Peptide therapeutics: Targeting the undruggable space

Targeting anti-apoptotic mechanisms in tumour cells: Strategies for enhancing Cancer therapy

Anti-cancer drug's cytotoxicity is determined by their ability to induce predetermined cell demise, commonly called apoptosis. The cancer-causing cells are able to evade cell death, which has been affiliated with both malignancy as well as resistance to cancer treatments. In order to avoid cell death, cancerous tumour cells often produce an abundance of anti-apoptotic proteins, becoming "dependent" on them. Consequently, protein inhibitors of cell death may prove to be beneficial as pharmacological targets for the future creation of cancer therapies. This article examines the molecular routes of apoptosis, its clinical manifestations, anti-cancer therapy options that target the intrinsic mechanism of apoptosis, proteins that prevent cell death, and members of the B-lymphoma-2 subset. In addition, novel approaches to cell death are highlighted, including how curcumin mitigates chemotherapy-induced apoptosis in healthy tissues and the various ways melatonin modifies apoptosis to improve cancer treatment efficacy, particularly through the TNF superfamily. Cancer treatment-induced increases in anti-apoptotic proteins lead to drug resistance; yet, ligands that trigger cell death by inhibiting these proteins are expected to improve chemotherapy's efficacy. The potential of frequency-modulated dietary phytochemicals as a cancer therapeutic pathway, including autophagy and apoptosis, is also explored. This approach may be more efficient than inhibition alone in overcoming drug resistance. Consequently, this method has the potential to allow for lower medication concentrations, reducing cytotoxicity and unwanted side effects.

Increased Throughput of Combined Stability Testing and Metabolite Identification Using a Sample Multiplexing Strategy for the Optimization of Engineered Cyclic Peptide Drugs

Engineered cyclic peptides (ECPs) have been in the spotlight as novel drug modalities for challenging therapeutic targets. Oral delivery of engineered cyclic peptides benefits from ease of administration. However, one of the main hurdles in developing orally effective peptide drugs is their potential metabolic instability due to enzymatic degradation. To that end, in vitro experiments with simulated intestinal fluid (SIF) have been used to assess drug metabolic stability in the gastrointestinal tract. Currently, metabolic stability evaluations and biotransformation assessments are performed separately, which can be time-consuming and result in complex data analysis. Presented here is a sample multiplexing strategy to address these challenges by leveraging a Thermo Scientific Orbitrap Astral mass spectrometer with two complementary analyzers, enabling the simultaneous analysis of metabolic stability from the Orbitrap full scan and biotransformation from the Astral analyzer from one sample injection. Furthermore, we demonstrate that 10 engineered cyclic peptides can be pooled into one sample injection without compromising the data quality to decrease the instrument run time and improve the throughput of the assay.

Cyclic peptide structure prediction and design using AlphaFold2

Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into the designed structures with high confidence. X-ray crystal structures for eight tested de novo designed sequences match very closely with the design models (RMSD < 1.0 Å), highlighting the atomic level accuracy in our approach. Further, we used the set of hallucinated peptides as starting scaffolds to design binders with nanomolar IC50 against MDM2 and Keap1. The computational methods and scaffolds developed here provide the basis for the custom design of peptides for diverse protein targets and therapeutic applications.

PepINVENT: generative peptide design beyond natural amino acids

Peptides play a crucial role in drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the peptide properties such as binding affinity, plasma stability and permeability. Incorporating novel NNAAs facilitates the design of more effective peptides with improved properties. The generative models used in the field have focused on navigating the peptide sequence space. The sequence space is formed by combinations of a predefined set of amino acids. However, there is still a need for a tool to explore the peptide landscape beyond this enumerated space to unlock and effectively incorporate the de novo design of new amino acids. To thoroughly explore the theoretical chemical space of peptides, we present PepINVENT, a novel generative AI-based tool as an extension to the small molecule molecular design platform, REINVENT. PepINVENT navigates the vast space of natural and non-natural amino acids to propose valid, novel, and diverse peptide designs. The generative model can serve as a central tool for peptide-related tasks, as it was not trained on peptides with specific properties or topologies. The prior was trained to understand the granularity of peptides and to design amino acids for filling the masked positions within a peptide. PepINVENT coupled with reinforcement learning enables the goal-oriented design of peptides using its chemistry-informed generative capabilities. This study demonstrates PepINVENT's ability to explore the peptide space with unique and novel designs and its capacity for property optimization in the context of therapeutically relevant peptides. Our tool can be employed for multi-parameter learning objectives, peptidomimetics, lead optimization, and a variety of other tasks within the peptide domain.

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as such, peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with noncanonical amino acids. By integrating multigranularity information at the residual and atomic level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide root-mean-square deviation (RMSD) for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with noncanonical amino acids, offering great potential for peptide drug design and advancing biomedical research.

Lipid Shape as a Membrane Activity Modulator of a Fusogenic Antimicrobial Peptide

An intriguing feature of many bacterial membranes is their prevalence of non-bilayer-forming lipids, such as the cone-shaped phosphatidylethanolamines and cardiolipins. Many membrane-active antimicrobial peptides lower the bilayer-to-hexagonal phase transition energy barrier in membranes containing such types of cone-shaped lipids. Here, we systematically studied how the molecular shape of lipids affects the activity of antimicrobial peptide EcDBS1R4, which is known to be an efficient fusogenic peptide. Using coarse-grained molecular dynamics simulations, we show the ability of EcDBS1R4 to form "hourglass-shaped" pores, which is inhibited by cone-shaped lipids. The abundance of cone-shaped lipids further correlates with the propensity of this peptide to oligomerize preferentially in antiparallel dimers. We also observe that EcDBS1R4 promotes the segregation of the anionic lipids. When coupled to dimerization, this charge segregation leads to regions in the bilayer that are devoid of peptides and rich in zwitterionic lipids. Our results indicate a protective role of cone-shaped lipids in bacterial membranes against pore-mediated permeabilization by EcDBS1R4.

