Anticancer peptides mechanisms, simple and complex

Plant-Derived as Alternatives to Animal-Derived Bioactive Peptides: A Review of the Preparation, Bioactivities, Structure-Activity Relationships, and Applications in Chronic Diseases

Background/Objectives: At present, a large number of bioactive peptides have been found from plant sources with potential applications for the prevention of chronic diseases. By promoting plant-derived bioactive peptides (PDBPs), we can reduce dependence on animals, reduce greenhouse gas emissions, and protect the ecological environment. Methods: In this review, we summarize recent advances in sustainably sourced PDBPs in terms of preparation methods, biological activity, structure-activity relationships, and their use in chronic diseases. Results: Firstly, the current preparation methods of PDBPs were summarized, and the advantages and disadvantages of enzymatic method and microbial fermentation method were introduced. Secondly, the biological activities of PDBPs that have been explored are summarized, including antioxidant, antibacterial, anticancer and antihypertensive activities. Finally, based on the biological activity, the structure-activity relationship of PDBPs and its application in chronic diseases were discussed. All these provide the foundation for the development of PDBPs. However, the study of PDBPs still has some limitations. Conclusions: Overall, PDBPs is a good candidate for the prevention and treatment of chronic diseases in humans. This work provides important information for exploring the source of PDBPs, optimizing its biological activity, and accurately designing functional foods or drugs.

Enhanced efficacy of combined VEGFR peptide-drug conjugate and anti-PD-1 antibody in treating hepatocellular carcinoma

This study aimed to design a VEGFR-targeting peptide-drug conjugate with the ability to decrease tumor burden and suppress tumor angiogenesis, and to further evaluate the therapeutic effect of anti-PD-1 antibody in HCC therapy. A VEGFR-targeting peptide VEGF125 - 136 (QR) was conjugated with a lytic peptide (KLU) to form a peptide-drug conjugate QR-KLU. And the efficacy of QR-KLU in combination with anti-PD-1 antibody for HCC therapy in vivo and in vitro were evaluated. QR-KLU inhibited the proliferation and migration of mouse HCC cell line (Hepa1-6) cells under normoxic and hypoxic conditions in a dose-dependent manner. In the subcutaneous Hepa1-6 tumor model, QR-KLU combined with the anti-PD-1 antibody substantially inhibited tumor growth, promoted tumor necrosis, and prolonged the survival time of tumor-bearing mice. QR-KLU substantially inhibited hypoxia-induced expression of VEGF, promoted tumor vascular normalization, and increased cluster of differentiation 8+ (CD8+) T cell infiltration in the tumor. In addition, QR-KLU and anti-PD-1 antibody demonstrated a strong synergistic effect in promoting the activation of intratumoral CD8+ T cells, reducing the expression of immune-inhibitory factors, and increasing the expression of immune-stimulatory factors. This study proposed a novel approach for enhancing the efficacy of anti-PD-1 antibody using a VEGFR-targeting peptide-drug conjugate in HCC therapy.

Gastrointestinal digestion of food proteins: Anticancer, antihypertensive, anti-obesity, and immunomodulatory mechanisms of the derived peptides

Food proteins and their peptides play a significant role in the important biological processes and physiological functions of the body. The peptides show diverse biological benefits ranging from anticancer to antihypertensive, anti-obesity, and immunomodulatory, among others. In this review, an overview of food protein digestion in the gastrointestinal tract and the mechanisms involved was presented. As some proteins remain resistant and undigested, the multifarious factors (e.g. protein type and structure, microbial composition, pH levels and redox potential, host factors, etc.) affecting their colonic fermentation, the derived peptides, and amino acids that evade intestinal digestion are thus considered. The section that follows focuses on the mechanisms of the peptides with anticancer, antihypertensive, anti-obesity, and immunomodulatory effects. As further considerations were made, it is concluded that clinical studies targeting a clear understanding of the gastrointestinal stability, bioavailability, and safety of food-based peptides are still warranted.

