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Purpose of Review: This review aims to elucidate the mechanisms employed by Host Defense Peptides (HDPs) in propagating their effector functions, associated bottlenecks complicating their therapeutic applications, and how these peptides can be optimized or modified as potential next-generation lead agents in targeted therapy in managing infectious diseases and cancer. Recent Findings: Emerging studies are affirming that due to dwindling therapeutic options, infectious diseases and cancers undoubtedly remain major threats to public health. This is mostly attributable to the toxicity and resistance of classical drugs in combating these health challenges, thus, novel bio-inspired treatment strategies are being sought. As the first line of defense in all organisms, the innate immune system produces many immune effector molecules including a group of biologically active endogenous peptides collectively known as Host Defense Peptides (HDPs), which can be further sub-classified into Antimicrobial Peptides (AMPs), or Anticancer Peptides (ACPs) based on their effector functions. These highly diverse, ubiquitous, and low-molecular-weight biomolecules are being identified and proposed as models for new drug/lead candidates. This is owing to their unique mechanism of action, sequence, and structural diversities which are integral to their downstream biological functions. Therefore, continuous efforts to elucidate the exploitability of these quintessential peptides in drug discovery research against these pathologies are worth exploring. Summary: The ubiquity, significant diversities, and neoteric novel biological activities of HDPs as one of the effectors of the innate immune system establish them as a unique and valuable therapeutic strategy for combating infectious diseases and cancer. They are especially becoming prominent in overcoming the limitations posed by conventional therapeutic agents in managing these diseases, making them promising candidates for future drug development. Furthermore, their diverse functions and numerous molecular targets will play a crucial role in their discovery as new therapeutics. [ABSTRACT FROM AUTHOR]

Background: Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. Results: This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model—evolutionary scale modeling (ESM-2)—to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models—CapsuleGAN, GRU, and CNN—while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. Conclusion: In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) is a potential alternative for future cancer therapy. ACPs specifically inhibit cancer cells through a non-enzymatic membranolysis mechanism. One potential anticancer peptide worthy of investigation is BZR-cotoxin IV. Currently, research on BZR-cotoxin IV is limited to isolation, making it an interesting area for further development. One approach to developing BZR-cotoxin IV is through chemical synthesis techniques. This compound belongs to the group of cyclodepsipeptides that are naturally produced by the fungus Bipolaris zeicola Race 3. The synthesis of BZR cotoxin IV involves 3 steps: (1) Synthesis of the hydroxy acid precursor by conversion of L-valine amino acid to (2R)-hydroxyisovaleric acid, (2) synthesis of the linear depsipeptide using solid-phase peptide synthesis on 2-chlorotrityl chloride resin and (3) cyclization of the linear depsipeptide using solution phase synthesis to produce the BZR cotoxin IV compound with a purity of 11.7%. BZR-cotoxin IV was characterized using HR-TOF-MS and ¹H and 13C-NMR to validate the desired product. Anticancer activity testing was performed using the Resazurin assay on the HeLa cancer cell line, resulting in an IC50 of 187.50 µg/mL (214.014 µM) categorized as moderate. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, cancer is a leading cause of death worldwide. Several bioactive molecules, such as peptides, have been developed to adjuvate in cancer therapy. Previous evidence showed that the peptides KLKKNL, MLKSKR, KKYRVF, FRTKKK, and SVVAKAPVGKR, identified from a protein fraction of S. hispanica seeds, may serve as adjuvant therapy based on their physicochemical properties. Thus, this work aimed to evaluate the cytotoxic effect of these peptides on cancer cells through in silico and in vitro assays. Molecular dynamics simulations were performed to determine the interaction of the peptides with a cancer cell membrane model. Additionally, cell viability assays were performed to assess the effect of the peptides on MCF-7, Caco2, HepG2, DU145, HeLa, and hFB cell lines, and on erythrocytes. Both in silico and in vitro evaluations reported that KLKKNL, MLKSKR, KKYRVF, FRTKKK, and SVVAKAPVGKR interacted with the cancer cell membrane, significantly decreasing their viability. Molecular dynamics of KKYRVF exhibited a stable interaction with the cancer cell membrane model (-3.2 Kcal·mol-1), and experimentally showed selective cytotoxicity on the cancer cell lines. These findings support the continuous assessment of these peptides as adjuvants in cancer treatment for their potential as ingredients in functional foods or nutraceuticals. [Display omitted] • Lung, colorectum, liver and breast cancers are the top types of cancer to cause death. • In silico and in vitro assays successfully predict the anticancer activity of peptides. • KLKKNKL, MLKSKR, KKYRVF, FRTKKK, and SVVAKAPVGKR report anticancer properties. • The five peptides reported interaction with the cancer cell membrane in silico. • The predictions were validated since the peptides decreased the cancer cells' viability. [ABSTRACT FROM AUTHOR]