ReaxFF-Guided Optimization of VIRIP-Based HIV-1 Entry Inhibitors

Peptides hold great promise for safe and effective treatment of viral infections. However, their use is often constrained by limited efficacy and high production costs, especially for long or complex peptide chains. Here, we used ReaxFF molecular dynamics (MD) simulations to optimize the size and activity of VIRIP (Virus Inhibitory Peptide), a naturally occurring 20-residue fragment of α1-antitrypsin that binds the HIV-1 GP41 fusion peptide (FP), thereby blocking viral fusion and entry into host cells. Specifically, we used the NMR structure of the complex between an optimized VIRIP derivative (VIR-165) and the HIV-1 gp41 FP for ReaxFF-guided in silico analysis, evaluating the contribution of each amino acid in the interaction of the inhibitor with its viral target. This approach allowed us to reduce the size of the HIV-1 FP inhibitor from 20 to 10 amino acids (2.28-1.11 kDa). HIV-1 infection assays showed that the size-optimized VIRIP derivative (soVIRIP) retains its broad-spectrum anti-HIV-1 capability and is nontoxic in the vertebrate zebrafish model. Compared to the original VIRIP, soVIRIP displayed more than 100-fold higher antiviral activity (IC50 of ∼120 nM). Thus, it is more potent than a dimeric 20-residue VIRIP derivative (VIR-576) that was proven safe and effective in a phase I/II clinical trial. Our results show that ReaxFF-based MD simulations represent a suitable approach for the optimization of therapeutic peptides.

Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged by the fact that approximately half of the human proteome is made up of proteins that do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered proteins (IDPs), are involved in numerous physiological functions and are associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), and type 2 diabetes. Targeting these proteins is challenging for two reasons: i) we need to preserve their physiological functions, and ii) drug design by molecular docking is not possible due to the lack of reliable starting conditions. Faced with this challenge, the solutions proposed by artificial intelligence (AI) such as AlphaFold are clearly unsuitable. Instead, we suggest an innovative approach consisting of mimicking, in short synthetic peptides, the conformational flexibility of IDPs. These peptides, which we call adaptive peptides, are derived from the domains of IDPs that become structured after interacting with a ligand. Adaptive peptides are designed with the aim of selectively antagonizing the harmful effects of IDPs, without targeting them directly but through selected ligands, without affecting their physiological properties. This "target the target, not the arrow" strategy is promised to open a new route to drug discovery for currently undruggable proteins.

Assessment of Phage-Displayed Peptides Targeting Cancer Cell Surface Proteins: A Comprehensive Molecular Docking Study

Peptides binding overexpressed breast and cervical cancer cell surface proteins can be isolated by phage display technology, and their affinity to their potential receptors can be assessed by molecular docking. We isolated 44 phage clones displaying dodecapeptides with high affinity to HeLa cervical cancer and MDA-MB-231 (MDA) breast cancer cells by repeated biopanning of an MK13 phage library and explored their affinity to specific proteins by molecular docking. Six peptides appeared repeatedly during biopanning: two with affinity to HeLa (H5/H21), and four with affinity to MDA cells (M3/M7/M15/M17). Peptide pairs M3/H5 and H1/M17 had affinity to both cell lines. A systematic review identified Annexin A2, EGFR, CD44, CD146, and Integrin alpha V as potential protein targets in HeLa cells, and Vimentin, Galectin-1, and Annexins A1 and A5 in MDA cells. Via virtual screening, we selected six peptides with the highest total docking scores: H1 (-916.32), H6 (-979.21), H19 (-1093.24), M6 (-732.21), M16 (-745.5), and M19 (-739.64), and identified that docking scores were strengthened by the protein type, the interacting amino acid side chains, and the polarity of peptides. This approach facilitates the selection of relevant peptides that could be further explored for active targeting in cancer diagnosis and treatment.

Peptide Double-Stapling and Arginine N-Glycosylation Triggered the Development of Therapeutic Antimicrobial Peptides Capable of Killing Drug-Resistant Bacteria in Mice

Antimicrobial peptides SAAP-148 exhibited excellent antimicrobial activities but suffered from inherent disadvantages, including cytotoxicity and poor proteolytic stability. Herein, we developed a novel strategy combining one unique silver-catalyzed solid-phase glycosylation-enabled arginine N-glycosylation strategy and all-hydrocarbon peptide double-stapling, and five-round peptide libraries were rationally constructed containing over 50 stapled and/or arginine N-glycosylated peptides. SLP-51 consisting of two introduced all-hydrocarbon staples and the C-terminal arginine glycosylation exhibited superior in vitro antimicrobial activities against drug-resistant Gram-positive or -negative clinical isolates. SLP-51 also exhibited improved proteolytic stability than the parent peptide SLP-0, and importantly, significantly weakened hemolysis. Experimental and modeling mechanism research indicated that SLP-51 exerted similar but stronger killing abilities by destroying the integrality of the bacterial membranes. In both skin wound and drug-resistant bacterial pneumonia models, SLP-51 showcased a potent therapeutic effect in treating both MRSA and Klebsiella pneumoniae infection in vivo and dramatical improvement of inflammatory injury.

