Search

BackgroundThe peptide VLL-28, identified in the sequence of an archaeal protein, the transcription factor Stf76 from Sulfolobus islandicus, was previously identified and characterized as an antimicrobial peptide, possessing a broad-spectrum antibacterial activity.MethodsThrough a combined approach of NMR and Circular Dichroism spectroscopy, Dynamic Light Scattering, confocal microscopy and cell viability assays, the interaction of VLL-28 with the membranes of both parental and malignant cell lines has been characterized and peptide mechanism of action has been studied.ResultsIt is here demonstrated that VLL-28 selectively exerts cytotoxic activity against murine and human tumor cells. By means of structural methodologies, VLL-28 interaction with the membranes has been proven and the binding residues have been identified. Confocal microscopy data show that VLL-28 is internalized only into tumor cells. Finally, it is shown that cell death is mainly caused by a time-dependent activation of apoptotic pathways.ConclusionsVLL-28, deriving from the archaeal kingdom, is here found to be endowed with selective cytotoxic activity towards both murine and human cancer cells and consequently can be classified as an ACP.General significanceVLL-28 represents the first ACP identified in an archaeal microorganism, exerting a trans-kingdom activity.

[Display omitted] • A deep learning-based dual-channel that integrates both local and global features is proposed for effective identification of ACPs. • Utilize ResCNN to capture local features and Attention to extract global features, then perform integration through Attention. • The t-SNE, PCA visualization and SHAP analysis demonstrated that the iACP-DFSRA is a competitive predictor for ACPs. • The proposed approach outperforms conventional methods across the majority of evaluation metrics. Anticancer peptides (ACPs) have been widely applied in the treatment of cancer owing to good safety, rational side effects, and high selectivity. However, the number of ACPs that have been experimentally validated is limited as identification of ACPs is extremely expensive. Hence, accurate and cost-effective identification methods for ACPs are urgently needed. In this work, we proposed a deep learning-based model, named iACP-DFSRA, for ACPs identification. Specifically, we adopted two kinds of sequence embedding technologies, ProtBert_BFD pre-training language model and handcrafted features to encode protein sequences. Then, the LightGBM was used for feature selection, and the selected features were input into ResCNN and Attention mechanism, respectively, to extract local and global features. Finally, the concatenate features were deeply fused by using the Attention mechanism to allow key features to be paid more attention to by the model and make predictions by fully connected layer. The results of 10-fold cross-validation demonstrated that the iACP-DFSRA model delivered improved results in most metrics with Sp of 94.15%, Sn of 95.32%, Acc of 94.74% and MCC of 89.48% compared to the latest AACFlow model. Indeed, the iACP-DFSRA model is the only model with Acc > 90% and MCC > 80% on this independent test dataset. Furthermore, we have further demonstrated the superiority of our model on additional datasets. In addition, t-SNE and SHAP interpretation analysis demonstrated that it is crucial to use two channels for feature extraction and use the Attention mechanism for deep fusion, which helps the iACP-DFSRA to predict ACPs more effectively. [ABSTRACT FROM AUTHOR]

In recent years, various research on cancer treatment has achieved significant progress. However, some of these treatments remain disputable because of the emergence and development of drug resistance, and the toxic side effects that were brought about by the lack of selectivity displayed by the treatments. Hence, there is considerable interest in a new class of anticancer molecules that is currently still under investigation termed the cationic antimicrobial peptides (AMPs). AMPs are a group of pervasive components of the innate immunity which can be found throughout all classes of life. The small innate peptides cover a broad spectrum of antibacterial activities due to their electrostatic interactions with the negatively charged bacterial membrane. Compared with normal cells, cancer cells have increased proportions of negatively charged molecules, including phosphatidylserine, glycoproteins, and glycolipids, on the outer plasma membrane. This provides an opportunity for exploiting the interaction between AMPs and negatively charged cell membranes in developing unconventional anticancer strategies. Some AMPs may also be categorized into a group of potential anticancer agents called cationic anticancer peptides (ACPs) due to their relative selectivity in cell membrane penetration and lysis, which is similar to their interaction with bacterial membranes. Several examples of ACPs that are used in tumor therapy for their ability in penetrating or lysing tumor cell membrane will be reviewed in this paper, along with a discussion on the recent advances and challenges in the application of ACPs.