The bright side of chemistry: Exploring synthetic peptide-based anticancer vaccines

The present review focuses on synthetic peptide-based vaccine strategies in the context of anticancer intervention, paying attention to critical aspects such as peptide epitope selection, adjuvant integration, and nuanced classification of synthetic peptide cancer vaccines. Within this discussion, we delve into the diverse array of synthetic peptide-based anticancer vaccines, each derived from tumor-associated antigens (TAAs), including melanoma antigen recognized by T cells 1 (Melan-A or MART-1), mucin 1 (MUC1), human epidermal growth factor receptor 2 (HER-2), tumor protein 53 (p53), human telomerase reverse transcriptase (hTERT), survivin, folate receptor (FR), cancer-testis antigen 1 (NY-ESO-1), and prostate-specific antigen (PSA). We also describe the synthetic peptide-based vaccines developed for cancers triggered by oncovirus, such as human papillomavirus (HPV), and hepatitis C virus (HCV). Additionally, the potential synergy of peptide-based vaccines with common therapeutics in cancer was considered. The last part of our discussion deals with the realm of the peptide-based vaccines delivery, highlighting its role in translating the most promising candidates into effective clinical strategies. Although this discussion does not cover all the ongoing peptide vaccine investigations, it aims at offering valuable insights into the chemical modifications and the structural complexities of anticancer peptide-based vaccines.

ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network

Cancer is a severe illness that significantly threatens human life and health. Anticancer peptides (ACPs) represent a promising therapeutic strategy for combating cancer. In silico methods enable rapid and accurate identification of ACPs without extensive human and material resources. This study proposes a two-stage computational framework called ACP-CapsPred, which can accurately identify ACPs and characterize their functional activities across different cancer types. ACP-CapsPred integrates a protein language model with evolutionary information and physicochemical properties of peptides, constructing a comprehensive profile of peptides. ACP-CapsPred employs a next-generation neural network, specifically capsule networks, to construct predictive models. Experimental results demonstrate that ACP-CapsPred exhibits satisfactory predictive capabilities in both stages, reaching state-of-the-art performance. In the first stage, ACP-CapsPred achieves accuracies of 80.25% and 95.71%, as well as F1-scores of 79.86% and 95.90%, on benchmark datasets Set 1 and Set 2, respectively. In the second stage, tasked with characterizing the functional activities of ACPs across five selected cancer types, ACP-CapsPred attains an average accuracy of 90.75% and an F1-score of 91.38%. Furthermore, ACP-CapsPred demonstrates excellent interpretability, revealing regions and residues associated with anticancer activity. Consequently, ACP-CapsPred presents a promising solution to expedite the development of ACPs and offers a novel perspective for other biological sequence analyses.

Rational design of a new short anticancer peptide with good potential for cancer treatment

Anticancer peptides (ACPs) have regarded as a new generation of promising antitumor drugs due to the unique mode of action. The main challenge is to develop potential anticancer peptides with satisfied antitumor activity and low toxicity. Here, a series of new α-helical anticancer peptides were designed and synthesized based on the regular repeat motif KLLK. The optimal peptides 14E and 14Aad were successfully derived from the new short α-helical peptide KL-8. Our results demonstrated that 14E and 14Aad had good antitumor activity and low toxicity, exhibiting excellent selectivity index. This result highlighted that the desirable modification position and appropriate hydrophobic side-chain structure of acidic amino acids played critical roles in regulating the antitumor activity/toxicity of new peptides. Further studies indicated that they could induce tumor cell death via the multiple actions of efficient membrane disruption and intracellular mechanisms, displaying apparent superiority in combination with PTX. In addition, the new peptides 14E and 14Aad showed excellent antitumor efficacy in vivo and low toxicity in mice compared to KL-8 and PTX. Particularly, 14Aad with the longer side chain at the 14th site exhibited the best therapeutic performance. In conclusion, our work provided a new avenue to develop promising anticancer peptides with good selectivity for tumor therapy.