Neo-Glycolipid Oximes as Intestinal Permeation Enhancers for Peptide Hormone PYY3-36

Herein, we describe the design and synthesis of 16 neo-glycolipids that are potential permeation enhancers for oral drug delivery of peptide therapeutics. These amphiphilic neo-glycolipids are composed of fatty acids and various carbohydrates (d-glucose, lactose, cellobiose, maltose) via an oxime linker. The ability of the synthesized neo-glycolipids to enhance permeation of fluorescein-labelled dextran (4 kDa) or 3H-mannitol across intestinal epithelium was investigated in vitro using monolayers of human epithelial Caco-2 cells. Their effects were compared with (pre-)clinically known enhancers as reference compounds; sodium salts of octanoic, decanoic, and dodecanoic acid, and sodium salcaprozate (SNAC). Most neo-glycolipids increased the permeation of the model compounds, proving that neo-glycolipids, which possess vastly different properties from the reference compounds, e. g., in terms of clogD and polar surface area, are effective permeation enhancers. The neo-glycolipid based on decanoic acid and glucose was more potent than related compounds based on disaccharides. Significant differences in solubility and cellular compatibility were found for neo-glyolipids based on different carbohydrates. Finally, neo-glycolipids were evaluated as permeation enhancers for the peptide hormone PYY3-36. Glucose- and maltose-derived neo-glycolipids based on decanoic and dodecanoic acid showed promising enhancements in PYY3-36 permeation in vitro while maintaining good cellular compatibility, relevant for oral delivery of obesity treatments.

New insights into protein-protein interaction modulators in drug discovery and therapeutic advance

Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered to be undruggable targets have become increasingly feasible due to the progress that has been made over the last two decades and the rapid technological advances. This work explores the influence of technological innovations on PPI research and development. Additionally, the diverse strategies for discovering, modulating, and characterizing PPIs and their corresponding modulators are examined with the aim of presenting a streamlined pipeline for advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in PPI modulator discovery and development, we aim to illustrate the efficacy of various strategies for identifying, optimizing, and overcoming challenges associated with PPI modulator design. The valuable lessons and insights gained from the identification, optimization, and approval of PPI modulators are discussed with the aim of demonstrating that PPI modulators have transitioned beyond early-stage drug discovery and now represent a prime opportunity with significant potential. The selected examples of PPI modulators encompass those developed for cancer, inflammation and immunomodulation, as well as antiviral applications. This perspective aims to establish a foundation for the effective targeting and modulation of PPIs using PPI modulators and pave the way for future drug development.

Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.

Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics

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 ever-expanding 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.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

Accurate de novo design of high-affinity protein binding macrocycles using deep learning

The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate KD between 1-10 μM, and the best anti-GABARAP macrocycle binds with a KD of 6 nM and a sub-nanomolar IC50 in vitro. For one of the targets, RbtA, we obtain a high-affinity binder with KD < 10 nM despite starting from the target sequence alone due to the lack of an experimentally determined target structure. X-ray structures determined for macrocycle-bound MCL1, GABARAP, and RbtA complexes match very closely with the computational design models, with three out of the four structures demonstrating Ca RMSD of less than 1.5 Å to the design models. In contrast to library screening approaches for which determining binding mode can be a major bottleneck, the binding modes of RFpeptides-generated macrocycles are known by design, which should greatly facilitate downstream optimization. RFpeptides thus provides a powerful framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.

Biocatalytic Tetrapeptide Macrocyclization by Cryptic Penicillin-binding Protein-type Thioesterases

Cyclic tetrapeptides (CTPs) are a diverse class of natural products with a broad range of biological activities. However, they are extremely challenging to synthesize due to the ring strain associated with their small ring size. While chemical methods have been developed to access CTPs, they generally require the presence of certain amino acids, limiting their substrate scopes. Herein, we report the first bioinformatics guided discovery of a thioesterase from a cryptic biosynthetic gene cluster for peptide cyclization. Specifically, we hypothesized that predicted Penicillin-binding type thioesterases (PBP-TEs) from cryptic nonribosomal peptide synthetase gene clusters containing four adenylation domains would catalyze tetrapeptide cyclization. We found that one of the predicted PBP-TEs, WP516, efficiently cyclizes a wide variety of tetrapeptide substrates. To date, it is only the second stand-alone enzyme capable of cyclizing tetrapeptides, and its substrate scope greatly surpasses that of the only other reported tetrapeptide cyclase Ulm16. AlphaFold modeling and covalent docking were used to rationalize the broad substrate scope of WP516 in comparison to other PBP-TEs. Overall, the bioinformatics guided workflow outlined in this paper, and the discovery of WP516, represent promising tools for the biocatalytic production of head-to-tail CTPs, as well as a more general strategy for discovery of enzymes for peptide cyclization.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

Designed Cell-Penetrating Peptide Constructs for Inhibition of Pathogenic Protein Self-Assembly

Peptides possess a number of pharmacologically desirable properties, including greater chemical diversity than other biomolecule classes and the ability to selectively bind to specific targets with high potency, as well as biocompatibility, biodegradability, and ease and low cost of production. Consequently, there has been considerable interest in developing peptide-based therapeutics, including amyloid inhibitors. However, a major hindrance to the successful therapeutic application of peptides is their poor delivery to target tissues, cells or subcellular organelles. To overcome these issues, recent efforts have focused on engineering cell-penetrating peptide (CPP) antagonists of amyloidogenesis, which combine the attractive intrinsic properties of peptides with potent therapeutic effects (i.e., inhibition of amyloid formation and the associated cytotoxicity) and highly efficient delivery (to target tissue, cells, and organelles). This review highlights some promising CPP constructs designed to target amyloid aggregation associated with a diverse range of disorders, including Alzheimer's disease, transmissible spongiform encephalopathies (or prion diseases), Parkinson's disease, and cancer.

Membrane-active peptides for anticancer therapies

Membrane-active peptides are found in many living organisms and play a critical role in their immune systems by combating various infectious diseases. These host defense peptides employ multiple mechanisms against different microorganisms and possess unique functions, such as anti-inflammatory and immunomodulatory effects, often working in synergy with other antimicrobial agents. Despite extensive research over the past few decades and the identification of thousands of sequences, only a few have been successfully applied in clinical settings and received approval from the U.S. Food and Drug Administration. In this chapter, we explore all peptide therapeutics that have reached the market, as well as candidates in preclinical and clinical trials, to understand their success and potential applications in cancer therapy. Our findings indicate that at least four membrane-active peptide drugs have progressed to preclinical or clinical phases, dmonstrating promising results for cancer treatment. We summarize our insights in this chapter, highlighting the potential of membrane-active anticancer peptide therapeutics and their applications as targeting ligands in various biomedical fields.