This study demonstrates the anticancer potential of RF13 peptide derived from vacuolar protein sorting associated protein 26B (VSP26B), which was obtained from transcriptome data of freshwater teleost, striped murrel (Channa striatus). In total, 28 different peptide segments were randomly predicted from VSP26B and their anticancer property was predicted through mACPpred algorithm. Based on the scoring value, out of 28, 3 peptides namely SA11, RF13 and DI14 were shortlisted for the primary physicochemical property screening. Also, the shortlisted peptides were validated for anticancer protein receptors using molecular docking. Based on the overall bioinformatics analysis, RF13 (1RRGKGGRRVTMSF13) peptide was selected to further investigate the anticancer potential at the molecular biology lab. In vitro experiment showed that RF13 inhibit the proliferation of human laryngeal epithelial cell (HEp-2). Further, microscopic observations confirmed the influence of peptides on the cell morphology that showed apoptosis due to Hoechst 33342 staining, which altogether demonstrated the anticancer effect of the examined peptide, RF13. The molecular mechanism of the peptide’s anticancer property was analyzed over cell cycle analysis. It showed that the peptide arrested the SubG1 and G1 phase of Hep-2 cells in a dose-dependent manner. The qPCR assay exhibited the apoptotic genes, including caspase-3 and caspase-9 expression significantly (p < 0.05) in an unregulated manner. Based on the obtained results, we propose that RF13 is potential enough as an anticancer agent; however, further direction needs to be focused on the possible specific target mechanism and its therapeutic approach towards anticancer drug development. [ABSTRACT FROM AUTHOR]

Background: Modern lifestyles mean that people are more likely to suffer from some form of cancer. As anticancer peptides can effectively kill cancer cells and play an important role in fighting cancer, they have been a subject of increasing research interest. Methods: This study presents a useful tool to identify the anticancer peptides based on a multi-kernel CNN and attention model, called ACP-MCAM. This model can automatically learn adaptive embedding and the context sequence features of ACP. In addition, to obtain better interpretability and integrity, we visualized the model. Results: Benchmarking comparison shows that ACP-MCAM significantly outperforms several state-of-the-art models. Different encoding schemes have different impacts on the performance of the model. We also studied tmethod parameter optimization. Conclusion: The ACP-MCAM can integrate multi-kernel CNN and self-attention mechanism, which outperforms the previous model in identifying anticancer peptides. It is expected that the work will provide new research ideas for anticancer peptide prediction in the future. In addition, this work will promote the development of the interdisciplinary field of artificial intelligence and biomedicine. [ABSTRACT FROM AUTHOR]

HB43 (FAKLLAKLAKKLL) is a synthetic peptide active against cell lines derived from breast, colon, melanoma, lung, prostate, and cervical cancers. Despite its remarkable spectrum of activity, the mechanism of action at the molecular level has never been investigated, preventing further optimization of its selectivity. The alternation of charged and hydrophobic residues suggests amphipathicity, but the formation of alpha‐helical structure seems discouraged by its short length and the large number of positively charged residues. Using different biophysical and in silico approaches we show that HB43 is completely unstructured in solution but assumes alpha‐helical conformation in the presence of DPC micelles and liposomes exposing phosphatidylserine (PS) used as mimics of cancer cell membranes. Membrane permeabilization assays demonstrate that the interaction leads to the preferential destabilization of PS‐containing vesicles with respect to PC‐containing ones, here used as noncancerous cell mimics. ssNMR reveals that HB43 is able to fluidify the internal structure of cancer‐cell mimicking liposomes while MD simulations show its internalization in such bilayers. This is achieved by the formation of specific interactions between the lysine side chains and the carboxylate group of phosphatidylserine and/or the phosphate oxygen atoms of targeted phospholipids, which could catalyze the formation of the alpha helix required for internalization. With the aim of better understanding the peptide biocompatibility and the additional antibacterial activity, the interaction with noncancerous cell mimicking liposomes exposing phosphatidylcholine (PC) and bacterial mimicking bilayers exposing phosphatidylglycerol (PG) is also described. [ABSTRACT FROM AUTHOR]

