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The oral cavity is a frequent site for head and neck cancers, which rank as the sixth most common cancer globally, with a 5-year survival rate slightly over 50%. Current treatments are limited, and resistance to therapy remains a significant clinical obstacle. IsCT1, a membrane-active peptide derived from the venom of the scorpion Opisthacanthus madagascariensis, has shown antitumor effects in various cancer cell lines, including breast cancer and chronic myeloid leukemia. However, its hemolytic action limits its potential therapeutic use. This study aims to assess the antitumor and antiproliferative activities of synthetic peptides derived from IsCT1 (IsCT-P, AC-AFPK-IsCT1, AFPK-IsCT1, AC-KKK-IsCT1, and KKK-IsCT1) in the context of oral squamous cell carcinoma. We evaluated the cytotoxic effects of these peptides on tongue squamous cell carcinoma cells and normal cells, as well as their impact on cell cycle phases, the expression of proliferation markers, modulators of cell death pathways, and mitochondrial potential. Our results indicate that the IsCT1 derivatives IsCT-P and AC-AFPK-IsCT1 possess cytotoxic properties towards squamous cell carcinoma cells, reducing mitochondrial membrane potential and the proliferative index. The treatment of cancer cells with AC-AFPK-IsCT1 led to a positive modulation of pro-apoptotic markers p53 and caspases 3 and 8, a decrease in PCNA and Cyclin D1 expression, and cell cycle arrest in the S phase. Notably, contrary to the parental IsCT1 peptide, AC-AFPK-IsCT1 did not exhibit hemolytic activity or cytotoxicity towards normal cells. Therefore, AC-AFPK-IsCT1 might be a viable therapeutic option for head and neck cancer treatment. [ABSTRACT FROM AUTHOR]

Anti‐cancer peptides (ACPs) represent a promising potential for cancer treatment, although their mechanisms need to be further elucidated to improve their application in cancer therapy. Lycosin‐I, a linear amphipathic peptide isolated from the venom of Lycosa singorensis, shows significant anticancer potential. Herein, it is found that Lycosin‐I, which can self‐assemble into a nanosphere structure, has a multimodal mechanism of action involving lipid binding for the selective and effective treatment of leukemia. Mechanistically, Lycosin‐I selectively binds to the K562 cell membrane, likely due to its preferential interaction with negatively charged phosphatidylserine, and rapidly triggers membrane lysis, particularly at high concentrations. In addition, Lycosin‐I induces apoptosis, cell cycle arrest in the G1 phase and ferroptosis in K562 cells by suppressing the PI3K‐AKT‐mTOR signaling pathway and activating cell autophagy at low concentrations. Furthermore, intraperitoneal injection of Lycosin‐I inhibits tumor growth of K562 cells in a nude mouse xenograft model without causing side effects. Collectively, the multimodal effect of Lycosin‐I can provide new insights into the mechanism of ACPs, and Lycosin‐I, which is characterized by high potency and specificity, can be a promising lead for the development of anti‐leukemia drugs. [ABSTRACT FROM AUTHOR]

Conventional cancer therapies are highly expensive and have serious complications. An alternative approach now emphasizes on the development of small, biologically active peptides without acute toxicity. Experimental screening to find curative anticancer peptides (ACP) often gives rise to multiple obstacles and is time dependent. Consequently, developing an effective computational technique to identify promising ACP candidates prior to preclinical research is in high demand. This study proposed a machine-learning framework that used the light gradient-boosting machine as a classifier and two compositional and two binary profile features as input. The ensemble model displayed an accuracy, MCC, and AUROC of 97.52%, 0.91, and 0.98, respectively, which outclassed most of the existing sequence-based computational tools. A distinct dataset of non-mutagenic, non-toxic, and non-inhibitory Cytochrome P-450 peptides was used to validate the hybrid model. The most relevant ACP in the alternative dataset was compared with two standard ACPs, beta defensin 2, and cecropin-A. Molecular docking of the predicted peptide revealed that it has a strong binding affinity with twenty-five anticancer drug targets, most notably phosphoenolpyruvate carboxykinase (− 7.2 kcal/mol). Additionally, molecular dynamics simulation and principal component analysis supported the stability of the peptide-receptor complex. Overall, the present findings will take a step forward in rational drug design through rapid identification and screening of therapeutic peptides. [ABSTRACT FROM AUTHOR]

Liver cancer is a worldwide issue that also affects the Malaysian population. The occurrence is closely related to risk factors like chronic infections and environmental exposures. Due to the toxicity of conventional therapeutic drugs for liver cancer, bioactive peptides have emerged as a popular alternative anticancer agent. Although the full-length pardaxin from Pardachirus marmoratus was proven with anticancer effects, its concurrent haemolytic effects are yet to be resolved. Therefore, this study utilized in silico and in vitro analyses to assess cytotoxic effects induced by the shortened pardaxin derivatives. The in silico findings led to the discovery of a series of shortened pardaxin derivatives with 13 amino acids, where single residue replacement prediction by bioinformatics tools was done on the shortened sequences. Among the top five shortened derivatives, the derivative where amino acid threonine was replaced by proline, was identified as the most potential candidate, namely LL13. The LL13 peptide was predicted with improved anticancer effects, non-toxic, and alleviated haemolytic effects as compared to its parental peptide. The subsequent cytotoxicity testing further validated its selective toxicity against liver cancer cells, HepG2 cells, with relatively lower killing effects on the normal cells, Vero cells. These in vitro findings validated the in silico predictions and also indicated that this peptide has potential as an anticancer drug with selective targeting capabilities. In conclusion, this study has highlighted the potential of using a combination of in silico and in vitro approaches to discover potentially shortened peptides as a novel therapeutic option for liver cancer treatment. [ABSTRACT FROM AUTHOR]

