactivity based on a vast dataset from an initial in vitro selection campaign against a model protein target, interleukin-17C (IL-17C). Taking the generated scaffold, a focused macrocyclic peptide library was rationally constructed to target IL-17C, yielding over 20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction. Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents a viable methodology for more efficient macrocyclic peptide discovery, offering potential time and cost savings. Additionally, this is also the first report regarding the discovery of macrocyclic peptides against IL-17C/IL-17RE interaction.

Discovery and characterization of cyclic peptides selective for the C-terminal bromodomains of BET family proteins

DNA-encoded cyclic peptide libraries can yield high-potency, high-specificity ligands against target proteins. We used such a library to seek ligands that could distinguish between paralogous bromodomains from the closely related bromodomain and extra-terminal domain family of epigenetic regulators. Several peptides isolated from a screen against the C-terminal bromodomain of BRD2, together with new peptides discovered in previous screens against the corresponding domain from BRD3 and BRD4, bound their targets with nanomolar and sub-nanomolar affinities. X-ray crystal structures of several of these bromodomain-peptide complexes reveal diverse structures and binding modes, which nevertheless display several conserved features. Some peptides demonstrate significant paralog-level specificity, although the physicochemical explanations for this specificity are often not clear. Our data demonstrate the power of cyclic peptides to discriminate between very similar proteins with high potency and hint that differences in conformational dynamics might modulate the affinity of these domains for particular ligands.

Cascade Synthesis of Fluorinated Spiroheterocyclic Scaffolding for Peptidic Macrobicycles

Octafluorocyclopentene (OFCP) engages linear, unprotected peptides in polysubstitution cascades that generate complex fluorinated polycycles. The reactions occur in a single flask at 0-25 °C and require no catalysts or heavy metals. OFCP can directly polycyclize linear sequences using native functionality, or fluorospiroheterocyclic intermediates can be intercepted with exogenous nucleophiles. The latter tactic generates molecular hybrids composed of peptides, sugars, lipids, and heterocyclic components. The platform can create stereoisomers of both single- and double-looped macrocycles. Calculations indicate that the latter can mimic diverse protein surface loops. Subsets of the molecules have low energy conformers that shield the polar surface area through intramolecular hydrogen bonding. A significant fraction of OFCP-derived macrocycles tested show moderate to high passive permeability in parallel artificial membrane permeability assays.

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Fine-Tuning and Enhancement of pH-Dependent Membrane Permeation of Cyclic Peptides by Utilizing Noncanonical Amino Acids with Extended Side Chains

The development of cyclic peptides that exhibit pH-sensitive membrane permeation is a promising strategy for tissue-selective drug delivery. We investigated the pH-dependent interactions of designed cyclic peptides bearing noncanonical amino acids of long acidic side chains with lipid membranes, including surface binding, insertion, and translocation across the membrane. As the length of the side chain of acidic amino acid increased, the binding affinity of the peptides to phosphatidylcholine bilayer surfaces decreased, while the pH for the 50% insertion of the peptides into the bilayers increased. The pH for membrane permeation of the peptides increased with the side chain length, resulting in specific membrane permeation at pH ∼6.5. The longer side chain of acidic amino acids improved the maximum rate of membrane permeation at low pH, where both entropic and enthalpic contributions affected the permeation. Our peptide also showed intracellular delivery of cargo molecules into living cells in a pH-dependent manner.

Wetting hysteresis induces effective unidirectional water transport through a fluctuating nanochannel

We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric noise fluctuations imposed on the channel radius cause unidirectional water flow without osmotic pressure, which can be attributed to hysteresis in the cyclic transition between the wetting/drying states. We show that the water transport depends on fluctuations, such as white, Brownian, and pink noises. Because of the high-frequency components in white noise, fast switching of open and closed states inhibits channel wetting. Conversely, pink and Brownian noises generate high-pass filtered net flow. Brownian fluctuation leads to a faster water transport rate, whereas pink noise has a higher capability to overcome pressure differences in the opposite direction. A trade-off relationship exists between the resonant frequency of the fluctuation and the flow amplification. The proposed pump can be considered as an analogy for the reversed Carnot cycle, which is the upper limit of the energy conversion efficiency.

Leveraging deep learning to improve vaccine design

Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.