Abstract Background Survivin and XIAP are two important members of the inhibitor of apoptosis protein family and have been considered as potential targets for cancer treatment due to their overexpression in large variety of cancers including colorectal cancer. It has been reported that survivin and XIAP can synergistically inhibit apoptosis by forming survivin-XIAP complex. In this study, we aimed to design a peptide that targets the survivin-XIAP complex and elucidate its anticancer mechanisms in colorectal cancer cells. Methods We designed and synthetized Sur-X, the peptide targeting survivin-XIAP complex. The anticancer effects of Sur-X were evaluated both in vitro and in vivo. The underlying molecular mechanisms were also investigated. Results Sur-X exhibited potent inhibitory effects on four colorectal cancer cell lines HCT116, HCT15, RKO and HT29, but not on human peritoneal mesothelial cell line HMrSV5. Mechanistically, Sur-X induced Caspase 9-dependent intrinsic apoptosis in colorectal cancer cells by disrupting the survivin-XIAP complex and subsequently destabilizing survivin and XIAP. Interestingly, we found that Sur-X can also promote necroptosis. It was demonstrated that Sur-X destroyed the interaction between XIAP and TAB1 in the XIAP-TAB1-TAK1 complex, leading to the instability of TAK1, an endogenous necroptosis inhibitor. Subsequently, the accelerated degradation of TAK1 attenuated its inhibition on necroptosis in colorectal cancer cells. Moreover, knockdown of TAK1 restored the sensitivity of TAB1-overexpressing colorectal cancer cells to Sur-X-induced necroptosis. The in vivo pro-apoptotic effect of Sur-X was confirmed by the enhanced TUNEL staining and the decreased expression of survivin and XIAP in tumor tissues from xenograft mouse models. In addition, extensive necrosis and weaker MLKL expression in xenografts provided evidence for the in vivo pro-necroptotic effect of Sur-X. Conclusions Peptide Sur-X exhibits strong pro-apoptotic and pro-necroptotic effects in colorectal cancer cells and has a high clinical translation potential in the treatment of colorectal cancer.

Background: Modern lifestyles mean that people are more likely to suffer from some form of cancer. As anticancer peptides can effectively kill cancer cells and play an important role in fighting cancer, they have been a subject of increasing research interest.Methods: This study presents a useful tool to identify the anticancer peptides based on a multi-kernel CNN and attention model, called ACP-MCAM. This model can automatically learn adaptive embedding and the context sequence features of ACP. In addition, to obtain better interpretability and integrity, we visualized the model.Results: Benchmarking comparison shows that ACP-MCAM significantly outperforms several state-of-the-art models. Different encoding schemes have different impacts on the performance of the model. We also studied tmethod parameter optimization.Conclusion: The ACP-MCAM can integrate multi-kernel CNN and self-attention mechanism, which outperforms the previous model in identifying anticancer peptides. It is expected that the work will provide new research ideas for anticancer peptide prediction in the future. In addition, this work will promote the development of the interdisciplinary field of artificial intelligence and biomedicine.

Nocardiotide A has been successfully synthesized using a combination of solid‐ and solution‐phase synthesis. Peptides 1 and 2 of nocardiotide A, with different C and N termini, were synthesized on 2‐chlorotrityl chloride resin. A combination of N,N,N′,N′‐tetramethyl‐O‐(1H‐benzotriazol‐1‐yl)uronium hexafluorophosphate and hydroxybenzotriazole (HBTU/HOBt) was employed as coupling reagent and the release of the linear peptides from the resin was undertaken using AcOH and trifluoroethanol (TFE) reagents resulting in linear hexapeptides with protected side chains in good yields (83.4 % and 76 %, respectively). Peptide 1, which has Ala as C terminus and Trp as N terminus, was the only peptide that was successfully cyclized without epimerization under a dilute condition (1.25×10−3 M) using HBTU (3 equiv.) and 1 % N,N‐diisopropylethylamine (DIPEA) in dichloromethane. A combination of a small residue at the C terminus and a larger residue at the N terminus was found to be effective for the cyclization. The protecting group of the crude cyclic product was then deprotected by 95 % TFA in water and the crude was then purified to give white solid product with 16 % overall yield. The peptide was then characterized by HR‐ToFMS, 1H‐ and 13C‐NMR spectroscopy. In order to ensure the absence of epimerization during cyclization, the D‐analogue of nocardiotide A was synthesized using similar synthetic protocol. The NMR data of D‐analogue was found to have a larger difference compared to the synthetic and natural product, showing there is no epimerization at C‐terminus during cyclization. [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.