Proteolysis Targeting Chimera Agents (PROTACs): New Hope for Overcoming the Resistance Mechanisms in Oncogene-Addicted Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) remains a disease with a poor prognosis despite the advances in therapies. NSCLC with actionable oncogenic alterations represent a subgroup of diseases for which tyrosine kinase inhibitors (TKIs) have shown relevant and robust impact on prognosis, both in early and advanced stages. While the introduction of powerful TKIs increases the ratio of potentially curable patients, the disease does develop resistance over time through either secondary mutations or bypass activating tracks. Therefore, new treatment strategies are being developed to either overcome this inevitable resistance or to prevent it, and proteolysis targeting chimera agents (PROTACs) are among them. They consist of two linked molecules that bind to a target protein and an E3 ubiquitin ligase that causes ubiquitination and degradation of proteins of interest. In this paper, we review the rationale for PROTAC therapy and the current development of PROTACs for oncogene-addicted lung cancer. Moreover, we critically analyze the strengths and limitations of this promising technique that may help pave the way for future perspectives.

Peptides for Targeting Chondrogenic Induction and Cartilage Regeneration in Osteoarthritis

OBJECTS

Osteoarthritis (OA) is a widespread degenerative joint condition commonly occurring in older adults. Currently, no disease-modifying drugs are available, and safety concerns associated with commonly used traditional medications have been identified. In this review, a significant portion of research in this field is concentrated on cartilage, aiming to discover methods to halt cartilage breakdown or facilitate cartilage repair.

METHODS

Researchers have mainly investigated the cartilage, seeking methods to promote its repair. This review focuses on peptide-based molecules known for their ability to selectively bind to growth factor cytokines and components of the cartilage extracellular matrix.

RESULTS

Chondroinductive peptides, synthetically producible, boast superior reproducibility, stability, modifiability, and yield efficiency over natural biomaterials. This review outlines a chondroinductive peptide design, molecular mechanisms, and their application in cartilage tissue engineering and also compares their efficacy in chondrogenesis in vitro and in vivo.

CONCLUSIONS

In this paper, we will summarize the application of peptides engineered to regenerate cartilage by acting as scaffolds, functional molecules, or both and discuss additional possibilities for peptides. This review article provides an overview of our current understanding of chondroinductive peptides for treating OA-affected cartilage and explores the delivery systems used for regeneration. These advancements may hold promise for enhancing or even replacing current treatment methodologies.

Stabilization of 310-Helices in Macrocycles Using Dominant Rotor Methodology

Stabilization of biologically relevant structural motifs has been a long-standing challenge. Here we show that atropisomeric dominant rotors can stabilize rare 310-helices in macrocycles. The target molecules were prepared using solid-phase peptide synthesis and subjected to extensive structural analysis. Molecular dynamics (MD) simulations enabled us to acquire solution structures for the target molecules, which offered evidence for stable 310-helix formation, ordinarily a metastable state. The 310-helices were shown to retain helicity after heating to 100 °C for 72 h. Moreover, the crude atropisomeric mixtures could be thermally enriched toward 310-helical macrocycles with selectivities of >20:1.

Discovery of a peptide proteolysis-targeting chimera (PROTAC) drug of p300 for prostate cancer therapy

BACKGROUND

The E1A-associated protein p300 (p300) has emerged as a promising target for cancer therapy due to its crucial role in promoting oncogenic signaling pathways in various cancers, including prostate cancer. This need is particularly significant in prostate cancer. While androgen deprivation therapy (ADT) has demonstrated promising efficacy in prostate cancer, its long-term use can eventually lead to the development of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). Notably, p300 has been identified as an important co-activator of the androgen receptor (AR), highlighting its significance in prostate cancer progression. Moreover, recent studies have revealed the involvement of p300 in AR-independent oncogenes associated with NEPC. Therefore, the blockade of p300 may emerge as an effective therapeutic strategy to address the challenges posed by both CRPC and NEPC.

METHODS

We employed AI-assisted design to develop a peptide-based PROTAC (proteolysis-targeting chimera) drug that targets p300, effectively degrading p300 in vitro and in vivo utilizing nano-selenium as a peptide drug delivery system.

FINDINGS

Our p300-targeting peptide PROTAC drug demonstrated effective p300 degradation and cancer cell-killing capabilities in both CRPC, AR-negative, and NEPC cells. This study demonstrated the efficacy of a p300-targeting drug in NEPC cells. In both AR-positive and AR-negative mouse models, the p300 PROTAC drug showed potent p300 degradation and tumor suppression.

INTERPRETATION

The design of peptide PROTAC drug targeting p300 is feasible and represents an efficient therapeutic strategy for CRPC, AR-negative prostate cancer, and NEPC.

FUNDING

The funding details can be found in the Acknowledgements section.

The Anti-Inflammatory Effect of SDF-1 Derived Peptide on Porphyromonas gingivalis Infection via Regulation of NLRP3 and AIM2 Inflammasome

(1) Background: Peptides are appealing as pharmacological materials because they are easily produced, safe, and tolerable. Despite increasing gum-care awareness, periodontitis is still prevalent and is influenced by factors like high sugar consumption, smoking, and aging. Porphyromonas gingivalis is considered a major etiologic agent of periodontitis and activates the NLR family pyrin domain containing 3 (NLRP3) but is absent in melanoma 2 (AIM2) inflammasomes, resulting in pro-inflammatory cytokine release. (2) Methods: We examined the anti-inflammatory effects of 18 peptides derived from human stromal cell-derived factor-1 (SDF-1) on THP-1 macrophages. Inflammation was induced by P. gingivalis, and the anti-inflammatory effects were analyzed using molecular biological techniques. In a mouse periodontitis model, alveolar bone resorption was assessed using micro-CT. (3) Results: Of the 18 SDF-1-derived peptides, S10 notably reduced IL-1β and TNF-α secretion. S10 also diminished the P. gingivalis-induced expression of NLRP3, AIM2, ASC (apoptosis-associated speck-like protein), caspase-1, and IL-1β. Furthermore, S10 attenuated the enhanced TLR (toll-like receptor) signaling pathway and decreased the phosphorylation of nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs). In addition, S10 mitigated alveolar bone loss in our P. gingivalis-induced mouse model of periodontitis. (4) Conclusions: S10 suppressed TLR/NF-κB/NLRP3 inflammasome signaling and the AIM2 inflammasome in our P. gingivalis-induced murine periodontitis model, which suggests that it has potential use as a therapeutic treatment for periodontitis.