Snake venom metalloproteinases (SVMPs) are enzymatic proteins found in snake venom and are known for their diverse biological activities, including induction of hemorrhage and degradation of fibrinogen. This study aimed to design and characterize anticancer peptides (ACPs) derived from an SVMP library based on their physicochemical properties. A comprehensive analysis predicted 185 ACPs and 177 non-ACPs from 652 SVMPs using a SVM algorithm. Among these, only 23 ACPs demonstrated the ability to penetrate cell membranes, of which 5 were selected as promising candidates. A reliable SVM and confidence scores were obtained for all ACP predictions. The predicted ACPs showed optimal hydrophobicity and favorable structural stability in plasma. The predicted ACPs were characterized by low solubility, high rigidity, and high interaction potential based on their net charge, net hydrogen, and steric hindrance. Among the five ACPs, ACP1 (GDLAAIRKRV) and ACP3 (GDETEIRSRI) had unique amino acid compositions, specifically arginine, lysine, aspartic acid, glutamic acid, and α-helical structures. Molecular docking simulations indicated their interactions with various cancer target proteins, leading to inhibit tumor cell proliferation or migration. In conclusion, ACP01 and ACP03 are potential candidates for the future treatment of breast cancer and leukemia. [ABSTRACT FROM AUTHOR]

Cancer, with its complexity and numerous origins, continues to provide a huge challenge in medical research. Anticancer peptides are a potential treatment option, but identifying and synthesizing them on a large scale requires accurate prediction algorithms. This study presents an intuitive classification strategy, named ACP-LSE, based on representation learning, specifically, a deep latent-space encoding scheme. ACP-LSE can demonstrate notable advancements in classification outcomes, particularly in scenarios with limited sample sizes and abundant features. ACP-LSE differs from typical black-box approaches by focusing on representation learning. Utilizing an auto-encoder-inspired network, it embeds high-dimensional features, such as the composition of g-spaced amino acid pairs, into a compressed latent space. In contrast to conventional auto-encoders, ACP-LSE ensures that the learned feature set is both small and effective for classification, giving a transparent alternative. The suggested approach is tested on benchmark datasets and demonstrates higher performance compared to the current methods. The results indicate improved Matthew's correlation coefficient and balanced accuracy, offering insights into crucial aspects for developing new ACPs. The implementation of the proposed ACP-LSE approach is accessible online, providing a valuable and reproducible resource for researchers in the field. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive patient care experiences. These advanced technologies, coupled with the power of bioinformatics and genomic data, facilitate groundbreaking developments. The precise prediction of ACPs from complex biological sequences remains an ongoing challenge in the genomic area. Currently, conventional approaches such as chemotherapy, target therapy, radiotherapy, and surgery are widely used for cancer treatment. However, these methods fail to completely eradicate neoplastic cells or cancer stem cells and damage healthy tissues, resulting in morbidity and even mortality. To control such diseases, oncologists and drug designers highly desire to develop new preventive techniques with more efficiency and minor side effects. Therefore, this research provides an optimized computational-based framework for discriminating against ACPs. In addition, the proposed approach intelligently integrates four peptide encoding methods, namely amino acid occurrence analysis (AAOA), dipeptide occurrence analysis (DOA), tripeptide occurrence analysis (TOA), and enhanced pseudo amino acid composition (EPseAAC). To overcome the issue of bias and reduce true error, the synthetic minority oversampling technique (SMOTE) is applied to balance the samples against each class. The empirical results over two datasets, where the accuracy of the proposed model on the benchmark dataset is 97.56% and on the independent dataset is 95.00%, verify the effectiveness of our ensemble learning mechanism and show remarkable performance when compared with state-of-the-art (SOTA) methods. In addition, the application of metaverse technology in healthcare holds promise for transformative innovations, potentially enhancing patient experiences and providing novel solutions in the realm of preventive techniques and patient care. [ABSTRACT FROM AUTHOR]

Introduction: Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends. Methods: Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs. Results: EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis. Conclusion: The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs. [ABSTRACT FROM AUTHOR]