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.

Investigation of the interaction between drugs and biomembrane models, as a strategy to study and eventually improve drug/substrate interactions, is a crucial factor in preliminary screening. Synthesized peptides represent a source of potential anticancer and theragnostic drugs. In this study, we investigated the interaction of a novel synthesized peptide, called RH-23, with a simplified dimyristoylphosphatidylcholine (DMPC) model of the cellular membrane. The interaction of RH-23 with DMPC, organized either in multilamellar vesicles (MLVs) and in Langmuir-Blodgett (LB) monolayers, was assessed using thermodynamic techniques, namely differential scanning calorimetry (DSC) and LB. The calorimetric evaluations showed that RH-23 inserted into MLVs, causing a stabilization of the phospholipid gel phase that increased with the molar fraction of RH-23. Interplay with LB monolayers revealed that RH-23 interacted with DMPC molecules. This work represents the first experimental thermodynamic study on the interaction between RH-23 and a simplified model of the lipid membrane, thus providing a basis for further evaluations of the effect of RH-23 on biological membranes and its therapeutic/diagnostic potential.

The p28 peptide derived from Pseudomonas aeruginosa azurin shows an anticancer activity after binding to p53 protein and is currently in Phase I of clinical trials. We have studied its structure in water and in a biomimetic media and show that the peptide is unstructured in water but when studied in a biomimetic medium assumes a structure very similar to the one observed in azurin, suggesting a high propensity of this peptide to maintain this secondary structure. Analysis of p28 sequences from different bacterial species indicates conservation of the secondary structure despite amino acid replacement in different positions, suggesting that others, similar peptides could be tested for binding to p53. [ABSTRACT FROM AUTHOR]

A self-contained nanocapsule for nonspecific cytotoxic anticancer drug delivery and release activated by an enzyme and an alternating magnetic field (AMF) is introduced. This specific prodrug-like drug delivery platform is based on an esterase, an oligomer-based separating barrier, and an ester-linked anticancer peptide melittin encapsulated together in the enlarged pore spaces of the mesoporous silica nanoparticles. A superparamagnetic iron oxide nanoparticle core embedded in the center acts as a nanoheater to stimulate a cascade drug release. Each pore space was designed as a reaction nanovial for the activation of the drug release when the solution inside the nanovial is heated. By employing a thermoresponsive separating barrier as a shield of the peptide drug as well as a separator between the ester-containing peptides and the esterase, the nonspecific cytotoxic effect of the drug and off-target drug release are avoided because the drugs remain inactive in the absence of AMF stimulation. When AMF heating actuates the removal of the separating barrier and exposes the peptide drugs to the enzymes, drug release can be activated. In vivo antitumor experiments further revealed that the nanocapsules exhibited excellent biocompatibility and high tumor-targeting/inhibiting efficiency. The selected esterase, which is in close proximity to the ester-containing peptide drug, efficiently cleaves the ester bonds, thereby causing a catalytic release of peptide drugs and intensified anticancer efficacy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Anticancer peptides (ACPs) have recently emerged as promising cancer therapeutics due to their selectivity and lower toxicity. However, the number of experimentally validated ACPs is limited, and identifying ACPs from large-scale sequence data is time-consuming and expensive. Therefore, it is critical to develop and improve upon existing computational models for identifying ACPs. In this study, a computational method named ACP_DA was proposed based on peptide residue composition and physiochemical properties information. To curtail overfitting and reduce computational costs, a sequential forward selection method was utilized to construct the optimal feature groups. Subsequently, the feature vectors were fed into light gradient boosting machine classifier for model construction. It was observed by an independent set test that ACP_DA achieved the highest Matthew's correlation coefficient of 0.63 and accuracy of 0.8129, displaying at least a 2% enhancement compared to state-of-the-art methods. The satisfactory results demonstrate the effectiveness of ACP_DA as a powerful tool for identifying ACPs, with the potential to significantly contribute to the development and optimization of promising therapies. The data and resource codes are available at https://github.com/Zlclab/ACP_DA. [ABSTRACT FROM AUTHOR]