Cysteine-Specific Multifaceted Bioconjugation of Peptides and Proteins Using 5-Substituted 1,2,3-Triazines

Peptide and protein postmodification have gained significant attention due to their extensive impact on biomolecule engineering and drug discovery, of which cysteine-specific modification strategies are prominent due to their inherent nucleophilicity and low abundance. Herein, the study introduces a novel approach utilizing multifunctional 5-substituted 1,2,3-triazine derivatives to achieve multifaceted bioconjugation targeting cysteine-containing peptides and proteins. On the one hand, this represents an inaugural instance of employing 1,2,3-triazine in biomolecular-specific modification within a physiological solution. On the other hand, as a powerful combination of precision modification and biorthogonality, this strategy allows for the one-pot dual-orthogonal functionalization of biomolecules utilizing the aldehyde group generated simultaneously. 1,2,3-Triazine derivatives with diverse functional groups allow conjugation to peptides or proteins, while bi-triazines enable peptide cyclization and dimerization. The examination of the stability of bi-triazines revealed their potential for reversible peptide modification. This work establishes a comprehensive platform for identifying cysteine-selective modifications, providing new avenues for peptide-based drug development, protein bioconjugation, and chemical biology research.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Tetrazine cyclized peptides for one-bead-one-compound library: Synthesis and sequencing

While most FDA-approved peptide drugs are cyclic, robust cyclization chemistry of peptides and the deconvolution of the cyclic peptide sequences using tandem mass spectrometry render cyclic peptide drug discovery difficult. In this chapter, the protocol for the successful synthesis of tetrazine-linked cyclic peptide library in solid phase, which shows both robust cyclization and easy sequence deconvolution, is described. The protocol for the linearization and cleavage of cyclic peptides from the solid phase by simple UV light irradiation, followed by accurate sequencing using tandem mass spectrometry, is described. We describe the troubleshooting for this dithiol bis-arylation reaction and for the successful cleavage of the aryl cyclic peptide into linear form. This method for efficient solid-phase macrocyclization can be used for the rapid production of loop-based peptides and screening for inhibition of protein-protein interactions, by using the covalent inverse electron-demand Diels Alder reaction to supplement the non-covalent interaction between a protein and its peptide binder, isolating highly selective peptides in the process.

Cell penetration of oxadiazole-containing macrocycles

Passive membrane permeability is an important property in drug discovery and biological probe design. To elucidate the cell-penetrating ability of oxadiazole-containing (Odz) peptides, we employed the Chloroalkane Penetration Assay. The present study demonstrates that Odz cyclic peptides can be highly cell-penetrant depending on the position of specific side chains and the chloroalkane tag. Solution NMR shows that Odz cyclic peptides adopt a β-turn conformation. However, despite observing high cell penetration, we observed low passive permeability in experiments with artificial membranes. These findings highlight the complexity of controlling cell penetration for conformationally sensitive macrocycles and suggest that Odz cyclic peptides may provide a framework for designing cell-penetrant cyclic peptides.

Therapeutic proteins: developments, progress, challenges, and future perspectives

Proteins are considered magic molecules due to their enormous applications in the health sector. Over the past few decades, therapeutic proteins have emerged as a promising treatment option for various diseases, particularly cancer, cardiovascular disease, diabetes, and others. The formulation of protein-based therapies is a major area of research, however, a few factors still hinder the large-scale production of these therapeutic products, such as stability, heterogenicity, immunogenicity, high cost of production, etc. This review provides comprehensive information on various sources and production of therapeutic proteins. The review also summarizes the challenges currently faced by scientists while developing protein-based therapeutics, along with possible solutions. It can be concluded that these proteins can be used in combination with small molecular drugs to give synergistic benefits in the future.

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.

Degradation of Polo-like Kinase 1 by the Novel Poly-Arginine N-Degron Pathway PROTAC Regulates Tumor Growth in Nonsmall Cell Lung Cancer

Polo-like kinase 1 (PLK1), which is crucial in cell cycle regulation, is considered a promising anticancer drug target. Herein, we present the N-degron pathway-based proteolysis targeting chimera (PROTAC) for PLK1 degradation, targeting the Polo-box domain (PBD). We identified DD-2 as the most potent PROTAC that selectively induces PLK1 degradation in cancer cells, including HeLa and nonsmall cell lung cancer (NSCLC), through the N-degron pathway. DD-2 exhibited significant in vitro anticancer effects, inducing G2/M arrest and apoptosis in HeLa and NSCLC cell lines. DD-2 showed significant tumor growth inhibition in a xenograft mouse model using HeLa and NSCLC cell lines, highlighting its potential in cancer treatment. Furthermore, the combination of DD-2 with tyrosine kinase inhibitor (TKI), osimertinib, effectively suppressed tumor growth in double-mutated H1975 cell lines, emphasizing DD-2's potential in combination cancer therapies. Collectively, this study demonstrates the potential of the N-degron pathway, especially using DD-2, for targeted cancer therapies.