The discovery of new highly effective anticancer drugs with few side effects is a challenge for drug development research. Natural or synthetic anticancer peptides (ACPs) represent a new generation of anticancer agents with high selectivity and specificity. The rapid emergence of chemoradiation-resistant lung cancer has necessitated the discovery of novel anticancer agents as alternatives to conventional therapeutics. In this study, we synthesized a peptide containing 22 amino acids and characterized it as a novel ACP (MP06) derived from green sea algae, Bryopsis plumosa. Using the ACP database, MP06 was predicted to possess an alpha-helical secondary structure and functionality. The anti-proliferative and apoptotic effects of the MP06, determined using the cytotoxicity assay and Annexin V/propidium iodide staining kit, were significantly higher in non-small-cell lung cancer (NSCLC) cells than in non-cancerous lung cells. We confirmed that MP06 suppressed cellular migration and invasion and inhibited the expression of N-cadherin and vimentin, the markers of epithelial–mesenchymal transition. Moreover, MP06 effectively reduced the metastasis of tumor xenografts in zebrafish embryos. In conclusion, we suggest considering MP06 as a novel candidate for the development of new anticancer drugs functioning via the ERK signaling pathway. [ABSTRACT FROM AUTHOR]

Targeted killing of tumor cells while protecting healthy cells is the pressing priority in cancer treatment. Lectins that target a specific glycan marker abundant in cancer cells can be valuable new tools for selective cancer cell killing. The lectin Shiga‐like toxin 1 B subunit (Stx1B) is an example that specifically binds globotriaosylceramide (CD77 or Gb3), which is overexpressed in certain cancers. In this study, a human lactoferricin‐derived synthetic retro di‐peptide R‐DIM‐P‐LF11‐215 with antitumor efficacy was fused to the lectin Stx1B to selectively target and kill Gb3+ cancer cells. We produced lectin‐peptide fusion proteins in Escherichia coli, isolated them by Gb3‐affinity chromatography, and assessed their ability to selectively kill Gb3+ cancer cells in a Calcein AM assay. Furthermore, to expand the applications of R‐DIM‐P‐LF11‐215 in developing therapeutic bioconjugates, we labeled R‐DIM‐P‐LF11‐215 with the unique reactive non‐canonical amino acid Nε‐((2‐azidoethoxy)carbonyl)‐L‐lysine (AzK) at a selected position by amber stop codon suppression. The R‐DIM‐P‐LF11‐215 20AzK and the unlabeled R‐DIM‐P‐LF11‐215 parent peptide were produced as GST‐fusion proteins for soluble expression in E. coli for the first time. We purified both variants by size‐exclusion chromatography and analyzed their peptide masses. Finally, a cyanin 3 fluorophore was covalently conjugated to R‐DIM‐P‐LF11‐215 20AzK by strain‐promoted alkyne‐azide cycloaddition. Our results showed that the recombinant lectin‐peptide fusion R‐DIM‐P‐LF11‐215‐Stx1B killed >99% Gb3+ HeLa cells while Gb3‐negative cells were unaffected. The peptides R‐DIM‐P‐LF11‐215 and R‐DIM‐P‐LF11‐215 20AzK were produced recombinantly in E. coli in satisfactory amounts and were tested functional by cytotoxicity and cell‐binding assays, respectively. [ABSTRACT FROM AUTHOR]

Abstract Background Breast cancer is the world’s most prevalent cancer among women. Microorganisms have been the richest source of antibiotics as well as anticancer drugs. Moricin peptides have shown antibacterial properties; however, the anticancer potential and mechanistic insights into moricin peptide-induced cancer cell death have not yet been explored. Methods An investigation through in silico analysis, analytical methods (Reverse Phase-High Performance Liquid Chromatography (RP-HPLC), mass spectroscopy (MS), circular dichroism (CD), and in vitro studies, has been carried out to delineate the mechanism(s) of moricin-induced cancer cell death. An in-silico analysis was performed to predict the anticancer potential of moricin in cancer cells using Anti CP and ACP servers based on a support vector machine (SVM). Molecular docking was performed to predict the binding interaction between moricin and peptide-related cancer signaling pathway(s) through the HawkDOCK web server. Further, in vitro anticancer activity of moricin was performed against MDA-MB-231 cells. Results In silico observation revealed that moricin is a potential anticancer peptide, and protein–protein docking showed a strong binding interaction between moricin and signaling proteins. CD showed a predominant helical structure of moricin, and the MS result determined the observed molecular weight of moricin is 4544 Da. An in vitro study showed that moricin exposure to MDA-MB-231 cells caused dose dependent inhibition of cell viability with a high generation of reactive oxygen species (ROS). Molecular study revealed that moricin exposure caused downregulation in the expression of Notch-1, NF-ƙB and Bcl2 proteins while upregulating p53, Bax, caspase 3, and caspase 9, which results in caspase-dependent cell death in MDA-MB-231 cells. Conclusions In conclusion, this study reveals the anticancer potential and underlying mechanism of moricin peptide-induced cell death in triple negative cancer cells, which could be used in the development of an anticancer drug.

