Selenium Drives a Transcriptional Adaptive Program to Block Ferroptosis and Treat Stroke.

Ferroptosis, a non-apoptotic form of programmed cell death, is triggered by oxidative stress in cancer, heat stress in plants, and hemorrhagic stroke. A homeostatic transcriptional response to ferroptotic stimuli is unknown. We show that neurons respond to ferroptotic stimuli by induction of selenoproteins, including antioxidant glutathione peroxidase 4 (GPX4). Pharmacological selenium (Se) augments GPX4 and other genes in this transcriptional program, the selenome, via coordinated activation of the transcription factors TFAP2c and Sp1 to protect neurons. Remarkably, a single dose of Se delivered into the brain drives antioxidant GPX4 expression, protects neurons, and improves behavior in a hemorrhagic stroke model. Altogether, we show that pharmacological Se supplementation effectively inhibits GPX4-dependent ferroptotic death as well as cell death induced by excitotoxicity or ER stress, which are GPX4 independent. Systemic administration of a brain-penetrant selenopeptide activates homeostatic transcription to inhibit cell death and improves function when delivered after hemorrhagic or ischemic stroke.

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP.

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi-Sugeno-Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.

Capillary electrophoresis in the analysis of therapeutic peptides-A review.

Therapeutic peptides are a growing class of innovative drugs with high efficiency and a low risk of adverse effects. These biomolecules fall within the molecular mass range between that of small molecules and proteins. However, their inherent instability and potential for degradation underscore the importance of reliable and effective analytical methods for pharmaceutical quality control, therapeutic drug monitoring, and compliance testing. Liquid chromatography-mass spectrometry (LC-MS) has long time been the "gold standard" conventional method for peptide analysis, but capillary electrophoresis (CE) is increasingly being recognized as a complementary and, in some cases, superior, highly efficient, green, and cost-effective alternative technique. CE can separate peptides composed of different amino acids owing to differences in their net charge and size, determining their migration behavior in an electric field. This review provides a comprehensive overview of therapeutic peptides that have been used in the clinical environment for the last 25 years. It describes the properties, classification, current trends in development, and clinical use of therapeutic peptides. From the analytical point of view, it discusses the challenges associated with the analysis of therapeutic peptides in pharmaceutical and biological matrices, as well as the evaluation of CE as a whole and the comparison with LC methods. The article also highlights the use of microchip electrophoresis, nonaqueous CE, and nonconventional hydrodynamically closed CE systems and their applications. Overall, the article emphasizes the importance of developing new CE-based analytical methods to ensure the high quality, safety, and efficacy of therapeutic peptides in clinical practice.

A Model Study to Assess Fibrillation and Product Stability to Support Peptide Drug Design.

The fibrillation of therapeutic peptides can present significant quality concerns and poses challenges for manufacturing and storage. A fundamental understanding of the mechanisms of fibrillation is critical for the rational design of fibrillation-resistant peptide drugs and can accelerate product development by guiding the selection of solution-stable candidates and formulations. The studies reported here investigated the effects of structural modifications on the fibrillation of a 29-residue peptide (PepA) and two sequence modified variants (PepB, PepC). The C-terminus of PepA was amidated, whereas both PepB and PepC retained the carboxylate, and Ser16 in PepA and PepB was substituted with a helix-stabilizing residue, α-aminoisobutyric acid (Aib), in PepC. In thermal denaturation studies by far-UV CD spectroscopy and fibrillation kinetic studies by fluorescence and turbidity measurements, PepA and PepB showed heat-induced conformational changes and were found to form fibrils, whereas PepC did not fibrillate and showed only minor changes in the CD signal. Pulsed hydrogen-deuterium exchange mass spectrometry (HDX-MS) showed a high degree of protection from HD exchange in mature PepA fibrils and its proteolytic fragments, indicating that most of the sequence had been incorporated into the fibril structure and occurred nearly simultaneously throughout the sequence. The effects of the net peptide charge and formulation pH on fibrillation kinetics were investigated. In real-time stability studies of two formulations of PepA at pH's 7.4 and 8.0, analytical methods detected significant changes in the stability of the formulations at different time points during the study, which were not observed during accelerated studies. Additionally, PepA samples were withdrawn from real-time stability and subjected to additional stress (40 °C, continuous shaking) to induce fibrillation; an approach that successfully amplified oligomers or prefibrillar species previously undetected in a thioflavin T assay. Taken together, these studies present an approach to differentiate and characterize fibrillation risk in structurally related peptides under accelerated and real-time conditions, providing a model for rapid, iterative structural design to optimize the stability of therapeutic peptides.