Peptide Therapeutics: Unveiling the Potential against Cancer-A Journey through 1989

The United States Food and Drug Administration (FDA) has approved a plethora of peptide-based drugs as effective drugs in cancer therapy. Peptides possess high specificity, permeability, target engagement, and a tolerable safety profile. They exhibit selective binding with cell surface receptors and proteins, functioning as agonists or antagonists. They also serve as imaging agents for diagnostic applications or can serve a dual-purpose as both diagnostic and therapeutic (theragnostic) agents. Therefore, they have been exploited in various forms, including linkers, peptide conjugates, and payloads. In this review, the FDA-approved prostate-specific membrane antigen (PSMA) peptide antagonists, peptide receptor radionuclide therapy (PRRT), somatostatin analogs, antibody-drug conjugates (ADCs), gonadotropin-releasing hormone (GnRH) analogs, and other peptide-based anticancer drugs are analyzed in terms of their chemical structures and properties, therapeutic targets and mechanisms of action, development journey, administration routes, and side effects.

Potential of oligonucleotide- and protein/peptide-based therapeutics in the management of toxicant/stressor-induced diseases

Exposure to toxicants/stressors has been linked to the development of many human diseases. They could affect various cellular components, such as DNA, proteins, lipids, and non-coding RNAs (ncRNA), thereby triggering various cellular pathways, particularly oxidative stress, inflammatory responses, and apoptosis, which can contribute to pathophysiological states. Accordingly, modulation of these pathways has been the focus of numerous investigations for managing related diseases. The involvement of various ncRNAs, such as small interfering RNA (siRNA), microRNAs (miRNA), and long non-coding RNAs (lncRNA), as well as various proteins and peptides in mediating these pathways, provides many target sites for pharmaceutical intervention. In this regard, various oligonucleotide- and protein/peptide-based therapies have been developed to treat toxicity-induced diseases, which have shown promising results in vitro and in vivo. This comprehensive review provides information about various aspects of toxicity-related diseases including their causing factors, main underlying mechanisms and intermediates, and their roles in pathophysiological states. Particularly, it highlights the principles and mechanisms of oligonucleotide- and protein/peptide-based therapies in the treatment of toxicity-related diseases. Furthermore, various issues of oligonucleotides and proteins/peptides for clinical usage and potential solutions are discussed.

Lipid-Based Nanoparticles as Oral Drug Delivery Systems: Overcoming Poor Gastrointestinal Absorption and Enhancing Bioavailability of Peptide and Protein Therapeutics

Delivery and formulation of oral peptide and protein therapeutics have always been a challenge for the pharmaceutical industry. The oral bioavailability of peptide and protein therapeutics mainly relies on their gastrointestinal solubility and permeability which are affected by their poor membrane penetration, high molecular weight and proteolytic (chemical and enzymatic) degradation resulting in limited delivery and therapeutic efficacy. The present review article highlights the challenges and limitations of oral delivery of peptide and protein therapeutics focusing on the application, potential and importance of solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) as lipid-based drug delivery systems (LBDDSs) and their advantages and drawbacks. LBDDSs, due to their lipid-based matrix can encapsulate both lipophilic and hydrophilic drugs, and by reducing the first-pass effect and avoiding proteolytic degradation offer improved drug stability, dissolution rate, absorption, bioavailability and controlled drug release. Furthermore, their small size, high surface area and surface modification increase their mucosal adhesion, tissue-targeted distribution, physiological function and half-life. Properties such as simple preparation, high-scale manufacturing, biodegradability, biocompatibility, prolonged half-life, lower toxicity, lower adverse effects, lipid-based structure, higher drug encapsulation rate and various drug release profile compared to other similar carrier systems makes LBDDSs a promising drug delivery system (DDS). Nevertheless, undesired physicochemical features of peptide and protein drug development and discovery such as plasma stability, membrane permeability and circulation half-life remain a serious challenge which should be addressed in future.

Lipid bilayer membrane permeability mechanism of the K-Ras(G12D)-inhibitory bicyclic peptide KS-58 elucidated by molecular dynamics simulations

Peptides are mid-size molecules (700-2000 g/mol) and have attracted particular interest as therapeutic modalities as they are superior in controlling protein-protein interactions, a process that is a typical drug target category, compared with small molecules (<500 g/mol). In 2020, we identified KS-58 (1333 g/mol) as a K-Ras(G12D)-inhibitory bicyclic peptide and suggested its cell membrane permeability. However, the membrane permeability mechanism had not been elucidated. In this study, we aim to clarify the mechanism by molecular dynamics (MD) simulations. Initially, we simulated the molecular conformations of KS-58 in water (a polar solvent) and in chloroform (a non-polar solvent). The identified stable conformations were significantly different in each solvent. KS-58 behaves as a chameleon-like molecule as it alters its polar surface area (PSA) depending on the solvent environment. It was also discovered that orientation of Asp's side chain is a critical energy barrier for KS-58 altering its conformation from hydrophilic to lipophilic. Taking these properties into consideration, we simulated its lipid bilayer membrane permeability. KS-58 shifted toward the inside of the lipid bilayer membrane with altering its conformations to lipophilic. When the simulation condition was set in deionized form of that carboxy group of Asp, KS-58 traveled deeper inside the cell membrane. PSA and the depth of the membrane penetration correlated. In vitro data suggested that cell membrane permeability of KS-58 is improved in weakly acidic conditions leading to partial deionization of the carboxy group. Our data provide an example of the molecular properties of mid-size peptides with membrane accessibility and propose an effective metadynamics approach to elucidate such molecular mechanisms by MD simulations.

