Recent advances in the development of therapeutic peptides

Contrastive learning for enhancing feature extraction in anticancer peptides

Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.

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.

Modeling Peptide-Protein Interactions by a Logo-Based Method: Application in Peptide-HLA Binding Predictions

Peptide-protein interactions form a cornerstone in molecular biology, governing cellular signaling, structure, and enzymatic activities in living organisms. Improving computational models and experimental techniques to describe and predict these interactions remains an ongoing area of research. Here, we present a computational method for peptide-protein interactions' description and prediction based on leveraged amino acid frequencies within specific binding cores. Utilizing normalized frequencies, we construct quantitative matrices (QMs), termed 'logo models' derived from sequence logos. The method was developed to predict peptide binding to HLA-DQ2.5 and HLA-DQ8.1 proteins associated with susceptibility to celiac disease. The models were validated by more than 17,000 peptides demonstrating their efficacy in discriminating between binding and non-binding peptides. The logo method could be applied to diverse peptide-protein interactions, offering a versatile tool for predictive analysis in molecular binding studies.

Post-imprinting modification of molecularly imprinted polymer for proteins detection: A review

Inspired by protein post-translational modification (PTM), post-imprinting modification (PIM) has been proposed and developed to prepare novel molecularly imprinted polymers (MIPs), which are similar to functionalized biosynthetic proteins. The PIM involves site-directed modifications in the imprinted cavity of the MIP, such as introducing high-affinity binding sites and introducing fluorescent signal molecules. This modification makes the MIP further functionalized and improves the shortcomings of general molecular imprinting, such as single function, low selectivity, low sensitivity, and inability to fully restore the complex function of natural antibodies. This paper describes the characteristics of PIM strategies, reviews the latest research progress in the recognition and detection of protein biomarkers such as lysozyme, prostate-specific antigen, alpha-fetoprotein, human serum albumin, and peptides, and further discusses the importance, main challenges, and development prospects of PIM. The PIM technology has the potential to develop a new generation of biomimetic recognition materials beyond natural antibodies. It can be used in bioanalysis and other multitudinous fields for its unique features in molecule recognition.

An Update on Protein Kinases as Therapeutic Targets-Part II: Peptides as Allosteric Protein Kinase C Modulators Targeting Protein-Protein Interactions

Human protein kinases are highly-sought-after drug targets, historically harnessed for treating cancer, cardiovascular disease, and an increasing number of autoimmune and inflammatory conditions. Most current treatments involve small molecule protein kinase inhibitors that interact orthosterically with the protein kinase ATP-binding pocket. As a result, these compounds are often poorly selective and highly toxic. Part I of this series reviews the role of PKC isoforms in various human diseases, featuring cancer and cardiovascular disease, as well as translational examples of PKC modulation applied to human health and disease. In the present Part II, we discuss alternative allosteric binding mechanisms for targeting PKC, as well as novel drug platforms, such as modified peptides. A major goal is to design protein kinase modulators with enhanced selectivity and improved pharmacological properties. To this end, we use molecular docking analysis to predict the mechanisms of action for inhibitor-kinase interactions that can facilitate the development of next-generation PKC modulators.

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.

Influence of Fatty Acid Modification on the Anticancer Activity of the Antimicrobial Peptide Figainin 1

Antimicrobial peptides derived from the skin secretions of amphibians have made important progress in tumor therapy due to their unique mechanism of destroying cell membranes. Figainin 1 (F1) is an 18-amino acid antimicrobial peptide from the skin secretions of Boana raniceps frogs. In a previous study, F1 was shown to inhibit cancer cell proliferation. F1 is composed entirely of natural amino acids; therefore, it is easily degraded by a variety of proteases, resulting in poor stability and a short half-life. In the present study, we used a fatty acid modification strategy to improve the stability of Figainin 1. Among the 8 peptides synthesized, A-10 showed the strongest antiproliferative activity against K562 cells and the other four tumor cell lines, and its stability against serum and proteinase K was improved compared with F1. We found that A-10 works through two mechanisms, cell membrane destruction and apoptosis, and can arrest the cell cycle in the G0/G1 phase. Moreover, A-10 exhibited self-assembly behavior. Overall, it is necessary to select a fatty acid with a suitable length for modification to improve the stability and antiproliferative activity of antimicrobial peptides. This study provides a good reference for the development of antimicrobial peptides as effective anticancer compounds.

Bioactive Peptides and Protein Hydrolysates as Lipoxygenase Inhibitors

Lipoxygenases are non-heme iron-containing enzymes that catalyze the oxidation of polyunsaturated fatty acids, resulting in the production of lipid hydroperoxides, which are precursors of inflammatory lipid mediators. These enzymes are widely distributed in humans, other eukaryotes, and cyanobacteria. Lipoxygenases hold promise as therapeutic targets for several human diseases, including cancer and inflammation-related disorders. Inhibitors of lipoxygenase have potential applications in pharmaceuticals, cosmetics, and food. Bioactive peptides are short amino acid sequences embedded within parent proteins, which can be released by enzymatic hydrolysis, microbial fermentation, and gastrointestinal digestion. A wide variety of bioactivities have been documented for protein hydrolysates and peptides derived from different biological sources. Recent findings indicate that protein hydrolysates and peptides derived from both edible and non-edible bioresources can act as lipoxygenase inhibitors. This review aims to provide an overview of the current knowledge regarding the production of anti-lipoxygenase protein hydrolysates and peptides from millet grains, chia seeds, insects, milk proteins, fish feed, velvet antler blood, fish scales, and feather keratins. The anti-lipoxygenase activities and modes of action of these protein hydrolysates and peptides are discussed. The strengths and shortcomings of previous research in this area are emphasized. Additionally, potential research directions and areas for improvement are suggested to accelerate the discovery of anti-lipoxygenase peptides in the near future.