Abstract Background The androgen receptor (AR) has been studied as an approach to cancer therapy. Aims We used human breast cancer‐derived cells with high, low, and very low expression levels of AR, in addition to prostate cancer‐derived LNCaP and DU‐145 cells as a positive and negative controls to examine apoptosis caused by a synthetic peptide that targets ARs. Methods and Results The peptide was produced to inhibit AR transactivation in breast cancer cell lines. We then measured cell viability, caspase‐3 activity, and the ratio of Bax/Bcl‐2. The findings indicated that the peptide (100–500 nM) in the presence of dihydrotestosterone (DHT) reduced cell growth in cells with high and low expression level of AR (p

The discovery of marine-derived compounds bloomed in recent years. Bioactive compounds can be extracted from marine creatures like seahorses, mollusks, aquatic plants, algae, or fishes. Numerous of these discovered compounds were proven with various therapeutic values, including antibacterial, antihypertensive, antioxidant, and anticancer properties. Pardaxin is one of the recently discovered bioactive peptides with dual antibacterial and anticancer effects. Pardaxin is a 33-amino acid peptide that is secreted by the Red Sea Moses, Pardachirus marmoratus, solely for defensive purposes. This peptide was determined as a cationic peptide that held an alpha-helical structure, and it is believed that these two characteristics contributed to the anticancer potential of pardaxin. Therefore, this article reviews the anticancer properties of pardaxin in terms of its cancer-killing mechanisms such as apoptosis, cell cycle arrest, and cell lysis. Besides, we also reviewed other potent therapeutic applications of pardaxin, including the use of pardaxin as an immunotherapy agent, drug carrier, and in cancer vaccines. This review aims to encourage the use of pardaxin as an anticancer agent in the medicinal field. [ABSTRACT FROM AUTHOR]

In the development and study of antimicrobial peptides (AMPs), researchers have kept a watchful eye on peptides from the brevinin family because of their extensive antimicrobial activities and anticancer potency. In this study, a novel brevinin peptide was isolated from the skin secretions of the Wuyi torrent frog, Amolops wuyiensis (A. wuyiensisi), named B1AW (FLPLLAGLAANFLPQIICKIARKC). B1AW displayed anti-bacterial activity against Gram-positive bacteria Staphylococcus aureus (S. aureus), methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis (E. faecalis). B1AW-K was designed to broaden the antimicrobial spectrum of B1AW. The introduction of a lysine residue generated an AMP with enhanced broad-spectrum antibacterial activity. It also displayed the ability to inhibit the growth of human prostatic cancer PC-3, non-small lung cancer H838, and glioblastoma cancer U251MG cell lines. In molecular dynamic (MD) simulations, B1AW-K had a faster approach and adsorption to the anionic membrane than B1AW. Therefore, B1AW-K was considered a drug prototype with a dual effect, which deserves further clinical investigation and validation.

Abstract Background Anticancer peptide (ACP) inhibits and kills tumor cells. Research on ACP is of great significance for the development of new drugs, and the prediction of ACPs and non-ACPs is the new hotspot. Results We propose a new machine learning-based method named GCNCPR-ACPs (a Graph Convolutional Neural Network Method based on collapse pooling and residual network to predict the ACPs), which automatically and accurately predicts ACPs using residual graph convolution networks, differentiable graph pooling, and features extracted using peptide sequence information extraction. The GCNCPR-ACPs method can effectively capture different levels of node attributes for amino acid node representation learning, GCNCPR-ACPs uses node2vec and one-hot embedding methods to extract initial amino acid features for ACP prediction. Conclusions Experimental results of ten-fold cross-validation and independent validation based on different metrics showed that GCNCPR-ACPs significantly outperformed state-of-the-art methods. Specifically, the evaluation indicators of Matthews Correlation Coefficient (MCC) and AUC of our predicator were 69.5% and 90%, respectively, which were 4.3% and 2% higher than those of the other predictors, respectively, in ten-fold cross-validation. And in the independent test, the scores of MCC and SP were 69.6% and 93.9%, respectively, which were 37.6% and 5.5% higher than those of the other predictors, respectively. The overall results showed that the GCNCPR-ACPs method proposed in the current paper can effectively predict ACPs.