Therapeutic peptides in the treatment of digestive inflammation: Current advances and future prospects.

Digestive inflammation is a widespread global issue that significantly impacts quality of life. Recent advances have highlighted the unique potential of therapeutic peptides for treating this condition, owing to their specific bioactivity and high specificity. By specifically targeting key proteins involved in the pathological process and modulating biomolecular functions, therapeutic peptides offer a novel and promising approach to managing digestive inflammation. This review explores the development history, pharmacological characteristics, clinical applications, and regulatory mechanisms of therapeutic peptides in treating digestive inflammation. Additionally, the review addresses pharmacokinetics and quality control methods of therapeutic peptides, focusing on challenges such as low bioavailability, poor stability, and difficulties in delivery. The role of modern biotechnologies and nanotechnologies in overcoming these challenges is also examined. Finally, future directions for therapeutic peptides and their potential impact on clinical applications are discussed, with emphasis placed on their significant role in advancing medical and therapeutic practices.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics.

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

PreTP-EL: prediction of therapeutic peptides based on ensemble learning.

Therapeutic peptides are important for understanding the correlation between peptides and their therapeutic diagnostic potential. The therapeutic peptides can be further divided into different types based on therapeutic function sharing different characteristics. Although some computational approaches have been proposed to predict different types of therapeutic peptides, they failed to accurately predict all types of therapeutic peptides. In this study, a predictor called PreTP-EL has been proposed via employing the ensemble learning approach to fuse the different features and machine learning techniques in order to capture the different characteristics of various therapeutic peptides. Experimental results showed that PreTP-EL outperformed other competing methods. Availability and implementation: A user-friendly web-server of PreTP-EL predictor is available at http://bliulab.net/PreTP-EL.

ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation.

The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods.

Pinpointing the interaction site between semaphorin-3A and its inhibitory peptide.

Semaphorin-3A (Sema-3A) is a chemorepellant protein with various biological functions, including kidney development. It interacts with a protein complex consisting of the receptors neuropilin-1 (NRP-1) and plexin-A1. After acute kidney injury, Sema-3A is overexpressed and secreted, leading to a loss of kidney function. The development of peptide inhibitors is a promising approach to modulate the interaction of Sema-3A with its receptor NRP-1. Few interaction points between these binding partners are known. However, an immunoglobulin-like domain-derived peptide of Sema-3A has shown a positive effect on cell proliferation. To specify these interactions between the peptide inhibitor and the Sema-3A-NRP-1 system, the peptides were modified with the photoactivatable amino acids 4-benzoyl-l-phenylalanine or photo-l-leucine by solid-phase peptide synthesis. Activity was tested by an enzyme-linked immunosorbent-based binding assay, and crosslinking experiments were analyzed by Western blot and mass spectrometry, demonstrating a specific binding site of the peptide at Sema-3A. The observed signals for Sema-3A-peptide interaction were found in a defined area of the Sema domain, which was also demonstrated to be involved in NRP-1 binding. The presented data identified the interaction site for further development of therapeutic peptides to treat acute kidney injury by blocking the Sema-3A-NRP-1 interaction.

Therapeutic Peptides for Treatment of Lung Diseases: Infection, Fibrosis, and Cancer.

Various lung diseases endanger people's health. Side effects and pharmaceutical resistance complicate the treatment of acute lung injury, pulmonary fibrosis, and lung cancer, necessitating the development of novel treatments. Antimicrobial peptides (AMPs) are considered to serve as a viable alternative to conventional antibiotics. These peptides exhibit a broad antibacterial activity spectrum as well as immunomodulatory properties. Previous studies have shown that therapeutic peptides including AMPs had remarkable impacts on animal and cell models of acute lung injury, pulmonary fibrosis, and lung cancer. The purpose of this paper is to outline the potential curative effects and mechanisms of peptides in the three types of lung diseases mentioned above, which may be used as a therapeutic strategy in the future.