Exploring FDA-Approved Frontiers: Insights into Natural and Engineered Peptide Analogues in the GLP-1, GIP, GHRH, CCK, ACTH, and α-MSH Realms

Peptides continue to gain significance in the pharmaceutical arena. Since the unveiling of insulin in 1921, the Food and Drug Administration (FDA) has authorised around 100 peptides for various applications. Peptides, although initially derived from endogenous sources, have evolved beyond their natural origins, exhibiting favourable therapeutic effectiveness. Medicinal chemistry has played a pivotal role in synthesising valuable natural peptide analogues, providing synthetic alternatives with therapeutic potential. Furthermore, key chemical modifications have enhanced the stability of peptides and strengthened their interactions with therapeutic targets. For instance, selective modifications have extended their half-life and lessened the frequency of their administration while maintaining the desired therapeutic action. In this review, I analyse the FDA approval of natural peptides, as well as engineered peptides for diabetes treatment, growth-hormone-releasing hormone (GHRH), cholecystokinin (CCK), adrenocorticotropic hormone (ACTH), and α-melanocyte stimulating hormone (α-MSH) peptide analogues. Attention will be paid to the structure, mode of action, developmental journey, FDA authorisation, and the adverse effects of these peptides.

Biocompatible strategies for peptide macrocyclisation

Peptides are increasingly important drug candidates, offering numerous advantages over conventional small molecules. However, they face significant challenges related to stability, cellular uptake and overall bioavailability. While individual modifications may not address all these challenges, macrocyclisation stands out as a single modification capable of enhancing affinity, selectivity, proteolytic stability and membrane permeability. The recent successes of in situ peptide modifications during screening in combination with genetically encoded peptide libraries have increased the demand for peptide macrocyclisation reactions that can occur under biocompatible conditions. In this perspective, we aim to distinguish biocompatible conditions from those well-known examples that are fully bioorthogonal. We introduce key strategies for biocompatible peptide macrocyclisation and contextualise them within contemporary screening methods, providing an overview of available transformations.

The obesity-autophagy-cancer axis: Mechanistic insights and therapeutic perspectives

Autophagy, a self-degradative process vital for cellular homeostasis, plays a significant role in adipose tissue metabolism and tumorigenesis. This review aims to elucidate the complex interplay between autophagy, obesity, and cancer development, with a specific emphasis on how obesity-driven changes affect the regulation of autophagy and subsequent implications for cancer risk. The burgeoning epidemic of obesity underscores the relevance of this research, particularly given the established links between obesity, autophagy, and various cancers. Our exploration delves into hormonal influence, notably INS (insulin) and LEP (leptin), on obesity and autophagy interactions. Further, we draw attention to the latest findings on molecular factors linking obesity to cancer, including hormonal changes, altered metabolism, and secretory autophagy. We posit that targeting autophagy modulation may offer a potent therapeutic approach for obesity-associated cancer, pointing to promising advancements in nanocarrier-based targeted therapies for autophagy modulation. However, we also recognize the challenges inherent to these approaches, particularly concerning their precision, control, and the dual roles autophagy can play in cancer. Future research directions include identifying novel biomarkers, refining targeted therapies, and harmonizing these approaches with precision medicine principles, thereby contributing to a more personalized, effective treatment paradigm for obesity-mediated cancer.

Promiscuity of Omphalotin A Biosynthetic Enzymes Allows de novo Production of Non-Natural Multiply Backbone N-Methylated Peptide Macrocycles in Yeast

Multiple backbone N-methylation and macrocyclization improve the proteolytic stability and oral availability of therapeutic peptides. Chemical synthesis of such peptides is challenging, in particular for the generation of peptide libraries for screening purposes. Enzymatic backbone N-methylation and macrocyclization occur as part of both non-ribosomal and ribosomal peptide biosynthesis, exemplified by the fungal natural products cyclosporin A and omphalotin A, respectively. Omphalotin A, a 9fold backbone N-methylated dodecamer isolated from the agaricomycete Omphalotus olearius, can be produced in Pichia pastoris by coexpression of the ophMA and ophP genes coding for the peptide precursor protein harbouring an autocatalytic peptide α-N-methyltransferase domain, and a peptide macrocyclase, respectively. Since both OphMA and OphP were previously shown to be relatively promiscuous in terms of peptide substrates, we expressed mutant versions of ophMA, encoding OphMA variants with altered core peptide sequences, along with wildtype ophP and assessed the production of the respective peptide macrocycles by the platform by high-performance liquid chromatography, coupled with tandem mass spectrometry (HPLC-MS/MS). Our results demonstrate the successful production of fifteen non-natural omphalotin-derived macrocycles, containing polar, aromatic and charged residues, and, thus, suggest that the system may be used as biotechnological platform to generate libraries of non-natural multiply backbone N-methylated peptide macrocycles.

Discovery and Development of Cyclic Peptide Proteasome Stimulators

The proteasome degrades proteins, which is essential for cellular homeostasis. Ubiquitin independent proteolysis degrades highly disordered and misfolded proteins. A decline of proteasomal activity has been associated with multiple neurodegenerative diseases due to the accumulation of misfolded proteins. In this work, cyclic peptide proteasome stimulators (CyPPSs) that enhance the clearance of misfolded proteins were discovered. In the initial screen of predicted natural products (pNPs), several cyclic peptides were found to stimulate the 20S core particle (20S CP). Development of a robust structural activity relationship led to the identification of potent, cell permeable CyPPSs. In vitro assays revealed that CyPPSs stimulate degradation of highly disordered and misfolded proteins without affecting ordered proteins. Furthermore, using a novel flow-based assay for proteasome activity, several CyPPSs were found to stimulate the 20S CP in cellulo. Overall, this work describes the development of CyPPSs as chemical tools capable of stimulating the proteasome and provides strong support for proteasome stimulation as a therapeutic strategy for neurodegenerative diseases.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Biocatalytic cyclization of small macrolactams by a penicillin-binding protein-type thioesterase

Macrocyclic peptides represent promising scaffolds for chemical tools and potential therapeutics. Synthetic methods for peptide macrocyclization are often hampered by C-terminal epimerization and oligomerization, leading to difficult scalability. While chemical strategies to circumvent this issue exist, they often require specific amino acids to be present in the peptide sequence. Herein, we report the characterization of Ulm16, a peptide cyclase belonging to the penicillin-binding protein-type class of thioesterases that catalyze head-to-tail macrolactamization of nonribosmal peptides. Ulm16 efficiently cyclizes various nonnative peptides ranging from 4 to 6 amino acids with catalytic efficiencies of up to 3 × 106 M-1 s-1. Unlike many previously described homologs, Ulm16 tolerates a variety of C- and N-terminal amino acids. The crystal structure of Ulm16, along with modeling of its substrates and site-directed mutagenesis, allows for rationalization of this wide substrate scope. Overall, Ulm16 represents a promising tool for the biocatalytic production of macrocyclic peptides.