The linear undecapeptide BP52 was previously reported to have antibacterial activity against phytopathogenic bacteria species. Due to the structural similarities to naturally occurring cationic helical antimicrobial peptides, it was speculated that this peptide could potentially target microbial pathogens and cancer cells found in mammals. Consequently, this study aims to further investigate the structural and biological properties of this peptide. Our findings indicate that BP52 exhibits strong antimicrobial and anticancer activity while displaying relatively low levels of hemolytic activity. Hence, this study suggests that BP52 could be a potential lead compound for drug discovery against infectious diseases and cancer. Besides, new insights into the relationships between the structure and the multifunctional properties of antimicrobial peptides were also explored. [ABSTRACT FROM AUTHOR]

Abstract Background Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. Results This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model—evolutionary scale modeling (ESM-2)—to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models—CapsuleGAN, GRU, and CNN—while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. Conclusion In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides .

Background: Interleukin-21 (IL-21) is a cytokine produced by various cell types, including T cells, natural killer cells, myeloid cells, and B cells, and has a broad range of potential applications in cancer therapy. To improve the therapeutic index, we explored the use of fusion technologies that involved linking other anticancer peptides to the IL-21 gene using specific linkers. Objectives: This study aimed to compare the anticancer potential of IL-21 and IL-21 fusion proteins. Methods: Antimicrobial peptides possessing anticancer properties were fused with IL-21 gene using a flexible linker (-GGGGS-), and the resulting construct was inserted into the pSecTag2a mammalian expression vector. The cassette was transfected into several cancer cell lines including H1 HeLa, HepG2, MCF-7, MDA-MB-231, HCT-116, HCC-1954, HEK-293, and SF-767. The cytotoxic effects of IL-21 and fusion proteins were evaluated using MTT, Caspase-3, LDH, and scratch assays. Results: The IL-21-Tachyplesin I fusion protein had the strongest antiproliferative activity against all tested cancer cells, followed by IL21-LPSBD2 and IL-21. In contrast, IL21-Cop A3, IL21-CSP I-Plus, and IL21-RGD Temporin-Las did not inhibit the viability of cancer cells. Conclusion: Fusion technology is a promising therapeutic technique that can be used to enhance the cytotoxicity and antiproliferative activity of anticancer proteins such as IL-21. [ABSTRACT FROM AUTHOR]

Cancer remains a concern after years of research in this field. Conventional therapies such as chemotherapy, radiation, and surgery are available for cancer treatment, but they are characterized by various side effects. There are several immunological challenges that make it difficult for the immune system and conventional therapies to treat cancer. Some of these challenges include heterogeneity, resistance to medicines, and cancer relapse. Even advanced treatments like immune checkpoint inhibitors (ICIs), which revolutionized cancer treatment, have associated toxicity and resistance further necessitate the exploration of alternative therapies. Anticancer peptides (ACPs) offer promising potential as cancer-fighting agents and address challenges such as treatment resistance, tumor heterogeneity, and metastasis. Although these peptides exist as components of the defense system in various plants, animals, fungi, etc., but can also be created synthetically and used as a new treatment measure. These peptides possess properties that make them appealing for cancer therapy, such as apoptosis induction, inhibition of angiogenesis, and cell membrane breakdown with low toxicity. Their capacity to specifically target cancer cells selectively holds promise for enhancing treatment environments as well as improving patients’ quality of life. This review provides detailed insights into the different prospects of ACPs, including their characterization, use as immunomodulatory agents in cancer treatment, and their mechanistic details after addressing various immunological challenges in existing cancer treatment strategies. In conclusion, ACPs have promising potential as novel cancer therapeutics due to their target specificity and fewer side effects than conventional therapies.

Abstract The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.