Conventional cancer therapies cause severe side effects. Peptide-based therapy as a complementary option in cancer treatment reports promising results due to its multifunctionality, selectivity, and safety. The aim of this study was to describe the multiple roles peptides have acquired in cancer therapy, the engineering approaches utilized to enhance peptide efficacy, and to evaluate the status and trends of publications related to anticancer peptides. Information related to the roles of peptides in anticancer therapy and to the engineering strategies to improve their efficacy was curated. The digital libraries PubMed, ScienceDirect, and SpringrLink, were consulted for the bibliometric analysis. Anticancer peptides may act through multiple pathways to eliminate tumor cells. Engineering strategies have been successfully implemented to overcome the low bioavailability of peptides, allowing them to improve their efficacy. The bibliometric analysis allowed the visualization of the importance anticancer peptides have gained in cancer therapy in the past two decades. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) represent a promising new therapeutic approach in cancer treatment. They can target cancer cells without affecting healthy tissues or altering normal physiological functions. Machine learning algorithms have increasingly been utilized for predicting peptide sequences with potential ACP effects. This study analyzed four benchmark datasets based on a well-established random forest (RF) algorithm. The peptide sequences were converted into 566 physicochemical features extracted from the amino acid index (AAindex) library, which were then subjected to feature selection using four methods: light gradient-boosting machine (LGBM), analysis of variance (ANOVA), chi-squared test (Chi2), and mutual information (MI). Presenting and merging the identified features using Venn diagrams, 19 key amino acid physicochemical properties were identified that can be used to predict the likelihood of a peptide sequence functioning as an ACP. The results were quantified by performance evaluation metrics to determine the accuracy of predictions. This study aims to enhance the efficiency of designing peptide sequences for cancer treatment. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the world's most prevalent cancer among women. Microorganisms have been the richest source of antibiotics as well as anticancer drugs. Moricin peptides have shown antibacterial properties; however, the anticancer potential and mechanistic insights into moricin peptide-induced cancer cell death have not yet been explored. Methods: An investigation through in silico analysis, analytical methods (Reverse Phase-High Performance Liquid Chromatography (RP-HPLC), mass spectroscopy (MS), circular dichroism (CD), and in vitro studies, has been carried out to delineate the mechanism(s) of moricin-induced cancer cell death. An in-silico analysis was performed to predict the anticancer potential of moricin in cancer cells using Anti CP and ACP servers based on a support vector machine (SVM). Molecular docking was performed to predict the binding interaction between moricin and peptide-related cancer signaling pathway(s) through the HawkDOCK web server. Further, in vitro anticancer activity of moricin was performed against MDA-MB-231 cells. Results: In silico observation revealed that moricin is a potential anticancer peptide, and protein–protein docking showed a strong binding interaction between moricin and signaling proteins. CD showed a predominant helical structure of moricin, and the MS result determined the observed molecular weight of moricin is 4544 Da. An in vitro study showed that moricin exposure to MDA-MB-231 cells caused dose dependent inhibition of cell viability with a high generation of reactive oxygen species (ROS). Molecular study revealed that moricin exposure caused downregulation in the expression of Notch-1, NF-ƙB and Bcl2 proteins while upregulating p53, Bax, caspase 3, and caspase 9, which results in caspase-dependent cell death in MDA-MB-231 cells. Conclusions: In conclusion, this study reveals the anticancer potential and underlying mechanism of moricin peptide-induced cell death in triple negative cancer cells, which could be used in the development of an anticancer drug. [ABSTRACT FROM AUTHOR]

Antibody‐directed drugs for targeted cancer treatment have become a hot topic in new anticancer drug development; however, antibody‐fused therapeutic peptides were rarely documented. Herein, we designed a fusion protein with a cetuximab‐derived single‐chain variable fragment targeting epidermal growth factor receptor (anti‐EGFR scFv) and the anticancer lytic peptide (ACLP) ZXR2, connected by a linker (G4S)3 and MMP2 cleavage site. The anti‐EGFR scFv‐ZXR2 recombinant protein showed specific anticancer activity on EGFR‐overexpressed cancer cell lines in a concentration‐ and time‐dependent manner, as it can bind to EGFR on cancer cell surfaces. This fusion protein caused cell membrane lysis as ZXR2, and showed improved stability in serum compared with ZXR2. These results suggest that scFv‐ACLP fusion proteins may be potential anticancer drug candidates for targeted cancer treatment, which also provide a feasible idea for targeted drug design. [ABSTRACT FROM AUTHOR]

Anticancer peptides and polymers represent an emerging field of tumor treatment and can physically interact with tumor cells to address the problem of multidrug resistance. In the present study, poly(l-ornithine)-b-poly(l-phenylalanine) (PLO-b-PLF) block copolypeptides were prepared and evaluated as macromolecular anticancer agents. Amphiphilic PLO-b-PLF self-assembles into nanosized polymeric micelles in aqueous solution. Cationic PLO-b-PLF micelles interact steadily with the negatively charged surfaces of cancer cells via electrostatic interactions and kill the cancer cells via membrane lysis. To alleviate the cytotoxicity of PLO-b-PLF, 1,2-dicarboxylic-cyclohexene anhydride (DCA) was anchored to the side chains of PLO via an acid-labile β-amide bond to fabricate PLO(DCA)-b-PLF. Anionic PLO(DCA)-b-PLF showed negligible hemolysis and cytotoxicity under neutral physiological conditions but recovered cytotoxicity (anticancer activity) upon charge reversal in the weakly acidic microenvironment of the tumor. PLO-based polypeptides might have potential applications in the emerging field of drug-free tumor treatment. [ABSTRACT FROM AUTHOR]

Objective: Cancer is a leading cause of death worldwide, requires development of new effective, specific, and safe strategies that do not carry the disadvantages of traditional cancer treatment approaches. Hence, this study aimed to identify anticancer peptide candidates in fermented food microbiomes through an in silico investigation. Materials and Methods: One hundred eight shotgun metagenomic sequencing samples from six studies on fermented food microbiomes were downloaded from the NCBI and ENA databases and included in the study. Bioinformatic analyses including quality control of raw data, de novo assembly, prediction of protein sequences, anticancer peptide predictions by an integrated use of four different prediction tools, toxicity predictions and database comparisons were performed. Results: One hundred forty-two novel anticancer peptide candidates were identified. Liquor, coffee, kefir fermentation samples contained the greatest numbers of anticancer peptide candidates while sugar, dairy, coconut kefir and brine-type fermentations were dominant sources according to the substrate type. Conclusion: This study indicates the potential of fermented food microbiomes as a useful source for candidate anticancer peptide detection. In vitro and in vivo validations of detected peptides may lead to development of new candidate molecules for cancer therapy in the future. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments. [ABSTRACT FROM AUTHOR]

