Contrastive learning for enhancing feature extraction in anticancer peptides.

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

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

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/.

ACP_MS: prediction of anticancer peptides based on feature extraction.

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.

Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells.

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.

Antimicrobial peptide moricin induces ROS mediated caspase-dependent apoptosis in human triple-negative breast cancer via suppression of notch pathway.

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.

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

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

Design, synthesis and anticancer evaluation of novel oncolytic peptide-chlorambucil conjugates.

Nitrogen mustards (NMs) are an important class of chemotherapeutic drugs and have been widely employed for the treatment of various cancers. However, due to the high reactivity of nitrogen mustard, most NMs react with proteins and phospholipids within the cell membrane. Therefore, only a very small fraction of NMs can reach the reach nucleus, alkylating and cross-linking DNA. To efficiently penetrate the cell membrane barrier, the hybridization of NMs with a membranolytic agent may be an effective strategy. Herein, the chlorambucil (CLB, a kind of NM) hybrids were first designed by conjugation with membranolytic peptide LTX-315. However, although LTX-315 could help large amounts of CLB penetrate the cytomembrane and enter the cytoplasm, CLB still did not readily reach the nucleus. Our previous work demonstrated that the hybrid peptide NTP-385 obtained by covalent conjugation of rhodamine B with LTX-315 could accumulate in the nucleus. Hence, the NTP-385-CLB conjugate, named FXY-3, was then designed and systematically evaluated both in vitro and in vivo. FXY-3 displayed prominent localization in the cancer cell nucleus and induced severe DNA double-strand breaks (DSBs) to trigger cell apoptosis. Especially, compared with CLB and LTX-315, FXY-3 exhibited significantly increased in vitro cytotoxicity against a panel of cancer cell lines. Moreover, FXY-3 showed superior in vivo anticancer efficiency in the mouse cancer model. Collectively, this study established an effective strategy to increase the anticancer activity and the nuclear accumulation of NMs, which will provide a valuable reference for future nucleus-targeting modification of nitrogen mustards.

LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets.

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.

A Novel Anticancer Peptide Derived from Bryopsis plumosa Regulates Proliferation and Invasion in Non-Small Cell Lung Cancer Cells.

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.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.

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.

Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.

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.

GCNCPR-ACPs: a novel graph convolution network method for ACPs prediction.

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.

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides.

Fast and accurate identification of the peptides with anticancer activity potential from large-scale proteins is currently a challenging task. In this study, we propose a new machine learning predictor, namely, ACPred-Fuse, that can automatically and accurately predict protein sequences with or without anticancer activity in peptide form. Specifically, we establish a feature representation learning model that can explore class and probabilistic information embedded in anticancer peptides (ACPs) by integrating a total of 29 different sequence-based feature descriptors. In order to make full use of various multiview information, we further fused the class and probabilistic features with handcrafted sequential features and then optimized the representation ability of the multiview features, which are ultimately used as input for training our prediction model. By comparing the multiview features and existing feature descriptors, we demonstrate that the fused multiview features have more discriminative ability to capture the characteristics of ACPs. In addition, the information from different views is complementary for the performance improvement. Finally, our benchmarking comparison results showed that the proposed ACPred-Fuse is more precise and promising in the identification of ACPs than existing predictors. To facilitate the use of the proposed predictor, we built a web server, which is now freely available via http://server.malab.cn/ACPred-Fuse.

The dual interaction of antimicrobial peptides on bacteria and cancer cells; mechanism of action and therapeutic strategies of nanostructures.

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.

Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties.

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.

Roles of Antimicrobial Peptides in Gynecological Cancers.

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.

De Novo Design of AC-P19M, a Novel Anticancer Peptide with Apoptotic Effects on Lung Cancer Cells and Anti-Angiogenic Activity.

Despite the current developments in cancer therapeutics, efforts to excavate new anticancer agents continue rigorously due to obstacles, such as side effects and drug resistance. Anticancer peptides (ACPs) can be utilized to treat cancer because of their effectiveness on a variety of molecular targets, along with high selectivity and specificity for cancer cells. In the present study, a novel ACP was de novo designed using in silico methods, and its functionality and molecular mechanisms of action were explored. AC-P19M was discovered through functional prediction and sequence modification based on peptide sequences currently available in the database. The peptide exhibited anticancer activity against lung cancer cells, A549 and H460, by disrupting cellular membranes and inducing apoptosis while showing low toxicity towards normal and red blood cells. In addition, the peptide inhibited the migration and invasion of lung cancer cells and reversed epithelial-mesenchymal transition. Moreover, AC-P19M showed anti-angiogenic activity through the inhibition of vascular endothelial growth factor receptor 2 signaling. Our findings suggest that AC-P19M is a novel ACP that directly or indirectly targets cancer cells, demonstrating the potential development of an anticancer agent and providing insights into the discovery of functional substances based on an in silico approach.

Biophysical Characterization of LTX-315 Anticancer Peptide Interactions with Model Membrane Platforms: Effect of Membrane Surface Charge.

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.

CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.

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.

Inhibition of breast cancer xenografts in a mouse model and the induction of apoptosis in multiple breast cancer cell lines by lactoferricin B peptide.

Breast cancer has a diverse aetiology characterized by the heterogeneous expression of hormone receptors and signalling molecules, resulting in varied sensitivity to chemotherapy. The adverse side effects of chemotherapy coupled with the development of drug resistance have prompted the exploration of natural products to combat cancer. Lactoferricin B (LfcinB) is a natural peptide derived from bovine lactoferrin that exhibits anticancer properties. LfcinB was evaluated in vitro for its inhibitory effects on cell lines representing different categories of breast cancer and in vivo for its suppressive effects on tumour xenografts in NOD-SCID mice. The different breast cancer cell lines exhibited varied levels of sensitivity to apoptosis induced by LfcinB in the order of SKBR3>MDA-MB-231>MDA-MB-468>MCF7, while the normal breast epithelial cells MCF-10A were not sensitive to LfcinB. The peptide also inhibited the invasion of the MDA-MB-231 and MDA-MB-468 cell lines. In the mouse xenograft model, intratumoural injections of LfcinB significantly reduced tumour growth rate and tumour size, as depicted by live imaging of the mice using in vivo imaging systems (IVIS). Harvested tumour volume and weight were significantly reduced by LfcinB treatment. LfcinB, therefore, is a promising and safe candidate that can be considered for the treatment of breast cancer.