Targeted delivery of therapeutic anticancer chimeric molecules enhances drug efficacy. Numerous studies have focused on developing novel treatments by employing cytokines, particularly interleukins, to inhibit the growth of cancer cells. In the present study, we fused interleukin 24 with the tumor-targeting peptide P20 through a rigid linker to selectively target cancer cells. The secondary structure, tertiary structure, and physicochemical characteristics of the constructed chimeric IL-24-P20 protein were predicted by using bioinformatics tools. In-silico analysis revealed that the fusion construct has a basic nature with 175 amino acids and a molecular weight of 20 kDa. By using the Rampage and ERRAT2 servers, the validity and quality of the fusion protein were evaluated. The results indicated that 93% of the chimeric proteins contained 90.1% of the residues in the favoured region, resulting in a reliable structure. Finally, docking and simulation studies were conducted via ClusPro and Desmond Schrödinger, respectively. Our results indicate that the constructed fusion protein exhibits excellent quality, interaction capabilities, validity, and stability. These findings suggest that the fusion protein is a promising candidate for targeted cancer therapy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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The low drug resistance, excellent cancer cell targeting, and anticancer benefits of anticancer peptides (ACPs) make them an attractive antitumor drug option. On the other hand, enzymes readily break down in many ACPs after delivery due to their non-specific toxicity. Consequently, drug delivery systems (DDSs) are necessary to avoid the peptides' dissociation and persuade targeted delivery. The cationic amphiphilic anticancer peptide, G(IIKK) 3 I-NH 2 (G3), was developed in this work using a high-performance microfluidic system, which encapsulates the zeolitic imidazolate framework (ZIF-8). The ACPs-loaded nanoparticles (NPs) were efficiently and quickly mixed using the microfluidic system, resulting in NPs with tunable properties. The encapsulation efficacy was high, and the production rate was 120 mL/min. The microfluidic technique-based ZIF-8 fabrication demonstrated better size homogeneity, as evidenced by a decreased polydispersity index (PDI) compared to the conventional technique. The hemolytic effect was much diminished, and a pH-controlled release of the G3 peptides was achieved by encapsulating G(IIKK) 3 I-NH 2 (termed as G3@ZIF-8) into the ZIF-8. Owing to the improved cell absorption by the ZIF-8, G3@ZIF-8 demonstrated a stronger anticancer impact than the free G3 peptide at identical concentrations. In addition to damaging the mitochondrial membrane of HCT 116 colorectal cancer cells, the NPs were able to suppress the proliferation. Consequently, ACP-based cancer therapy can be expanded by developing G3-loaded ZIF-8-NPs, offering an option technique for ACP-based delivery. [Display omitted] • The anticancer peptide G(IIKK) 3 I-NH 2 (G3) encapsulates the zeolitic imidazolate framework (ZIF-8). • The ACPs-loaded nanoparticles (NPs) were efficiently developed microfluidic system. • The encapsulation efficacy was high, and the production rate was 120 mL/min. • G3@ZIF-8 showed a stronger anticancer impact than the free G3 peptide. • NPs suppress colon cancer cells proliferation by mitochondrial membrane potential. [ABSTRACT FROM AUTHOR]

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research. [ABSTRACT FROM AUTHOR]

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC. [ABSTRACT FROM AUTHOR]

Glioma cells overexpress different peptide receptors that are useful for research, diagnosis, management, and treatment of the disease. Oncogenic peptides favor the proliferation, migration, and invasion of glioma cells, as well as angiogenesis, whereas anticancer peptides exert antiproliferative, antimigration, and anti-angiogenic effects against gliomas. Other peptides exert a dual effect on gliomas, that is, both proliferative and antiproliferative actions. Peptidergic systems are therapeutic targets, as peptide receptor antagonists/peptides or peptide receptor agonists can be administered to treat gliomas. Other anticancer strategies exerting beneficial effects against gliomas are discussed herein, and future research lines to be developed for gliomas are also suggested. Despite the large amount of data supporting the involvement of peptides in glioma progression, no anticancer drugs targeting peptidergic systems are currently available in clinical practice to treat gliomas. [ABSTRACT FROM AUTHOR]

Background: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications. [ABSTRACT FROM AUTHOR]

Cancer is known as the main cause of mortality in the world, and every year, the rate of incidence and death due to cancer is increasing. Bioactive peptides are one of the novel therapeutic options that are considered a suitable alternative to toxic chemotherapy drugs because they limit side effects with their specific function. In fact, bioactive peptides are short amino acid sequences that obtain diverse physiological functions to maintain human health after being released from parent proteins. This group of biological molecules that can be isolated from different types of natural protein sources has attracted much attention in the field of pharmaceutical and functional foods production. The current article describes the therapeutic benefits of bioactive peptides and specifically and extensively reviews their role in cancer treatment, available sources for discovering anticancer peptides, mechanisms of action, production methods, and existing challenges.