Eptifibatide, an Older Therapeutic Peptide with New Indications: From Clinical Pharmacology to Everyday Clinical Practice.

Therapeutic peptides are oligomers or short polymers of amino acids used for various medical purposes. Peptide-based treatments have evolved considerably due to new technologies, stimulating new research interests. They have been shown to be beneficial in a variety of therapeutic applications, notably in the treatment of cardiovascular disorders such as acute coronary syndrome (ACS). ACS is characterized by coronary artery wall damage and consequent formation of an intraluminal thrombus obstructing one or more coronary arteries, leading to unstable angina, non-ST elevated myocardial infarction, and ST-elevated myocardial infarction. One of the promising peptide drugs in the treatment of these pathologies is eptifibatide, a synthetic heptapeptide derived from rattlesnake venom. Eptifibatide is a glycoprotein IIb/IIIa inhibitor that blocks different pathways in platelet activation and aggregation. In this narrative review, we summarized the current evidence on the mechanism of action, clinical pharmacology, and applications of eptifibatide in cardiology. Additionally, we illustrated its possible broader usage with new indications, including ischemic stroke, carotid stenting, intracranial aneurysm stenting, and septic shock. Further research is, however, required to fully evaluate the role of eptifibatide in these pathologies, independently and in comparison to other medications.

A Spectroscopic and Molecular Dynamics Study on the Aggregation Properties of a Lipopeptide Analogue of Liraglutide, a Therapeutic Peptide against Diabetes Type 2.

The pharmacokinetics of peptide drugs are strongly affected by their aggregation properties and the morphology of the nanostructures they form in their native state as well as in their therapeutic formulation. In this contribution, we analyze the aggregation properties of a Liraglutide analogue (LG18), a leading drug against diabetes type 2. LG18 is a lipopeptide characterized by the functionalization of a lysine residue (K26) with an 18C lipid chain. To this end, spectroscopic experiments, dynamic light scattering measurements, and molecular dynamics simulations were carried out, following the evolution of the aggregation process from the small LG18 clusters formed at sub-micromolar concentrations to the mesoscopic aggregates formed by aged micromolar solutions. The critical aggregation concentration of LG18 in water (pH = 8) was found to amount to 4.3 µM, as assessed by the pyrene fluorescence assay. MD simulations showed that the LG18 nanostructures are formed by tetramer building blocks that, at longer times, self-assemble to form micrometric supramolecular architectures.

Stimuli Induced Uptake of Protein-Like Peptide Brush Polymers.

Recently, we presented a strategy for packaging peptides as side-chains in high-density brush polymers. For this globular protein-like polymer (PLP) formulation, therapeutic peptides were shown to resist proteolytic degradation, enter cells efficiently and maintain biological function. In this paper, we establish the role charge plays in dictating the cellular uptake of these peptide formulations, finding that peptides with a net positive charge will enter cells when polymerized, while those formed from anionic or neutral peptides remain outside of cells. Given these findings, we explored whether cellular uptake could be selectively induced by a stimulus. In our design, a cationic peptide is appended to a sequence of charge-neutralizing anionic amino acids through stimuli-responsive cleavable linkers. As a proof-of-concept study, we tested this strategy with two different classes of stimuli, exogenous UV light and an enzyme (a matrix metalloproteinase) associated with the inflammatory response. The key finding is that these materials enter cells only when acted upon by the stimulus. This approach makes it possible to achieve delivery of the polymers, therapeutic peptides or an appended cargo into cells in response to an appropriate stimulus.

Aggregation Behavior of Structurally Similar Therapeutic Peptides Investigated by 1H NMR and All-Atom Molecular Dynamics Simulations.