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

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. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a typically violent sort of malignancy and a major source of morbidity and mortality worldwide. Most of the radiation as well as chemotherapeutic agents used for the treatment of PDAC exhibit strong adverse effects with low specificity. Henceforth, a need for an alternative demanded that prompted us to design a novel anticancer peptide (KOR-19) in such a way that it interacts predominantly with cancer cells but exhibits low toxicity with normal mammalian cells. Cell proliferation (XTT) assay and crystal violet assay revealed that the novel designed peptide exhibited vital toxic activities against four different PDAC cell lines (BxPC3, Colo-357, Panc89, and Panc1). On the opposite, this peptide depicted negligible or very low toxicity towards a non-cancerous primary mouse pancreatic stellate cells (MPSC). In the LDH-release assay, we have detected potential membrane damaging properties of KOR-19 as its major mode of action. Further, the hemolytic assay revealed very low or negligible toxic activities towards both mouse- and human RBCs. The flow cytometric analysis demonstrated that the KOR-19 expanded both apoptotic and necrotic cell death in all PDAC cell lines in a dose-dependent manner. Morphological assessments also supported the notion of necrosis assassination of cancer cells as there were elevated swollen vesicle-like structure and cell aggregation noticed in a cell type-dependent manner. In summary, KOR-19 exhibits excellent "druggable" properties due to its promising oncolytic and growth inhibitory activities against PDAC cells, making it a promising agent for the future remedy choice for PDAC. [ABSTRACT FROM AUTHOR]

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.

Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features 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. [ABSTRACT FROM AUTHOR]

Cancer has now become a common disease affecting human health. Existing cancer treatment drugs can no longer meet the growing needs of cancer patients, and the emergence of anticancer drug resistance has exacerbated this phenomenon. By designing and synthesizing new anticancer peptide nanoparticles and studying their anticancer effects, new strategies for cancer treatment may be obtained. Novel anticancer peptides are synthesized by adding basic amino acids and solid-phase synthesis technology, and their structural information is determined by mass spectrometry. Nanoparticles of anticancer peptide were synthesized by nano-self-assembly technology. Two novel anticancer peptides exhibited anticancer activity, one of which was assembled into nanoparticles. The theoretical isoelectric points of the modified SZG3 and SZG5 are all greater than physiological pH, and will be positively charged under physiological conditions. The estimated half -life of SZG3 and SZG5 is significantly extended (30h), which is beneficial to increase the efficacy and reduce toxic and side effects. SZG3 and SZG5 have a good inhibitory effect on tumor cells and have low toxicity to normal cells. [ABSTRACT FROM AUTHOR]