Abstract Antimicrobial peptides (AMPs) have sparked significant interest as potential anti-cancer agents, thereby becoming a focal point in pursuing novel cancer-fighting strategies. These peptides possess distinctive properties, underscoring the importance of developing more potent and selectively targeted versions with diverse mechanisms of action against human cancer cells. Such advancements would offer notable advantages compared to existing cancer therapies. This research aimed to examine the toxicity and selectivity of the nrCap18 peptide in both cancer and normal cell lines. Furthermore, the rate of cellular death was assessed using apoptosis and acridine orange/ethidium bromide (AO/EB) double staining at three distinct incubation times. Additionally, the impact of this peptide on the cancer cell cycle and migration was evaluated, and ultimately, the expression of cyclin-dependent kinase 4/6 (CDK4/6) genes was investigated. The results obtained from the study demonstrated significant toxicity and selectivity in cancer cells compared to normal cells. Moreover, a strong progressive increase in cell death was observed over time. Furthermore, the peptide exhibited the ability to halt the progression of cancer cells in the G1 phase of the cell cycle and impede their migration by suppressing the expression of CDK4/6 genes.

Simple Summary: This study focused on finding effective anticancer peptides (ACPs) from induced tumor-suppressing cells (iTSCs) to combat breast cancer metastasis to the skeletal system. This study identified P18 as the most potent ACP from a pool of candidates, derived from a protein called Arhgdia. P18 showed significant inhibitory effects on breast cancer cell viability, migration, and invasion, as well as interfering with certain cancer-promoting proteins and GTPase signaling. Importantly, P18 demonstrated enhanced effectiveness when combined with traditional chemotherapy drugs, without significantly affecting healthy cells. Moreover, it mitigated the bone loss associated with breast cancer spread to bones. This study suggests that P18, especially in its modified form (Ac-P18-NH2), could be a promising candidate for breast cancer treatment and preventing bone destruction by regulating specific cellular signaling pathways. Background: The skeletal system is a common site for metastasis from breast cancer. In our prior work, we developed induced tumor-suppressing cells (iTSCs) capable of secreting a set of tumor-suppressing proteins. In this study, we examined the possibility of identifying anticancer peptides (ACPs) from trypsin-digested protein fragments derived from iTSC proteomes. Methods: The efficacy of ACPs was examined using an MTT-based cell viability assay, a Scratch-based motility assay, an EdU-based proliferation assay, and a transwell invasion assay. To evaluate the mechanism of inhibitory action, a fluorescence resonance energy transfer (FRET)-based GTPase activity assay and a molecular docking analysis were conducted. The efficacy of ACPs was also tested using an ex vivo cancer tissue assay and a bone microenvironment assay. Results: Among the 12 ACP candidates, P18 (TDYMVGSYGPR) demonstrated the most effective anticancer activity. P18 was derived from Arhgdia, a Rho GDP dissociation inhibitor alpha, and exhibited inhibitory effects on the viability, migration, and invasion of breast cancer cells. It also hindered the GTPase activity of RhoA and Cdc42 and downregulated the expression of oncoproteins such as Snail and Src. The inhibitory impact of P18 was additive when it was combined with chemotherapeutic drugs such as Cisplatin and Taxol in both breast cancer cells and patient-derived tissues. P18 had no inhibitory effect on mesenchymal stem cells but suppressed the maturation of RANKL-stimulated osteoclasts and mitigated the bone loss associated with breast cancer. Furthermore, the P18 analog modified by N-terminal acetylation and C-terminal amidation (Ac-P18-NH2) exhibited stronger tumor-suppressor effects. Conclusions: This study introduced a unique methodology for selecting an effective ACP from the iTSC secretome. P18 holds promise for the treatment of breast cancer and the prevention of bone destruction by regulating GTPase signaling. [ABSTRACT FROM AUTHOR]