Androcin 18-1, a novel scorpion-venom peptide, shows a potent antitumor activity against human U87 cells via inducing mitochondrial dysfunction

Scorpion venom is a potent natural source for antitumor drug development due to the multiple action modes of anticancer components. Although the sequence of Androcin 18-1 has been identified from the transcriptome profile of the scorpion venom Androctonus bicolor, its bioactivity remains unclear. In this study, we described the antitumor mechanism whereby Androcin 18-1 inhibits the proliferation and induces apoptosis by inducing cell membrane disruption, ROS accumulation, and mitochondrial dysfunction in human U87 glioblastoma cells. Moreover, Androcin 18-1 could suppress cell migration via the mechanisms associated with cytoskeleton disorganization and MMPs/TIMPs expression regulation. The discovery of this work highlights the potential application of Androcin 18-1 in drug development for glioblastoma treatment.

Computational peptide discovery with a genetic programming approach

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.

Applications of peptide-based nanomaterials in targeting cancer therapy

To meet the demand for precision medicine, researchers are committed to developing novel strategies to reduce systemic toxicity and side effects in cancer treatment. Targeting peptides are widely applied due to their affinity and specificity, and their ability to be high-throughput screened, chemically synthesized and modified. More importantly, peptides can form ordered self-assembled structures through non-covalent supramolecular interactions, which can form nanostructures with different morphologies and functions, playing crucial roles in targeted diagnosis and treatment. Among them, in targeted immunotherapy, utilizing targeting peptides to block the binding between immune checkpoints and ligands, thereby activating the immune system to eliminate cancer cells, is an advanced therapeutic strategy. In this mini-review, we summarize the screening, self-assembly, and biomedical applications of targeting peptide-based nanomaterials. Furthermore, this mini-review summarizes the potential and optimization strategies of targeting peptides.

Physical-Chemical Approach to Designing Drugs with Multiple Targets

Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.

Computational design of anti-cancer peptides tailored to target specific tumor markers

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.

Antimicrobial peptides: An alternative to traditional antibiotics

As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.

Anticancer activities of natural antimicrobial peptides from animals

Cancer is the most common cause of human death worldwide, posing a serious threat to human health and having a negative impact on the economy. In the past few decades, significant progress has been made in anticancer therapies, but traditional anticancer therapies, including radiation therapy, surgery, chemotherapy, molecular targeted therapy, immunotherapy and antibody-drug conjugates (ADCs), have serious side effects, low specificity, and the emergence of drug resistance. Therefore, there is an urgent need to develop new treatment methods to improve efficacy and reduce side effects. Antimicrobial peptides (AMPs) exist in the innate immune system of various organisms. As the most promising alternatives to traditional drugs for treating cancers, some AMPs also have been proven to possess anticancer activities, which are defined as anticancer peptides (ACPs). These peptides have the advantages of being able to specifically target cancer cells and have less toxicity to normal tissues. More and more studies have found that marine and terrestrial animals contain a large amount of ACPs. In this article, we introduced the animal derived AMPs with anti-cancer activity, and summarized the types of tumor cells inhibited by ACPs, the mechanisms by which they exert anti-tumor effects and clinical applications of ACPs.

Fatty acid modification of casein bioactive peptides nano-assemblies, synthesis, characterization and anticarcinogenic effect