Abstract Microbial infection and cancer are two leading causes of global mortality. Discovering and developing new therapeutics with better specificity having minimal side-effects and no drug resistance are of an immense need. In this regard, cationic antimicrobial peptides (AMP) with dual antimicrobial and anticancer activities are the ultimate choice. For better efficacy and improved stability, the AMPs available for treatment still required to be modified. There are several strategies in which AMPs can be enhanced through, for instance, nano-carrier application with high selectivity and specificity enables researchers to estimate the rate of drug delivery to a particular tissue. In this review we present the biology and modes of action of AMPs for both anticancer and antimicrobial activities as well as some modification strategies to improve the efficacy and selectivity of these AMPs. Graphical Abstract

Pancreatic ductal adenocarcinoma remains a highly debilitating condition with no effective disease-modifying interventions. In our search for natural products with promising anticancer activity, we identified the aminolipopeptide trichoderin A as a potential candidate. While it was initially isolated as an antitubercular peptide, we provide evidence that it is also selectively toxic against BxPC-3 and PANC-1 human pancreatic ductal adenocarcinoma cells cultured under glucose deprivation. This has critical implications for the pancreatic ductal adenocarcinoma, which is characterized by nutrient deprivation due to its hypovascularized network. We have also successfully simplified the trichoderin A peptide backbone, allowing greater accessibility to the peptide for further biological testing. In addition, we also conducted a preliminary investigation into the role of peptide lipidation at the N-terminus. This showed that analogues with longer fatty acyl chains exhibited superior cytotoxicity than those with shorter acyl chains. Further structural optimization of trichoderin A is anticipated to improve its biological activity, whilst ongoing mechanistic studies to elucidate its intracellular mechanism of action are conducted in parallel. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/. [ABSTRACT FROM AUTHOR]

The betanodavirus B2 protein targets mitochondria and triggers mitochondrion-mediated cell death signaling in lung cancer cells; however, its molecular mechanism remains unknown. In this study, we observed that B2 triggers hydrogen peroxide/Nrf2-involved stress signals in the dynamic regulation of non-small lung cancer cell (NSCLC)-programmed cell death. Here, the B2 protein works as a necrotic inducer that triggers lung cancer death via p53 upregulation and RIP3 expression, suggesting a new perspective on lung cancer therapy. We employed the B2 protein to target A549 lung cancer cells and solid tumors in NOD/SCID mice. Tumors were collected and processed for the hematoxylin and eosin staining of tissue and cell sections, and their sera were used for blood biochemistry analysis. We observed that B2 killed an A549 cell-induced solid tumor in NOD/SCID mice; however, the mutant ΔB2 did not. In NOD/SCID mice, B2 (but not ΔB2) induced both p53/Bax-mediated apoptosis and RIPK3-mediated necroptosis. Finally, immunochemistry analysis showed hydrogen peroxide /p38/Nrf2 stress strongly inhibited the production of tumor markers CD133, Thy1, and napsin, which correlate with migration and invasion in cancer cells. This B2-triggered, ROS/Nrf2-mediated stress signal triggered multiple signals via pathways that killed A549 lung cancer tumor cells in vivo. Our results provide novel insight into lung cancer management and drug therapy. [ABSTRACT FROM AUTHOR]

Background: Anticancer peptide (ACP) inhibits and kills tumor cells. Research on ACP is of great significance for the development of new drugs, and the prediction of ACPs and non-ACPs is the new hotspot. Results: We propose a new machine learning-based method named GCNCPR-ACPs (a Graph Convolutional Neural Network Method based on collapse pooling and residual network to predict the ACPs), which automatically and accurately predicts ACPs using residual graph convolution networks, differentiable graph pooling, and features extracted using peptide sequence information extraction. The GCNCPR-ACPs method can effectively capture different levels of node attributes for amino acid node representation learning, GCNCPR-ACPs uses node2vec and one-hot embedding methods to extract initial amino acid features for ACP prediction. Conclusions: Experimental results of ten-fold cross-validation and independent validation based on different metrics showed that GCNCPR-ACPs significantly outperformed state-of-the-art methods. Specifically, the evaluation indicators of Matthews Correlation Coefficient (MCC) and AUC of our predicator were 69.5% and 90%, respectively, which were 4.3% and 2% higher than those of the other predictors, respectively, in ten-fold cross-validation. And in the independent test, the scores of MCC and SP were 69.6% and 93.9%, respectively, which were 37.6% and 5.5% higher than those of the other predictors, respectively. The overall results showed that the GCNCPR-ACPs method proposed in the current paper can effectively predict ACPs. [ABSTRACT FROM AUTHOR]