Lipidation and PEGylation Strategies to Prolong the in Vivo Half-Life of a Nanomolar EphA4 Receptor Antagonist

The EphA4 receptor tyrosine kinase plays a role in neurodegenerative diseases, inhibition of nerve regeneration, cancer progression and other diseases. Therefore, EphA4 inhibition has potential therapeutic value. Selective EphA4 kinase inhibitors are not available, but we identified peptide antagonists that inhibit ephrin ligand binding to EphA4 with high specificity. One of these peptides is the cyclic APY-d3 (βAPYCVYRβASWSC-NH2), which inhibits ephrin-A5 ligand binding to EphA4 with low nanomolar binding affinity and is highly protease resistant. Here we describe modifications of APY-d3 that yield two different key derivatives with greatly increased half-lives in the mouse circulation, the lipidated APY-d3-laur8 and the PEGylated APY-d3-PEG4. These two derivatives inhibit ligand induced EphA4 activation in cells with sub-micromolar potency. Since they retain high potency and specificity for EphA4, lipidated and PEGylated APY-d3 derivatives represent new tools for discriminating EphA4 activities in vivo and for preclinical testing of EphA4 inhibition in animal disease models.

Enniatin A Analogues as Novel Hsp90 Inhibitors that Modulate Triple-Negative Breast Cancer

The 90 kilo-Dalton heat shock protein (Hsp90) is a molecular chaperone that facilitates the maturation of nascent polypeptides into their biologically active conformation. Because many of the >400 known client protein substrates are implicated in the development/progression of cancer, it is hypothesized that Hsp90 inhibition will simultaneously shut down numerous oncogenic pathways. Unfortunately, most of the small molecule Hsp90 inhibitors that have undergone clinical evaluation thus far have failed due to various toxicities. Therefore, the disruption of Hsp90 protein-protein interactions with cochaperones and/or client substrates has been proposed as an alternative way to achieve Hsp90 inhibition without such adverse events. The hexadepsipeptide Enniatin A (EnnA) has recently been reported to be one such inhibitor that also manifests immunogenic activity. Herein, we report preliminary structure-activity relationship (SAR) studies to determine the structural features that confer this unprecedented activity for an Hsp90 inhibitor. Our studies find that EnnA's branching moieties are necessary for its activity, but some structural modifications are tolerated.

Stabilized cyclic peptides as modulators of protein-protein interactions: promising strategies and biological evaluation

Protein-protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs "undruggable". Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novo development of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically "intractable" PPI systems.

Backbone N-methylation of peptides: Advances in synthesis and applications in pharmaceutical drug development

Peptide-based drugs have garnered considerable attention in recent years owing to their increasingly crucial role in the treatment of diverse diseases. However, the limited pharmacokinetic properties of peptides have hindered their full potential. One prominent strategy for enhancing the druggability of peptides is N-methylation, which involves the addition of a methyl group to the nitrogen atom of the peptide backbone. This modification significantly improves the stability, bioavailability, receptor binding affinity and selectivity of peptide drug candidates. In this review, we provide a comprehensive overview of the advancements in synthetic methods for N-methylated peptide synthesis, as well as the associated limitations. Moreover, we explore the versatile effects of N-methylation on various aspects of peptide properties. Furthermore, we emphasize the efforts dedicated to N-methylated peptide pharmaceuticals that have successfully obtained marketing approval.

In silico study identifies peptide inhibitors that negate the effect of non-synonymous mutations in major drug targets of SARS-CoV-2 variants

Since its advent in December 2019, SARS-CoV-2 has diverged into multiple variants with differing levels of virulence owing to the accumulation of mutations in its genome. The structural changes induced by non-synonymous mutations in major drug targets of the virus are known to alter the binding of potential antagonistic inhibitors. Here, we analyzed the effects of non-synonymous mutations in major targets of SARS-CoV-2 in response to potential peptide inhibitors. We screened 12 peptides reported to have anti-viral properties against RBD and 5 peptides against Mpro of SARS-CoV-2 variants using molecular docking and simulation approaches. The mutational landscape of RBD among SARS-CoV-2 variants had 21 non-synonymous mutations across 18 distinct sites. Among these, 14 mutations were present in the RBM region directly interacting with the hACE2 receptor. However, Only 3 non-synonymous mutations were observed in Mpro. We found that LCB1 - a de novo-synthesized peptide has the highest binding affinity to RBD despite non-synonymous mutations in variants and engages key residues of RBD-hACE2 interaction such as K417, E484, N487, and N501. Similarly, an antimicrobial peptide; 2JOS, was identified against Mpro with high binding affinity as it interacts with key residues in dimerization sites such as E166 and F140 crucial for viral replication. MD simulations affirm the stability of RBD-LCB1 and Mpro-2JOS complexes with an average RMSD of 1.902 and 2.476 respectively. We ascertain that LCB1 and 2JOS peptides are promising inhibitors to combat emerging variants of SARS-CoV-2 and thus warrant further investigations using in-vitro and in-vivo analysis.Communicated by Ramaswamy H. Sarma.

Cell-penetrating peptides predictors: A comparative analysis of methods and datasets

Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.