Amide-to-ester substitution as a stable alternative to N-methylation for increasing membrane permeability in cyclic peptides

Naturally occurring peptides with high membrane permeability often have ester bonds on their backbones. However, the impact of amide-to-ester substitutions on the membrane permeability of peptides has not been directly evaluated. Here we report the effect of amide-to-ester substitutions on the membrane permeability and conformational ensemble of cyclic peptides related to membrane permeation. Amide-to-ester substitutions are shown to improve the membrane permeability of dipeptides and a model cyclic hexapeptide. NMR-based conformational analysis and enhanced sampling molecular dynamics simulations suggest that the conformational transition of the cyclic hexapeptide upon membrane permeation is differently influenced by an amide-to-ester substitution and an amide N-methylation. The effect of amide-to-ester substitution on membrane permeability of other cyclic hexapeptides, cyclic octapeptides, and a cyclic nonapeptide is also investigated to examine the scope of the substitution. Appropriate utilization of amide-to-ester substitution based on our results will facilitate the development of membrane-permeable peptides.

Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design

Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.

Cyclic peptide structure prediction and design using AlphaFold

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 Å. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7-13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 Å), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

Potent De Novo Macrocyclic Peptides That Inhibit O-GlcNAc Transferase through an Allosteric Mechanism

Glycosyltransferases are a superfamily of enzymes that are notoriously difficult to inhibit. Here we apply an mRNA display technology integrated with genetic code reprogramming, referred to as the RaPID (random non-standard peptides integrated discovery) system, to identify macrocyclic peptides with high binding affinities for O-GlcNAc transferase (OGT). These macrocycles inhibit OGT activity through an allosteric mechanism that is driven by their binding to the tetratricopeptide repeats of OGT. Saturation mutagenesis in a maturation screen using 39 amino acids, including 22 non-canonical residues, led to an improved unnatural macrocycle that is ≈40 times more potent than the parent compound (K_i ^app =1.5 nM). Subsequent derivatization delivered a biotinylated derivative that enabled one-step affinity purification of OGT from complex samples. The high potency and novel mechanism of action of these OGT ligands should enable new approaches to elucidate the specificity and regulation of OGT.

The cyclic peptide G4CP2 enables the modulation of galactose metabolism in yeast by interfering with GAL4 transcriptional activity

Genetically-encoded combinatorial peptide libraries are convenient tools to identify peptides to be used as therapeutics, antimicrobials and functional synthetic biology modules. Here, we report the identification and characterization of a cyclic peptide, G4CP2, that interferes with the GAL4 protein, a transcription factor responsible for the activation of galactose catabolism in yeast and widely exploited in molecular biology. G4CP2 was identified by screening CYCLIC, a Yeast Two-Hybrid-based combinatorial library of cyclic peptides developed in our laboratory. G4CP2 interferes with GAL4-mediated activation of galactose metabolic enzymes both when expressed intracellularly, as a recombinant peptide, and when provided exogenously, as a chemically-synthesized cyclic peptide. Our results support the application of G4CP2 in microbial biotechnology and, additionally, demonstrate that CYCLIC can be used as a tool for the rapid identification of peptides, virtually without any limitations with respect to the target protein. The possible biotechnological applications of cyclic peptides are also discussed.

Customized Reversible Stapling for Selective Delivery of Bioactive Peptides

We have developed a new concept for reversible peptide stapling that involves macrocyclization between two amino groups and decyclization promoted via dual 1,4-elimination. Depending on the trigger moiety, this strategy could be employed to selectively deliver peptides to either intracellular or extracellular targets. As a proof of concept, a peptide inhibitor targeting a lysine-specific demethylase 1 (LSD1) was temporarily cyclized to enhance its stability and ability to cross the cell membrane. Once inside the cells, the biologically active linear peptide was released under reducing environment. Moreover, we have developed reversibly stapled peptides using antimicrobial peptides (RStAMPs) whose bioactive helical conformation can be temporarily destabilized by stapling the peptide backbone. The resulting helix-distorted RStAMPs are nontoxic and highly resistant to protease hydrolysis, while at the infection site, RStAMPs can be rapidly activated by the overproduced H₂O₂ through the dual 1,4-elimination. The latter restored the helical structure of the native peptide and its antimicrobial activity. This work illustrates a highly valuable macrocyclization strategy for the peptide community and should greatly benefit the field of peptide delivery.