Anticancer Mechanisms of Bioactive Peptides

Recent advances and applications of peptide-agent conjugates for targeting tumor cells

BACKGROUND

Cancer, being a complex disease, presents a major challenge for the scientific and medical communities. Peptide therapeutics have played a significant role in different medical practices, including cancer treatment.

METHOD

This review provides an overview of the current situation and potential development prospects of anticancer peptides (ACPs), with a particular focus on peptide vaccines and peptide-drug conjugates for cancer treatment.

RESULTS

ACPs can be used directly as cytotoxic agents (molecularly targeted peptides) or can act as carriers (guiding missile) of chemotherapeutic agents and radionuclides by specifically targeting cancer cells. More than 60 natural and synthetic cationic peptides are approved in the USA and other major markets for the treatment of cancer and other diseases. Compared to traditional cancer treatments, peptides exhibit anticancer activity with high specificity and the ability to rapidly kill target cancer cells. ACP's target and kill cancer cells via different mechanisms, including membrane disruption, pore formation, induction of apoptosis, necrosis, autophagy, and regulation of the immune system. Modified peptides have been developed as carriers for drugs, vaccines, and peptide-drug conjugates, which have been evaluated in various phases of clinical trials for the treatment of different types of solid and leukemia cancer.

CONCLUSIONS

This review highlights the potential of ACPs as a promising therapeutic option for cancer treatment, particularly through the use of peptide vaccines and peptide-drug conjugates. Despite the limitations of peptides, such as poor metabolic stability and low bioavailability, modified peptides show promise in addressing these challenges. Various mechanism of action of anticancer peptides. Modes of action against cancer cells including: inducing apoptosis by cytochrome c release, direct cell membrane lysis (necrosis), inhibiting angiogenesis, inducing autophagy-mediated cell death and immune cell regulation.

Aquatic Peptide: The Potential Anti-Cancer and Anti-Microbial Activity of GE18 Derived from Pathogenic Fungus Aphanomyces invadans

Small molecules as well as peptide-based therapeutic approaches have attracted global interest due to their lower or no toxicity in nature, and their potential in addressing several health complications including immune diseases, cardiovascular diseases, metabolic disorders, osteoporosis and cancer. This study proposed a peptide, GE18 of subtilisin-like peptidase from the virulence factor of aquatic pathogenic fungus Aphanomyces invadans, which elicits anti-cancer and anti-microbial activities. To understand the potential GE18 peptide-induced biological effects, an in silico analysis, in vitro (L6 cells) and in vivo toxicity assays (using zebrafish embryo), in vitro anti-cancer assays and anti-microbial assays were performed. The outcomes of the in silico analyses demonstrated that the GE18 peptide has potent anti-cancer and anti-microbial activities. GE18 is non-toxic to in vitro non-cancerous cells and in vivo zebrafish larvae. However, the peptide showed significant anti-cancer properties against MCF-7 cells with an IC50 value of 35.34 µM, at 24 h. Besides the anti-proliferative effect on cancer cells, the peptide exposure does promote the ROS concentration, mitochondrial membrane potential and the subsequent upregulation of anti-cancer genes. On the other hand, GE18 elicits significant anti-microbial activity against P. aeruginosa, wherein GE18 significantly inhibits bacterial biofilm formation. Since the peptide has positively charged amino acid residues, it targets the cell membrane, as is evident in the FESEM analysis. Based on these outcomes, it is possible that the GE18 peptide is a significant anti-cancer and anti-microbial molecule.

PS9, Derived from an Aquatic Fungus Virulent Protein, Glycosyl Hydrolase, Arrests MCF-7 Proliferation by Regulating Intracellular Reactive Oxygen Species and Apoptotic Pathways

One of the most common diseases in women is breast cancer, which has the highest death globally. Surgery, chemotherapy, hormone treatments, and radiation are the current treatment options for breast cancer. However, these options have several adverse side effects. Recently, peptide-based drugs have gained attention as anticancer therapy. Studies report that peptides from biological toxins such as venom and virulent pathogenic molecules have potential therapeutic effects against multiple diseases, including cancers. This study reports on the in vitro anticancer effect of a short peptide, PS9, derived from a virulent protein, glycosyl hydrolase, of an aquatic fungus, Aphanomyces invadans. This peptide arrests MCF-7 proliferation by regulating intercellular reactive oxygen species (ROS) and apoptotic pathways. Based on the potential for the anticancer effect of PS9, from the in silico analysis, in vitro analyses using MCF-7 cells were executed. PS9 showed a dose-dependent activity; its IC50 value was 25.27-43.28 μM at 24 h. The acridine orange/ethidium bromide (AO/EtBr) staining, to establish the status of apoptosis in MCF-7 cells, showed morphologies for early and late apoptosis and necrotic cell death. The 2,7-dichlorodihydrofluorescein diacetate (DCFDA) staining and biochemical analyses showed a significant increase in reactive oxygen species (ROS). Besides, PS9 has been shown to regulate the caspase-mediated apoptotic pathway. PS9 is nontoxic, in vitro, and in vivo zebrafish larvae. Together, PS9 may have an anticancer effect in vitro.