In this study, the nano-assemblies of bovine casein hydrolyzed peptides (HP) modified by fatty acids with various alkyl chain lengths (C8, C10, C12 and C14) were synthesized. The physicochemical properties of HP-C8-HP-C14 nano-assemblies were characterized using spectra, laser particle size analyzer, contact angle meter, scanning electron microscope (SEM) and cryo-transmission electron microscope (Cryo-TEM). HP-C8 and HP-C10 self-assembled into a hollow cube cage with an average size of ~500 nm, and the assembly of HP-C12 showed a flower-shaped morphology with more dispersed behavior, and droplet size was observed as ~20 nm. The in vitro cytotoxicity against human breast cancer cells MCF-7 was tested using CCK-8 assay and flow cytometry analysis. HP-C12 showed the highest cytotoxicity for MCF-7 cells with an inhibition rate of 66.03 % ± 0.35 % with an IC50 value of 7.4 μM among HP-Cn. HP-C8, HP-C10 and HP-C12 significantly affected on the migration, invasion and apoptosis of MCF-7 cells. The apoptosis mechanism may depend on the upregulation of anti-apoptotic protein Bcl-2 as well as pro-apoptotic proteins Bax and caspase-8. The dead MCF-7 cells were analyzed with UHPLC-MS/MS using untargeted metabolomics, revealing key metabolic pathways.

Natural Anticancer Peptides from Marine Animal Species: Evidence from In Vitro Cell Model Systems

Anticancer peptides are short and structurally heterogeneous aminoacidic chains, which display selective cytotoxicity mostly against tumor cells, but not healthy cells, based on their different cell surface properties. Their anti-tumoral activity is carried out through interference with intracellular homeostasis, such as plasmalemma integrity, cell cycle control, enzymatic activities and mitochondrial functions, ultimately acting as angiogenesis-, drug resistance- and metastasis-inhibiting agents, immune stimulators, differentiation inducers and necrosis or extrinsic/intrinsic apoptosis promoters. The marine environment features an ever-growing level of biodiversity, and seas and oceans are poorly exploited mines in terms of natural products of biomedical interest. Adaptation processes to extreme and competitive environmental conditions led marine species to produce unique metabolites as a chemical strategy to allow inter-individual signalization and ensure survival against predators, infectious agents or UV radiation. These natural metabolites have found broad use in various applications in healthcare management, due to their anticancer, anti-angiogenic, anti-inflammatory and regeneration abilities. The aim of this review is to pick selected studies that report on the isolation of marine animal-derived peptides and the identification of their anticancer activity in in vitro cultures of cancer cells, and list them with respect to the taxonomical hierarchy of the source organism.

Plant-based proteins: advanced extraction technologies, interactions, physicochemical and functional properties, food and related applications, and health benefits

Even though plant proteins are more plentiful and affordable than animal proteins in comparison, direct usage of plant-based proteins (PBPs) is still limited because PBPs are fed to animals as feed to produce animal-based proteins. Thus, this work has comprehensively reviewed the effects of various factors such as pH, temperature, pressure, and ionic strength on PBP properties, as well as describes the protein interactions, and extraction methods to know the optimal conditions for preparing PBP-based products with high functional properties and health benefits. According to the cited studies in the current work, the environmental factors, particularly pH and ionic strength significantly affected on physicochemical and functional properties of PBPs, especially solubility was 76.0% to 83.9% at pH = 2, while at pH = 5.0 reduced from 5.3% to 9.6%, emulsifying ability was the lowest at pH = 5.8 and the highest at pH 8.0, and foaming capacity was lowest at pH 5.0 and the highest at pH = 7.0. Electrostatic interactions are the main way for protein interactions, which can be used to create protein/polysaccharide complexes for food industrial purposes. The extraction yield of proteins can be reached up to 86-95% with high functional properties using sustainable and efficient routes, including enzymatic, ultrasound-, microwave-, pulsed electric field-, and high-pressure-assisted extraction. Nondairy alternative products, especially yogurt, 3D food printing and meat analogs, synthesis of nanoparticles, and bioplastics and packaging films are the best available PBPs-based products. Moreover, PBPs particularly those that contain pigments and their products showed good bioactivities, especially antioxidants, antidiabetic, and antimicrobial.

