Structural modification of the tripeptide KPV by reductive "glycoalkylation" of the lysine residue

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.

Antiplasmodial peptaibols act through membrane directed mechanisms

Our previous study identified 52 antiplasmodial peptaibols isolated from fungi. To understand their antiplasmodial mechanism of action, we conducted phenotypic assays, assessed the in vitro evolution of resistance, and performed a transcriptome analysis of the most potent peptaibol, HZ NPDG-I. HZ NPDG-I and 2 additional peptaibols were compared for their killing action and stage dependency, each showing a loss of digestive vacuole (DV) content via ultrastructural analysis. HZ NPDG-I demonstrated a stepwise increase in DV pH, impaired DV membrane permeability, and the ability to form ion channels upon reconstitution in planar membranes. This compound showed no signs of cross resistance to targets of current clinical candidates, and 3 independent lines evolved to resist HZ NPDG-I acquired nonsynonymous changes in the P. falciparum multidrug resistance transporter, pfmdr1. Conditional knockdown of PfMDR1 showed varying effects to other peptaibol analogs, suggesting differing sensitivity.

Self-assembled short peptides: Recent advances and strategies for potential pharmaceutical applications

Self-assembled short peptides have intrigued scientists due to the convenience of synthesis, good biocompatibility, low toxicity, inherent biodegradability and fast response to change in the physiological environment. Therefore, it is necessary to present a comprehensive summary of the recent advances in the last decade regarding the construction, route of administration and application of self-assembled short peptides based on the knowledge on their unique and specific ability of self-assembly. Herein, we firstly explored the molecular mechanisms of self-assembly of short peptides, such as non-modified amino acids, as well as Fmoc-modified, N-functionalized, and C-functionalized peptides. Next, cell penetration, fusion, and peptide targeting in peptide-based drug delivery were characterized. Then, the common administration routes and the potential pharmaceutical applications (drug delivery, antibacterial activity, stabilizers, imaging agents, and applications in bioengineering) of peptide drugs were respectively summarized. Last but not least, some general conclusions and future perspectives in the relevant fields were briefly listed. Although with certain challenges, great opportunities are offered by self-assembled short peptides to the fascinating area of drug development.

A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure

In the present study, a systematic effort has been made to predict the hemolytic potency of chemically modified peptides. All models have been trained, tested, and evaluated on a dataset that contains 583 modified hemolytic peptides and a balanced number of non-hemolytic peptides. Machine learning techniques have been used to build the classification models using an immense range of peptide features that include 2D, 3D descriptors, fingerprints, atom, and diatom compositions. Random Forest based model developed using fingerprints as an input feature achieved maximum accuracy of 78.33% with AUC of 0.86 on the main dataset and accuracy of 78.29% with AUC of 0.85 on the validation dataset. Models developed in this study have been incorporated in a web server “HemoPImod” to facilitate the scientific community (http://webs.iiitd.edu.in/raghava/hemopimod/).