Evolving strategies and application of proteins and peptide therapeutics in cancer treatment

Several proteins and peptides have therapeutic potential and can be used for cancer therapy. By binding to cell surface receptors and other indicators uniquely linked with or overexpressed on tumors compared to healthy tissue, protein biologics enhance the active targeting of cancer cells, as opposed to the passive targeting of cells by conventional small-molecule chemotherapeutics. This study focuses on peptide medications that exist to slow or stop tumor growth and the spread of cancer, demonstrating the therapeutic potential of peptides in cancer treatment. As an alternative to standard chemotherapy, peptides that selectively kill cancer cells while sparing healthy tissue are developing. A mountain of clinical evidence supports the efficacy of peptide-based cancer vaccines. Since a single treatment technique may not be sufficient to produce favourable results in the fight against cancer, combination therapy is emerging as an effective option to generate synergistic benefits. One example of this new area is the use of anticancer peptides in combination with nonpeptidic cytotoxic drugs or the combination of immunotherapy with conventional therapies like radiation and chemotherapy. This review focuses on the different natural and synthetic peptides obtained and researched. Discoveries, manufacture, and modifications of peptide drugs, as well as their contemporary applications, are summarized in this review. We also discuss the benefits and difficulties of potential advances in therapeutic peptides.

Bioactive peptides from scorpion venoms: therapeutic scaffolds and pharmacological tools

Evolution and natural selection have endowed animal venoms, including scorpion venoms, with a wide range of pharmacological properties. Consequently, scorpions, their venoms, and/or their body parts have been used since time immemorial in traditional medicines, especially in Africa and Asia. With respect to their pharmacological potential, bioactive peptides from scorpion venoms have become an important source of scientific research. With the rapid increase in the characterization of various components from scorpion venoms, a large number of peptides are identified with an aim of combating a myriad of emerging global health problems. Moreover, some scorpion venom-derived peptides have been established as potential scaffolds helpful for drug development. In this review, we summarize the promising scorpion venoms-derived peptides as drug candidates. Accordingly, we highlight the data and knowledge needed for continuous characterization and development of additional natural peptides from scorpion venoms, as potential drugs that can treat related diseases.

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction

In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can facilitate its finding and speed up its application in treating cancer. However, many recent approaches are based on machine learning, which not only restricts the representation ability of the models but also requires a complex hand-crafted feature extraction process. Here, we propose AntiMF, a deep learning model that utilizes multi-view mechanism based on different feature extraction models. Comparative results show that our model has a better performance than the state-of-the-art methods in the prediction of anticancer peptides. By using an ensemble learning framework to extract representation, AntiMF can capture the different dimensional information, which can make model representation more complete. Moreover, we visualize what AntiMF learns on one of its ensemble models to intuitively show the effectivity of our model, indicating that AntiMF has the great potential ability to be an effective and useful model to identify anticancer peptides accurately.

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

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We present a novel meta-approach, MLACP 2.0, and implement it as a user-friendly webserver for the accurate identification of ACPs.

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MLACP 2.0 employed 11 different encoding schemes and eight different classifiers, including convolutional neural networks, to create a stable meta-model.

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Benchmarking study has demonstrated that MLACP 2.0 achieves superior performance in ACP prediction compared to publicly available state-of-the-art predictors.

Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.

Novel Peptide Therapeutic Approaches for Cancer Treatment

Peptides are increasingly being developed for use as therapeutics to treat many ailments, including cancer. Therapeutic peptides have the advantages of target specificity and low toxicity. The anticancer effects of a peptide can be the direct result of the peptide binding its intended target, or the peptide may be conjugated to a chemotherapy drug or radionuclide and used to target the agent to cancer cells. Peptides can be targeted to proteins on the cell surface, where the peptide–protein interaction can initiate internalization of the complex, or the peptide can be designed to directly cross the cell membrane. Peptides can induce cell death by numerous mechanisms including membrane disruption and subsequent necrosis, apoptosis, tumor angiogenesis inhibition, immune regulation, disruption of cell signaling pathways, cell cycle regulation, DNA repair pathways, or cell death pathways. Although using peptides as therapeutics has many advantages, peptides have the disadvantage of being easily degraded by proteases once administered and, depending on the mode of administration, often have difficulty being adsorbed into the blood stream. In this review, we discuss strategies recently developed to overcome these obstacles of peptide delivery and bioavailability. In addition, we present many examples of peptides developed to fight cancer.

Anticancer peptides prediction with deep representation learning features

Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, we proposed a computational method named identify anticancer peptides via deep representation learning features (iACP-DRLF) using light gradient boosting machine algorithm and deep representation learning features. Two kinds of sequence embedding technologies were used, namely soft symmetric alignment embedding and unified representation (UniRep) embedding, both of which involved deep neural network models based on long short-term memory networks and their derived networks. The results showed that the use of deep representation learning features greatly improved the capability of the models to discriminate anticancer peptides from other peptides. Also, UMAP (uniform manifold approximation and projection for dimension reduction) and SHAP (shapley additive explanations) analysis proved that UniRep have an advantage over other features for anticancer peptide identification. The python script and pretrained models could be downloaded from https://github.com/zhibinlv/iACP-DRLF or from http://public.aibiochem.net/iACP-DRLF/.