Evaluating the In Vitro Activity and Safety of Modified LfcinB Peptides as Potential Colon Anticancer Agents: Cell Line Studies and Insect-Based Toxicity Assessments

Anticancer peptides are increasingly being considered as alternative treatments for cancer due to their potency, selectivity, and low toxicity. Previously, the peptide LfcinB (21-25)Pal showed in vitro anticancer effects against the Caco-2 colon cancer cell line (half-maximal inhibitory concentration (IC50): 86 μM). In this study, we developed modifications to the peptide sequence to increase its anticancer activity. Sequence modifications were made such as the inclusion of amino hexanoic acid (Ahx), N-terminal biotinylation, acetylation, and substitutions of Orn for Arg and/or d-Arg by l-Arg. The molecules were synthesized using manual solid-phase peptide synthesis (SPPS), and their synthetic feasibility (SAScore) ranged from 6.2 to 7.6. The chromatographic purities of the synthesized peptides were greater than 89%. We found that Ahx-RWQWRWQWR and RWQWRWQW-Orn showed activity against both Caco-2 and HT-29 cell lines and decreased IC50 values by approx. 50% in Caco-2 cells (IC50: 40 μM) when compared to the parent peptide RWQWRWQWR. Moreover, the modified peptides demonstrated lower hemolytic effects, with values <10% at 200 μg/mL. Toxicity was assessed using the Galleria mellonella model and the half-maximal lethal dose (LD50) for the best peptides was >100 mg/kg, indicating that their toxicity is classified as moderately toxic or lower. In contrast, cisplatin showed an LD50 of 13 mg/Kg. The designed anticancer peptides presented good in vitro activity and low toxicity, making them promising molecules for future drug development studies.

Topoisomeric Membrane-Active Peptides: A Review of the Last Two Decades

In recent decades, bioactive peptides have been gaining recognition in various biomedical areas, such as intracellular drug delivery (cell-penetrating peptides, CPPs) or anti-infective action (antimicrobial peptides, AMPs), closely associated to their distinct mode of interaction with biological membranes. Exploiting the interaction of membrane-active peptides with diverse targets (healthy, tumoral, bacterial or parasitic cell membranes) is opening encouraging prospects for peptides in therapeutics. However, ordinary peptides formed by L-amino acids are easily decomposed by proteases in biological fluids. One way to sidestep this limitation is to use topoisomers, namely versions of the peptide made up of D-amino acids in either canonic (enantio) or inverted (retroenantio) sequence. Rearranging peptide sequences in this fashion provides a certain degree of native structure mimicry that, in appropriate contexts, may deliver desirable biological activity while avoiding protease degradation. In this review, we will focus on recent accounts of membrane-active topoisomeric peptides with therapeutic applications as CPP drug delivery vectors, or as antimicrobial and anticancer candidates. We will also discuss the most common modes of interaction of these peptides with their membrane targets.

An Augmented Sample Selection Framework for Prediction of Anticancer Peptides

Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method.

A Nucleus-Targeting WT1 Antagonistic Peptide Encapsulated in Polymeric Nanomicelles Combats Refractory Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is recognized as a classic clonal myeloproliferative disorder. Given the limited treatment options for CML patients in the accelerated phase (AP) and blast phase (BP), there is an evident need to develop new therapeutic strategies. This has the potential to improve outcomes for individuals in the advanced stages of CML. A promising therapeutic target is Wilms' tumor 1 (WT1), which is highly expressed in BP-CML cells and plays a crucial role in CML progression. In this study, a chemically synthesized nucleus-targeting WT1 antagonistic peptide termed WIP2W was identified. The therapeutic implications of both the peptide and its micellar formulation, M-WIP2W, were evaluated in WT1+ BP-CML cell lines and in mice. The findings indicate that WIP2W can bind specifically to the WT1 protein, inducing cell cycle arrest and notable cytotoxicity in WT1+ BP-CML cells. Moreover, subcutaneous injections of M-WIP2W were observed to significantly enhance intra-tumoral accumulation and to effectively inhibit tumor growth. Thus, WIP2W stands out as a potent and selective WT1 inhibitor, and the M-WIP2W nanoformulation appears promising for the therapeutic treatment of refractory CML as well as other WT1-overexpressing malignant cancers.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.