In recent yearsjajajj, peptide-based therapeutics have attracted increasing interest as a potential approach to cancer treatment. Peptides are characterized by high specificity and low cytotoxicity, but they cannot be considered universal drugs for all types of cancer. Of the numerous anticancer-reported peptides, both natural and synthetic, only a few have reached clinical applications. However, in most cases, the mechanism behind the anticancer activity of the peptide is not fully understood. For this reason, in this work, we investigated the effect of the novel peptide ∆M4, which has documented anticancer activity, on two human skin cancer cell lines. A novel approach to studying the potential induction of apoptosis by anticancer peptides is the use of protein microarrays. The results of the apoptosis protein study demonstrated that both cell types, skin malignant melanoma (A375) and epidermoid carcinoma (A431), exhibited markers associated with apoptosis and cellular response to oxidative stress. Additionally, ∆M4 induced concentration- and time-dependent moderate ROS production, triggering a defensive response from the cells, which showed decreased activation of cytoplasmic superoxide dismutase. However, the studied cells exhibited a differential response in catalase activity, with A375 cells showing greater resistance to the peptide action, possibly mediated by the Nrf2 pathway. Nevertheless, both cell types showed moderate activity of caspases 3/7, suggesting that they may undergo partial apoptosis, although another pathway of programmed death cannot be excluded. Extended analysis of the mechanisms of action of anticancer peptides may help determine their effectiveness in overcoming chemoresistance in cancerous cells. [ABSTRACT FROM AUTHOR]

Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods. [ABSTRACT FROM AUTHOR]

Acquired drug resistance is the major cause for disease recurrence in cancer patients, and this is particularly true for patients with metastatic melanoma that carry a BRAF V600E mutation. To address this problem, we investigated cyclic membrane-active peptides as an alternative therapeutic modality to kill drug-tolerant and resistant melanoma cells to avoid acquired drug resistance. We selected two stable cyclic peptides (cTI and cGm), previously shown to have anti-melanoma properties, and compared them with dabrafenib, a drug used to treat cancer patients with the BRAF V600E mutation. The peptides act via a fast membrane-permeabilizing mechanism and kill metastatic melanoma cells that are sensitive, tolerant, or resistant to dabrafenib. Melanoma cells do not become resistant to long-term treatment with cTI, nor do they evolve their lipid membrane composition, as measured by lipidomic and proteomic studies. In vivo studies in mice demonstrated that the combination treatment of cTI and dabrafenib resulted in fewer metastases and improved overall survival. Such cyclic membrane-active peptides are thus well suited as templates to design new anticancer therapeutic strategies.

Background Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model’s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model’s predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Abstract Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide’s anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.

Abstract 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.

The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large‐scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface‐exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences. [ABSTRACT FROM AUTHOR]

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein–protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation). [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art. [ABSTRACT FROM AUTHOR]

Simple Summary: Anticancer peptides are short aminoacidic chains, which display selective cytotoxicity mostly against tumor, but not healthy, cells through interference with intracellular biological events. The marine environment features an ever-growing level of biodiversity and, therefore, seas and oceans are indeed 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, which have found broad use for various applications in healthcare management, due to their anticancer, anti-angiogenic, anti-inflammatory and regeneration abilities. The aim of this review is to pick and list 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. 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. [ABSTRACT FROM AUTHOR]

Temporin-SHf is a linear, ultra-short, hydrophobic, α-helix, and phe-rich cationic antimicrobial peptide. The antitumor activities and mechanism of temporin-SHf-induced cancer cell death are unknown. The temporin-SHf was synthesized by solid-phase Fmoc chemistry and antimicrobial and antitumor activities were investigated. Temporin-SHf was microbiocidal, non-hemolytic, and cytotoxic to human cancer cells but not to non-tumorigenic cells. It affected the cancer cells' lysosomal integrity and caused cell membrane damage. The temporin-SHf inhibited A549 cancer cell proliferation and migration. It is anti-angiogenic and causes cancer cell death through apoptosis. The molecular mechanism of action of temporin-SHf confirmed that it kills cancer cells by triggering caspase-dependent apoptosis through an intrinsic mitochondrial pathway. Owing to its short length and broad spectrum of antitumor activity, temporin-SHf is a promising candidate for developing a new class of anticancer drugs. [ABSTRACT FROM AUTHOR]