Background: Survivin and XIAP are two important members of the inhibitor of apoptosis protein family and have been considered as potential targets for cancer treatment due to their overexpression in large variety of cancers including colorectal cancer. It has been reported that survivin and XIAP can synergistically inhibit apoptosis by forming survivin-XIAP complex. In this study, we aimed to design a peptide that targets the survivin-XIAP complex and elucidate its anticancer mechanisms in colorectal cancer cells. Methods: We designed and synthetized Sur-X, the peptide targeting survivin-XIAP complex. The anticancer effects of Sur-X were evaluated both in vitro and in vivo. The underlying molecular mechanisms were also investigated. Results: Sur-X exhibited potent inhibitory effects on four colorectal cancer cell lines HCT116, HCT15, RKO and HT29, but not on human peritoneal mesothelial cell line HMrSV5. Mechanistically, Sur-X induced Caspase 9-dependent intrinsic apoptosis in colorectal cancer cells by disrupting the survivin-XIAP complex and subsequently destabilizing survivin and XIAP. Interestingly, we found that Sur-X can also promote necroptosis. It was demonstrated that Sur-X destroyed the interaction between XIAP and TAB1 in the XIAP-TAB1-TAK1 complex, leading to the instability of TAK1, an endogenous necroptosis inhibitor. Subsequently, the accelerated degradation of TAK1 attenuated its inhibition on necroptosis in colorectal cancer cells. Moreover, knockdown of TAK1 restored the sensitivity of TAB1-overexpressing colorectal cancer cells to Sur-X-induced necroptosis. The in vivo pro-apoptotic effect of Sur-X was confirmed by the enhanced TUNEL staining and the decreased expression of survivin and XIAP in tumor tissues from xenograft mouse models. In addition, extensive necrosis and weaker MLKL expression in xenografts provided evidence for the in vivo pro-necroptotic effect of Sur-X. Conclusions: Peptide Sur-X exhibits strong pro-apoptotic and pro-necroptotic effects in colorectal cancer cells and has a high clinical translation potential in the treatment of colorectal cancer. [ABSTRACT FROM AUTHOR]

Peptides have been thoroughly studied as new therapeutic strategies for cancer treatment. In this work, we explored in vitro the anticancer potential of three novel peptides derived from the C-terminal of azurin, an anticancer bacterial protein produced by Pseudomonas aeruginosa. CT-p26, CT-p19 and CT-p19LC peptides were previously obtained through an in silico peptide design optimization process, CT-p19LC being the most promising as it presented higher hydrophobicity and solubility, positive total charge and, most importantly, greater propensity for anticancer activity. Therefore, in this study, through proliferation and apoptosis assays, CT-p19LC was tested in four cancer cell lines—A549, MCF-7, HeLa and HT-29—and in two non-cancer cell lines—16HBE14o- and MCF10A. Its membrane-targeting activity was further evaluated with zeta potential measurements and membrane order was assessed with the Laurdan probe. The results obtained demonstrated that CT-p19LC decreases cell viability through induction of cell death and binds to the plasma membrane of cancer cells, but not to non-cancer cells, making them less rigid. Overall, this study reveals that CT-p19LC is an auspicious selective anticancer peptide able to react with cancer cell membranes and cause effective action.

Despite considerable progress, the treatment of acute leukemia continues to be a challenge for a significant majority of patients. Using a well-characterized preclinical mouse model of acute promyelocytic leukemia (APL), we evaluated here the antileukemic efficacy of RT53, an anticancer peptide with potential immunological properties. Our results indicate that RT53 possesses a direct antileukemic effect, even at a late stage. We also demonstrate that a single injection of a vaccine consisting of leukemic blasts exposed to RT53, which induces the hallmarks of immunogenic cell death, was highly effective in preventing leukemia development in both prophylactic and therapeutic settings. The vaccine comprising RT53-treated APL cells generated long-term antileukemic protection and depletion experiments indicated that CD4 + T cells were of crucial importance for vaccine efficacy. Combined, our results provide the rationale for the exploration of RT53-based therapies for the treatment of acute leukemia.

The physicochemical properties of antimicrobial peptides (AMPs) including size, net charge, amphipathic structure, hydrophobicity, and mode-of-action together determine their broad-spectrum activities against bacteria, fungi, protozoa, and viruses. Recent studies show that some AMPs have both antimicrobial and anticancer activities, suggesting a new strategy for cancer therapy. Hepatocellular carcinoma (HCC), the primary liver cancer, is a leading cause of cancer mortality worldwide, and lacks effective treatment. Anticancer peptides (ACPs) derived from AMPs or natural resources could be applied to combat HCC directly or as a synergistic treatment. However, the number of known ACPs is low compared to the number of antibacterial and antifungal peptides, and very few of them can be applied clinically for HCC treatment. In this review, we first summarize recent studies related to ACPs for HCC, followed by a description of potential modes-of-action including direct killing, anti-inflammation, immune modulation, and enhanced wound healing. We then describe the structures of AMPs and methods to design and modify these peptides to improve their anticancer efficacy. Finally, we explore the potential application of ACPs as vaccines or nanoparticles for HCC treatment. Overall, ACPs display several attractive properties as therapeutic agents, including broad-spectrum anticancer activity, ease-of-design and modification, and low production costs. As this is an emerging and novel area of cancer therapy, additional studies are needed to identify existing candidate AMPs with ACP activity, and assess their anticancer activity and specificity, and immunomodulatory effects, using in vitro, in silico, and in vivo approaches.