It was the aim of this study to develop Cyclosaplin analogues and assess the anticancer effects of those peptide analogues on both MDA-MB-231 and K562 cell lines. The analogues of Cyclosaplin peptide (Cyclosaplin-2A and Cyclosaplin-7G) were designed and then investigated by online web server predictor AntiCP. The peptide analogues were applied to MDA-MB-231 and K562 cells in various concentrations and for various periods of time. The anticancer potential was confirmed by the MTT assay. Haemolytic activity also was assessed. In order to investigate the apoptotic effects of peptides on cancer cells, different tests such as morphological examination, Giemsa test, and DNA fragmentation were performed. Lactate dehydrogenase leakage assay was used to reject peptide-induced necrosis. As a result of computational studies, we discovered that the analogues of peptides also have anticancer properties. However, we have found through our practical research that analogues had less anticancer properties than their parent peptides. The MTT assay and morphological study confirmed the anticancer effects. For MD-AMB-231 cells, an IC50 of Cyclosaplin-2A was 70 µg/mL, and Cyclosaplin-7G was 90 µg/mL. In addition, for K562 cells, an IC50 of Cyclosaplin-2A was 10 µg/mL, and Cyclosaplin-7G was 15 µg/mL. Other tests also confirmed the anticancer effect of the peptide analogues. According to haemolytic assays, none of the peptide analogues possessed any haemolytic activity against human erythrocytes, indicating that the compounds are non-toxic to normal cells. There was evidence that peptide analogues, particularly Cyclosaplin-2A, had anticancer properties against cells derived from breast (MDA-MB-231) and blood (K562) cancers. [ABSTRACT FROM AUTHOR]

Complex pathological diseases, such as cancer, infection, and Alzheimer's, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer's. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates' clinical-translation chance. [ABSTRACT FROM AUTHOR]

Developing novel anti‑angiogenic agents with minimal toxicity is notably challenging for cancer therapeutics. The discovery and development of peptides, whether derived from natural sources or synthesized, has potential for developing anti‑angiogenic agents characterized by their ability to penetrate cancer cells, high specificity and low toxicity. The present study identified a Bryopsis plumose ‑derived anticancer and anti‑angiogenesis marine‑derived peptide 06 (MP06). A 22‑amino acid peptide was synthesized and conjugated with fluorescein isothiocyanate (FITC‑MP06) for intracellular localization in H1299 non‑small cell lung cancer cells. Regulatory effects of this peptide on the viability, migration and self‑renewal of lung cancer cells was assessed. Furthermore, anti‑angiogenic effect of MP06 was investigated by monitoring vascular tube formation in human umbilical vein endothelial cells and a zebrafish model. Aquaporin (AQP)3, a membrane channel in various tissues, is involved in regulating stemness, epithelial‑mesenchymal transition (EMT) and angiogenesis. MP06 downregulated AQP3 expression. Consistently, AQP3 knockdown by RNA silencing downregulated its gene expression, leading to a decrease in stemness, EMT and angiogenesis properties in H1299 cells. MP06 could thus serve as a novel therapeutic target with anticancer and angiogenesis properties for non‑small cell lung cancer.

The discovery of new highly effective anticancer drugs with few side effects is a challenge for drug development research. Natural or synthetic anticancer peptides (ACPs) represent a new generation of anticancer agents with high selectivity and specificity. The rapid emergence of chemoradiation-resistant lung cancer has necessitated the discovery of novel anticancer agents as alternatives to conventional therapeutics. In this study, we synthesized a peptide containing 22 amino acids and characterized it as a novel ACP (MP06) derived from green sea algae, Bryopsis plumosa. Using the ACP database, MP06 was predicted to possess an alpha-helical secondary structure and functionality. The anti-proliferative and apoptotic effects of the MP06, determined using the cytotoxicity assay and Annexin V/propidium iodide staining kit, were significantly higher in non-small-cell lung cancer (NSCLC) cells than in non-cancerous lung cells. We confirmed that MP06 suppressed cellular migration and invasion and inhibited the expression of N-cadherin and vimentin, the markers of epithelial–mesenchymal transition. Moreover, MP06 effectively reduced the metastasis of tumor xenografts in zebrafish embryos. In conclusion, we suggest considering MP06 as a novel candidate for the development of new anticancer drugs functioning via the ERK signaling pathway.

Improving the cell selectivity of anticancer peptides (ACPs) is a major hurdle in their clinical utilisation. In this study, a new acid-activated ACP was designed by conjugating a cationic ACP LK to its anionic binding partner peptide (LEH) via a disulphide linker to trigger antitumor activity at acidic pH while masking its killing activity at normal pH. Three anionic binding peptides containing different numbers of glutamic acid (Glu) and histidine were engineered to obtain an efficient acid-activated ACP. The conjugates LK-LEH2 and LK-LEH3 exhibited 6.1- and 8.0-fold higher killing activity at pH 6.0 relative to at pH 7.4, respectively, suggesting their excellent pH-dependent antitumor activity; and their cytotoxicity was 10-fold lower than that of LK. However, LK-LEH4 had no pH-responsive killing effect. Interestingly, increasing the number of Glu from 2 to 4 increased the pH-response of the physical mixture of LK and LEH; conversely, they weakly decreased the cytotoxicity of LK, suggesting that the conjugate connection is required to achieve excellent pH dependence while maintaining minimum toxicity. LK-LEH2 and LK-LEH3 were more enzymatically stable than LK, indicating their potential for in vivo application. Our work provided a basis for designing promising ACPs with good selectivity and low toxicity. [ABSTRACT FROM AUTHOR]

Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc [ABSTRACT FROM AUTHOR]

The development of drug delivery systems with high drug-loading efficiency, kinetic stability against dilution, as well as enhanced anticancer activity is of crucial importance to the fields of self-assembly and nanomedicine. Herein, we propose a strategy where the anticancer peptide acts as water-soluble monomer to directly participate in emulsion interfacial polymerization for fabricating polypeptide nanospheres. The constructed polypeptide nanospheres hold a high drug loading efficiency of 77%, and can be stably dispersed in highly diluted aqueous solutions. The acid-labile amide linkage in polypeptide nanospheres can be hydrolyzed in tumor acidic environments, thus releasing anticancer peptides selectively. The polypeptide nanospheres achieve significantly enhanced anticancer activity against HCT116 cells in vitro and in vivo through improved mitochondrial and membrane disruption. In addition, its side effects on normal cells can be reduced significantly. It is highly anticipated that more kinds of anticancer drug candidates or anticancer drugs can be applied to fabricate polymeric nanomedicines with improved anticancer activity through this strategy. [ABSTRACT FROM AUTHOR]

We conduct molecular dynamics simulations of model heterogeneous membranes and their interactions with a 24-amino acid peptide—NAF-144–67. NAF-144–67 is an anticancer peptide that selectively permeates and kills malignant cells; it does not permeate normal cells. We examine three membranes with different binary mixtures of lipids, DOPC–DOPA, DOPC–DOPS, and DOPC–DOPE, with a single peptide embedded in each as models for the diversity of biological membranes. We illustrate that the peptide organization in the membrane depends on the types of nearby phospholipids and is influenced by the charge and size of the head groups. The present study sheds light on early events of permeation and the mechanisms by which an amphiphilic peptide crosses from an aqueous solution to a hydrophobic membrane. Understanding the translocation mechanism is likely to help the design of new permeants. [ABSTRACT FROM AUTHOR]

LTX-315 is a clinical-stage, anticancer peptide therapeutic that disrupts cancer cell membranes. Existing mechanistic knowledge about LTX-315 has been obtained from cell-based biological assays, and there is an outstanding need to directly characterize the corresponding membrane-peptide interactions from a biophysical perspective. Herein, we investigated the membrane-disruptive properties of the LTX-315 peptide using three cell-membrane-mimicking membrane platforms on solid supports, namely the supported lipid bilayer, intact vesicle adlayer, and tethered lipid bilayer, in combination with quartz crystal microbalance-dissipation (QCM-D) and electrochemical impedance spectroscopy (EIS) measurements. The results showed that the cationic LTX-315 peptide selectively disrupted negatively charged phospholipid membranes to a greater extent than zwitterionic or positively charged phospholipid membranes, whereby electrostatic interactions were the main factor to influence peptide attachment and membrane curvature was a secondary factor. Of note, the EIS measurements showed that the LTX-315 peptide extensively and irreversibly permeabilized negatively charged, tethered lipid bilayers that contained high phosphatidylserine lipid levels representative of the outer leaflet of cancer cell membranes, while circular dichroism (CD) spectroscopy experiments indicated that the LTX-315 peptide was structureless and the corresponding membrane-disruptive interactions did not involve peptide conformational changes. Dynamic light scattering (DLS) measurements further verified that the LTX-315 peptide selectively caused irreversible disruption of negatively charged lipid vesicles. Together, our findings demonstrate that the LTX-315 peptide preferentially disrupts negatively charged phospholipid membranes in an irreversible manner, which reinforces its potential as an emerging cancer immunotherapy and offers a biophysical framework to guide future peptide engineering efforts. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are essential components of the mucosal barrier of the female reproductive tract (FRT) and are involved in many important physiological processes, including shaping the microbiota and maintaining normal reproduction and pregnancy. Gynecological cancers seriously threaten women's health and bring a heavy burden to society so that new strategies are needed to deal with these diseases. Recent studies have suggested that AMPs also have a complex yet intriguing relationship with gynecological cancers. The expression level of AMPs changes during tumor progression and they may act as promising biomarkers in cancer detection and prognosis prediction. Although AMPs have long been considered as host protective, they actually play a "double-edged sword" role in gynecological cancers, either tumorigenic or antitumor, depending on factors such as AMP and cancer types, as well as AMP concentrations. Moreover, AMPs are associated with chemoresistance and regulation of AMPs' expression may alter sensitivity of cancer cells to chemotherapy. However, more work is needed, especially on the identification of molecular mechanisms of AMPs in the FRT, as well as the clinical application of these AMPs in detection, diagnosis and treatment of gynecological malignancies. [ABSTRACT FROM AUTHOR]

Abstract Background Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides. Results The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively. Conclusions CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.