Understanding of peptide aggregation propensity is an important aspect in pharmaceutical development of peptide drugs. In this work, methodologies based on all-atom molecular dynamics (AA-MD) simulations and 1H NMR (in neat H2O) were evaluated as tools for identification and investigation of peptide aggregation. A series of structurally similar, pharmaceutically relevant peptides with known differences in aggregation behavior (D-Phe6-GnRH, ozarelix, cetrorelix, and degarelix) were investigated. The 1H NMR methodology was used to systematically investigate variations in aggregation with peptide concentration and time. Results show that 1H NMR can be used to detect the presence of coexisting classes of aggregates and the inclusion or exclusion of counterions in peptide aggregates. Interestingly, results suggest that the acetate counterions are included in aggregates of ozarelix and cetrorelix but not in aggregates of degarelix. The peptides investigated in AA-MD simulations (D-Phe6-GnRH, ozarelix, and cetrorelix) showed the same rank order of aggregation propensity as in the NMR experiments. The AA-MD simulations also provided molecular-level insights into aggregation dynamics, aggregation pathways, and the influence of different structural elements on peptide aggregation propensity and intermolecular interactions within the aggregates. Taken together, the findings from this study illustrate that 1H NMR and AA-MD simulations can be useful, complementary tools in early evaluation of aggregation propensity and formulation development for peptide drugs.

Lunasin as a Promising Plant-Derived Peptide for Cancer Therapy.

Cancer has become one of the main public health problems worldwide, demanding the development of new therapeutic agents that can help reduce mortality. Lunasin is a soybean peptide that has emerged as an attractive option because its preventive and therapeutic actions against cancer. In this review, we evaluated available research on lunasin's structure and mechanism of action, which should be useful for the development of lunasin-based therapeutic products. We described data on its primary, secondary, tertiary, and possible quaternary structure, susceptibility to post-translational modifications, and structural stability. These characteristics are important for understanding drug activity and characterizing lunasin products. We also provided an overview of research on lunasin pharmacokinetics and safety. Studies examining lunasin's mechanisms of action against cancer were reviewed, highlighting reported activities, and known molecular partners. Finally, we briefly discussed commercially available lunasin products and potential combination therapeutics.

De Novo Development of Mitochondria-Targeted Molecular Probes Targeting Pink1.

Mitochondria play central roles in maintaining cellular metabolic homeostasis, cell survival and cell death, and generate most of the cell's energy. Mitochondria maintain their homeostasis by dynamic (fission and fusion) and quality control mechanisms, including mitophagy, the removal of damaged mitochondria that is mediated mainly by the Pink1/Parkin pathway. Pink1 is a serine/threonine kinase which regulates mitochondrial function, hitherto many molecular mechanisms underlying Pink1 activity in mitochondrial homeostasis and cell fate remain unknown. Peptides are vital biological mediators that demonstrate remarkable potency, selectivity, and low toxicity, yet they have two major limitations, low oral bioavailability and poor stability. Herein, we rationally designed a linear peptide that targets Pink1 and, using straightforward chemistry, we developed molecular probes with drug-like properties to further characterize Pink1. Initially, we conjugated a cell-penetrating peptide and a cross-linker to map Pink1's 3D structure and its interaction sites. Next, we conjugated a fluorescent dye for cell-imaging. Finally, we developed cyclic peptides with improved stability and binding affinity. Overall, we present a facile approach to converting a non-permeable linear peptide into a research tool possessing important properties for therapeutics. This is a general approach using straightforward chemistry that can be tailored for various applications by numerous laboratories.

Application of Af4-Multidetection to Liraglutide in Its Formulation: Preserving and Representing Native Aggregation.

Aggregation is among the most critical parameters affecting the pharmacological and safety profile of peptide Active Pharmaceutical Ingredients (APIs). For this reason, it is of utmost importance to define the exact aggregation state of peptide drugs, particularly when the API is marketed as a ready-to-use solution. Consequently, appropriate non-destructive techniques able to replicate the peptide environment must be employed. In our work, we exploited Asymmetrical Flow Field-Flow Fractionation (AF4), connected to UV, dRI, fluorescence, and MALS detectors, to fully characterize the aggregation state of Liraglutide, a peptide API used for the treatment of diabetes type 2 and chronic obesity. In previous studies, Liraglutide was hypothesized to assemble into hexa-octamers in phosphate buffer, but no information on its behavior in the formulation medium was provided up to now. The method used allowed researchers to work using formulation as the mobile phase with excellent recoveries and LoQ/LoD, discerning between stable and degraded samples, and detecting, when present, aggregates up to 108 Da. The native state of Liraglutide was assessed and found to be an association into pentamers, with a non-spherical conformation. Combined to benchmark analyses, the sameness study was complete and descriptive, also giving insight on the aggregation process and covalent/non-covalent aggregate types.