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., (© 2024 The Protein Society.)

Chemotherapy in childhood leukemia is associated with late morbidity in leukemic survivors, while certain patient subsets are relatively resistant to standard chemotherapy. It is therefore important to identify new agents with sensitivity and selectivity towards leukemic cells, while having less systemic toxicity. Peptide-based therapeutics has gained a great deal of attention during the last few years. Here, we used an integrative workflow combining mass spectrometric peptide library construction, in silico anticancer peptide screening, and in vitro leukemic cell studies to discover a novel anti-leukemic peptide having 3+ charges and an alpha helical structure, namely HMP-S7, from human breast milk. HMP-S7 showed cytotoxic activity against four distinct leukemic cell lines in a dose-dependent manner but had no effect on solid malignancies or representative normal cells. HMP-S7 induced leukemic cell death by penetrating the plasma membrane to enter the cytoplasm and cause the leakage of lactate dehydrogenase, thus acting in a membranolytic manner. Importantly, HMP-S7 exhibited anti-leukemic effects against patient-derived leukemic cells ex vivo. In conclusion, HMP-S7 is a selective anti-leukemic peptide with promise, which requires further validation in preclinical and clinical studies.

Despite considerable progress, the treatment of acute leukemia continues to be a challenge for a significant majority of patients. Using a well-characterized preclinical mouse model of acute promyelocytic leukemia (APL), we evaluated here the antileukemic efficacy of RT53, an anticancer peptide with potential immunological properties. Our results indicate that RT53 possesses a direct antileukemic effect, even at a late stage. We also demonstrate that a single injection of a vaccine consisting of leukemic blasts exposed to RT53, which induces the hallmarks of immunogenic cell death, was highly effective in preventing leukemia development in both prophylactic and therapeutic settings. The vaccine comprising RT53-treated APL cells generated long-term antileukemic protection and depletion experiments indicated that CD4 + T cells were of crucial importance for vaccine efficacy. Combined, our results provide the rationale for the exploration of RT53-based therapies for the treatment of acute leukemia. [ABSTRACT FROM AUTHOR]

The great therapeutic potential of peptides has not yet been achieved, mainly due to their remarkably short in vivo half-life. Although conjugation to macromolecules has been an effective way of improving protein in vivo half-life, the steric hindrance of macromolecules usually reduces the in vivo efficacy of peptides. Here we report a complex delivery system made from PsTag polypeptide, polyglutamic acid chain, matrix metalloproteinase 2 (MMP2)-degradable domain and cationic cell penetrating peptide for anticancer peptide delivery. Clear evidence was shown in vitro and in vivo to demonstrate that this multifunctional protein fusing a pro-apoptotic KLAKLAKKLAKLAK (KLA), named PAK, can increase circulation time in blood, enhance accumulation at tumor sites, eliminate the PsTag domain and the polyanionic sequence when triggered by tumor overexpressing MMP2, and then expose the cell penetrating peptide to realize the potent cellular uptake of KLA. Treatment of tumor-bearing mice with PAK could markedly induce tumor cells apoptosis and inhibit tumor growth, with no significant adverse effects. These results suggest our fusion protein can be a potential delivery system for peptide delivery in cancer treatments. [ABSTRACT FROM AUTHOR]