Purpose: Aurein 1.2 (Aur) peptide is known for possessing anticancer characteristics devoid of conventional therapeutics side effects. For improving Aur peptide anticancer functionality, different anticancer peptides were constructed based on Aur peptide through targeting two separate strategies, including (1) sequence-based mutations and (2) adding a cell-penetrating peptide linker. Methods: The study was approached by designing three different analogs of Aur, including (a) Aur mutant (Aurm), (b) Aur with N-terminal polyarginine linker (R5-Aur), and (c) Aurm with R5 (R5-Aurm). Computational molecular dynamics simulations clearly showed higher structural stability of R5-Aur and R5-Aurm compared to Aur, solely. The α-helical properties of R5-Aur and R5-Aurm were protected during 500 ns simulations in water solution while no such structural conservation was seen for Aur in silico. Results: The results of the current study highlight response to one of the main challenges of cancer therapy through selective invasion of Aur to cancer cells without significant involvement of normal cells. This issue was confirmed by different assays, including: MTT assay, flow cytometry, qPCR, and nuclei morphological observations. Furthermore, this study intensifies exploiting in silico approaches for adjusting drug delivery. The results of different assessments on designed peptides reveal an anticancer activity pattern rising from Aur toward Aurm, and R5- Aur, consecutively. Conclusion: The designed structure of Aur shows improved anticancer activity through molecular changes which makes it suggestable for anticancer therapies.

Abstract Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent.

Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.

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.

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides. [ABSTRACT FROM AUTHOR]

Recent research highlights the key role of iron dyshomeostasis in the pathogenesis of prostate cancer (PCa). PCa cells are heavily dependent on bioavailable iron, which frequently results in the reprogramming of iron uptake and storage pathways. Although advanced-stage PCa is currently incurable, bioactive peptides capable of modulating key iron-regulatory genes may constitute a means of exploiting a metabolic adaptation necessary for tumor growth. Recent annual increases in PCa incidence have been reported, highlighting the urgent need for novel treatments. We examined the ability of LNCaP, PC3, VCaP, and VCaP-EnzR cells to form colonies in the presence of androgen receptor inhibitors (ARI) and a series of iron-gene modulating oligopeptides (FT-001-FT-008). The viability of colonies following treatment was determined with clonogenic assays, and the expression levels of FTH1 (ferritin heavy chain 1) and TFRC (transferrin receptor) were determined with quantitative polymerase chain reaction (PCR). Peptides and ARIs combined significantly reduced PCa cell growth across all phenotypes, of which two peptides were the most effective. Colony growth suppression generally correlated with the magnitude of concurrent increases in FTH1 and decreases in TFRC expression for all cells. The results of this study provide preliminary insight into a novel approach at targeting iron dysmetabolism and sensitizing PCa cells to established cancer treatments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent yearsjajajj, peptide-based therapeutics have attracted increasing interest as a potential approach to cancer treatment. Peptides are characterized by high specificity and low cytotoxicity, but they cannot be considered universal drugs for all types of cancer. Of the numerous anticancer-reported peptides, both natural and synthetic, only a few have reached clinical applications. However, in most cases, the mechanism behind the anticancer activity of the peptide is not fully understood. For this reason, in this work, we investigated the effect of the novel peptide ∆M4, which has documented anticancer activity, on two human skin cancer cell lines. A novel approach to studying the potential induction of apoptosis by anticancer peptides is the use of protein microarrays. The results of the apoptosis protein study demonstrated that both cell types, skin malignant melanoma (A375) and epidermoid carcinoma (A431), exhibited markers associated with apoptosis and cellular response to oxidative stress. Additionally, ∆M4 induced concentration- and time-dependent moderate ROS production, triggering a defensive response from the cells, which showed decreased activation of cytoplasmic superoxide dismutase. However, the studied cells exhibited a differential response in catalase activity, with A375 cells showing greater resistance to the peptide action, possibly mediated by the Nrf2 pathway. Nevertheless, both cell types showed moderate activity of caspases 3/7, suggesting that they may undergo partial apoptosis, although another pathway of programmed death cannot be excluded. Extended analysis of the mechanisms of action of anticancer peptides may help determine their effectiveness in overcoming chemoresistance in cancerous cells.

