Hypercholesterolemia, characterized by elevated low-density lipoprotein (LDL) cholesterol levels, is a significant risk factor for cardiovascular disease. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in cholesterol metabolism by regulating LDL receptor degradation, making it a therapeutic target for mitigating hypercholesterolemia-associated risks. In this context, we aimed to engineer human H ferritin as a scaffold to present 24 copies of a PCSK9-targeting domain. The rationale behind this protein nanoparticle design was to disrupt the PCSK9-LDL receptor interaction, thereby attenuating the PCSK9-mediated impairment of LDL cholesterol clearance. The N-terminal sequence of human H ferritin was engineered to incorporate a 13-amino acid linear peptide (Pep2-8), which was previously identified as the smallest PCSK9 inhibitor. Exploiting the quaternary structure of ferritin, engineered nanoparticles were designed to display 24 copies of the targeting peptide on their surface, enabling a multivalent binding effect. Extensive biochemical characterization confirmed precise control over nanoparticle size and morphology, alongside robust PCSK9-binding affinity (KD in the high picomolar range). Subsequent efficacy assessments employing the HepG2 liver cell line demonstrated the ability of engineered ferritin\'s ability to disrupt PCSK9-LDL receptor interaction, thereby promoting LDL receptor recycling on cell surfaces and consequently enhancing LDL uptake. Our findings highlight the potential of ferritin-based platforms as versatile tools for targeting PCSK9 in the management of hypercholesterolemia. This study not only contributes to the advancement of ferritin-based therapeutics but also offers valuable insights into novel strategies for treating cardiovascular diseases.

For centuries, humans have used mushrooms as both food and pro-health supplements. Mushrooms, especially those related to the functions of the human immune system, are rich in dietary fiber, minerals, essential amino acids, and various bioactive compounds and have significant health-promoting properties. Immunoregulatory compounds in mushrooms include lectins, terpenes, terpenoids, polysaccharides, and fungal immunomodulatory proteins (FIPs). The distribution of these compounds varies from one species of mushroom to another, and their immunomodulatory activities depend on the core structures and chemical modifications in the composition of the fractions. In this review, we describe active compounds from medical mushrooms. We summarize potential mechanisms for their in vitro and in vivo activities and detail approaches used in developing and applying bioactive compounds from mushrooms. Finally, we discuss applications of fungal peptides and highlight areas that require improvement before the widespread use of those compounds as therapeutic agents and explore the status of clinical studies on the immunomodulatory activities of mushrooms and their products, as well as the prospect of clinical application of AMPs as \'drug-like\' compounds with great potential for treatment of non-healing chronic wounds and multiresistant infections.

The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

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.

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.

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based 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.

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.

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.