Efficient synthesis and anticancer evaluation of spider toxin peptide LVTX-8-based analogues with enhanced stability

Cytotoxic peptides derived from spider venoms have been considered as promising candidates for anticancer treatment. The novel cell penetrating peptide LVTX-8, which is a 25-residue amphipathic α-helical peptide isolated from spider Lycosa vittata, exhibited potent cytotoxicity and is a potential precursor for further anticancer drug development. Nevertheless, LVTX-8 may be easily degraded by multiple proteases, inducing the proteolytic stability problem and short half-life. In this study, ten LVTX-8-based analogs were rationally designed and the efficient manual synthetic method was established by the DIC/Oxyma based condensation system. The cytotoxicity of synthetic peptides was systematically evaluated against seven cancer cell lines. Seven of the derived peptides exhibited high cytotoxicity towards tested cancer in vitro, which was better than or comparable to that of natural LVTX-8. In particular, both N-acetyl and C-hydrazide modified LVTX-8 (825) and the conjugate methotrexate (MTX)-GFLG-LVTX-8 (827) possessed more durable anticancer efficiency, higher proteolytic stability, as well as lower hemolysis. Finally, we confirmed that LVTX-8 could disrupt the integrity of cell membrane, target the mitochondria and reduce the mitochondrial membrane potential to induce the cell death. Taken together, the structural modifications were conducted on LVTX-8 for the first time and the stability significantly improved derivatives 825 and 827 may provide useful references for the modifications of cytotoxic peptides.

One New Acid-Activated Hybrid Anticancer Peptide by Coupling with a Desirable pH-Sensitive Anionic Partner Peptide

Anticancer peptides (ACPs) are promising antitumor resources, and developing acid-activated ACPs as more effective and selective antitumor drugs would represent new progress in cancer therapy. In this study, we designed a new class of acid-activated hybrid peptides LK-LE by altering the charge shielding position of the anionic binding partner LE based on the cationic ACP LK and investigated their pH response, cytotoxic activity, and serum stability, in hoping to achieve a desirable acid-activatable ACP. As expected, the obtained hybrid peptides could be activated and exhibit a remarkable antitumor activity by rapid membrane disruption at acidic pH, whereas its killing activity could be alleviated at normal pH, showing a significant pH response compared with LK. Importantly, this study found that the peptide LK-LE3 with the charge shielding in the N-terminal of LK displayed notably low cytotoxicity and more stability, demonstrating that the position of charge masking is extremely important for the improvement of peptide toxicity and stability. In short, our work opens a new avenue to design promising acid-activated ACPs as potential targeting agents for cancer treatment.

A Review on Genotoxic and Genoprotective Effects of Biologically Active Compounds of Animal Origin

Envenomation by animal venoms remains a serious medical and social problem, especially in tropical countries. On the other hand, animal venoms are widely used as a source of biologically active compounds for the development of novel drugs. Numerous derivatives of animal venoms are already used in clinical practice. When analysing the mechanisms of action of animal venoms, attention is usually focused on the main target of the venom's enzymes and peptides such as neurotoxic, cytotoxic or haemorrhagic effects. In the present review, we would like to draw attention to the "hidden" effects of animal venoms and their derivatives in regard to DNA damage and/or protection against DNA damage. Alkaloids and terpenoids isolated from sponges such as avarol, ingenamine G or variolin B manifest the capability to bind DNA in vitro and produce DNA breaks. Trabectidin, isolated from a sea squirt, also binds and damages DNA. A similar action is possible for peptides isolated from bee and wasp venoms such as mastoparan, melectin and melittin. However, DNA lesions produced by the crude venoms of jellyfish, scorpions, spiders and snakes arise as a consequence of cell membrane damage and the subsequent oxidative stress, whereas certain animal venoms or their components produce a genoprotective effect. Current research data point to the possibility of using animal venoms and their components in the development of various potential therapeutic agents; however, before their possible clinical use the route of injection, molecular target, mechanism of action, exact dosage, possible side effects and other fundamental parameters should be further investigated.

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.