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. [ABSTRACT FROM AUTHOR]

The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well‐established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi‐center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types. [ABSTRACT FROM AUTHOR]

Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent. [ABSTRACT FROM AUTHOR]

[Display omitted] Anticancer peptides (ACPs) are promising antitumor drugs owning to their great cancer cell targeting and anticancer effects as well as low drug resistance. However, many of the ACPs have non-specific toxicity and can be easily degraded by the enzymes after administration. Therefore, drug delivery systems (DDSs) are required to shield these peptides from degradation and induce targeted delivery. In this paper, a high performance microfluidic device was used to fabricate the zeolitic imidazolate framework (ZIF-8) encapsulating an ACP (At3) recently developed by our group. The microfluidic device allowed for efficient and rapid mixing to generate ACP loaded nanoparticles (NPs) with controllable properties at high production rate (120 mL/min) and high encapsulation efficiency. The ZIF-8 NPs synthesised by microfluidic processing showed lower polydispersity index (PDI) than the conventional method, demonstrating an improved size uniformity. Encapsulating At3 into the ZIF-8 (At3@ZIF-8) significantly reduced the hemolytic effect and provided a pH-controlled release of At3 peptide. At3@ZIF-8 showed higher anticancer effect than the unloaded peptide at the same concentration due to the enhanced cell uptake by the ZIF-8 NPs. The NPs were able to inhibit the growth of the multicellular tumour spheroids (MCTSs) and damage the mitochondrial membrane of the MCF-7 breast cancer cells. In vivo experiments demonstrated that the At3@ZIF-8 NPs inhibited the growth of MCF-7 tumours in nude mice without changing the biochemical properties of the blood or the histopathological properties of vital organs. Therefore, the development of At3 loaded NPs provides an alternative approach in ACP delivery which can broaden the application of ACP-based cancer therapy. [ABSTRACT FROM AUTHOR]

Purpose: Aurein 1.2 (Aur) peptide is known for possessing anticancer characteristics devoid of conventional therapeutics side effects. For improving Aur peptide anticancer functionality, different anticancer peptides were constructed based on Aur peptide through targeting two separate strategies, including (1) sequence-based mutations and (2) adding a cell-penetrating peptide linker. Methods: The study was approached by designing three different analogs of Aur, including (a) Aur mutant (Aurm), (b) Aur with N-terminal polyarginine linker (R5-Aur), and (c) Aurm with R5 (R5-Aurm). Computational molecular dynamics simulations clearly showed higher structural stability of R5-Aur and R5-Aurm compared to Aur, solely. The a-helical properties of R5-Aur and R5-Aurm were protected during 500 ns simulations in water solution while no such structural conservation was seen for Aur in silico. Results: The results of the current study highlight response to one of the main challenges of cancer therapy through selective invasion of Aur to cancer cells without significant involvement of normal cells. This issue was confirmed by different assays, including: MTT assay, flow cytometry, qPCR, and nuclei morphological observations. Furthermore, this study intensifies exploiting in silico approaches for adjusting drug delivery. The results of different assessments on designed peptides reveal an anticancer activity pattern rising from Aur toward Aurm, and R5-Aur, consecutively. Conclusion: The designed structure of Aur shows improved anticancer activity through molecular changes which makes it suggestable for anticancer therapies. [ABSTRACT FROM AUTHOR]

Objective: The growing microbial resistance to antibiotics is a global public concern, which creates serious needs for newer antimicrobial agents. Antimicrobial peptides (AMPs) are increasingly exploited in drug development as therapeutic candidates. Here, we aimed to design and characterize a novel peptide with broad spectrum antimicrobial activity. Methods: Hybridization and sequence modification approaches were used to design the novel peptide, named HAZ, aiming at optimizing the physicochemical parameters involved in antimicrobial activity. Peptide activities were assessed alone or combined with different selected antibiotics against various sensitive and drug-resistant bacterial strains. In addition, the hemolysis and the cytotoxic activities of HAZ peptide were evaluated on human red blood cells and epithelial adenocarcinoma cells (A549), respectively. Results: HAZ peptide was sequentially modified to result in favored physicochemical parameters (helicity 95.24 %, hydrophobic ratio 47 %, and net charge of 8 + ). Functional assessment of HAZ revealed significant antimicrobial activity, with MIC values of 15 – 20 µM against tested bacterial strains. It also exhibited biofilm eradication activity at slightly higher concentrations. HAZ-antibiotics combinations exhibited a synergistic action mode that led to dramatic decrease in the MIC values for both HAZ peptide and the antibiotic. Such efficacy was accompanied with minimal hemolytic toxicity on human erythrocytes. Importantly, HAZ displayed promising anticancer activity against human lung cancer cells. Conclusion: Rationally-designed antimicrobial peptides offer promising alternatives to the current antibiotics for management of infectious diseases. HAZ peptide is a broad-spectrum AMP, and a promising candidate for antimicrobial and anticancer drug development.

Abstract The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well‐established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi‐center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types.

Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold crossvalidation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods. [ABSTRACT FROM AUTHOR]