The physicochemical properties of antimicrobial peptides (AMPs) including size, net charge, amphipathic structure, hydrophobicity, and mode-of-action together determine their broad-spectrum activities against bacteria, fungi, protozoa, and viruses. Recent studies show that some AMPs have both antimicrobial and anticancer activities, suggesting a new strategy for cancer therapy. Hepatocellular carcinoma (HCC), the primary liver cancer, is a leading cause of cancer mortality worldwide, and lacks effective treatment. Anticancer peptides (ACPs) derived from AMPs or natural resources could be applied to combat HCC directly or as a synergistic treatment. However, the number of known ACPs is low compared to the number of antibacterial and antifungal peptides, and very few of them can be applied clinically for HCC treatment. In this review, we first summarize recent studies related to ACPs for HCC, followed by a description of potential modes-of-action including direct killing, anti-inflammation, immune modulation, and enhanced wound healing. We then describe the structures of AMPs and methods to design and modify these peptides to improve their anticancer efficacy. Finally, we explore the potential application of ACPs as vaccines or nanoparticles for HCC treatment. Overall, ACPs display several attractive properties as therapeutic agents, including broad-spectrum anticancer activity, ease-of-design and modification, and low production costs. As this is an emerging and novel area of cancer therapy, additional studies are needed to identify existing candidate AMPs with ACP activity, and assess their anticancer activity and specificity, and immunomodulatory effects, using in vitro , in silico , and in vivo approaches. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides present a broad spectrum of therapeutic applications, including their use as anticancer peptides. These peptides have as target microbial, normal, and cancerous cells. The oncological properties of these peptides may occur by membranolytic mechanisms or non-membranolytics. In this work, we demonstrate for the first time the cytotoxic effects of the cationic alpha-helical antimicrobial peptide LyeTx I-b on glioblastoma lineage U87-MG. The anticancer property of this peptide was associated with a membranolytic mechanism. Loss of membrane integrity occurred after incubation with the peptide for 15 min, as shown by trypan blue uptake, reduction of calcein-AM conversion, and LDH release. Morphological studies using scanning electron microscopy demonstrated disruption of the plasma membrane from cells treated with LyeTx I-b, including the formation of holes or pores. Transmission electron microscopy analyses showed swollen nuclei with mild DNA condensation, cell volume increase with an electron-lucent cytoplasm and organelle vacuolization, but without the rupture of nuclear or plasmatic membranes. Morphometric analyses revealed a high percentage of cells in necroptosis stages, followed by necrosis and apoptosis at lower levels. Necrostatin-1, a known inhibitor of necroptosis, partially protected the cells from the toxicity of the peptide in a concentration-dependent manner. Imaging flow cytometry confirmed that 59% of the cells underwent necroptosis after 3-h incubation with the peptide. It is noteworthy that LyeTx I-b showed only mild cytotoxicity against normal fibroblasts of human and monkey cell lines and low hemolytic activity in human erythrocytes. All data together point out the anticancer potential of this peptide. [ABSTRACT FROM AUTHOR]

Direct-acting anticancer (DAA) peptides are cytolytic peptides that show promise as novel anticancer agents. DAA peptides bind to anionic molecules that are abundant on cancer cells relative to normal healthy cells, which results in preferential killing of cancer cells. Due to the mechanism by which DAA peptides kill cancer cells, it was thought that resistance would be difficult to achieve. Here, we describe the generation and characterization of two MDA-MB-231 breast cancer cell-line variants with reduced susceptibility to pleurocidin-family and mastoparan DAA peptides. Peptide resistance correlated with deficiencies in peptide binding to cell-surface structures, suggesting that resistance was due to altered composition of the cell membrane. Peptide-resistant MDA-MB-231 cells were phenotypically distinct yet remained susceptible to chemotherapy. Surprisingly, neither of the peptide-resistant breast cancer cell lines was able to establish tumors in immune-deficient mice. Histological analysis and RNA sequencing suggested that tumorigenicity was impacted by alternations in angiogenesis and extracellular matrix composition in the peptide-resistant MDA-MB-231 variants. Collectively, these data further support the therapeutic potential of DAA peptides as adjunctive treatments for cancer.

Cancer is increasingly recognized as one of the primary causes of death and has become a multifaceted global health issue. Modern medical science has made significant advancements in the diagnosis and therapy of cancer over the past decade. The detrimental side effects, lack of efficacy, and multidrug resistance of conventional cancer therapies have created an urgent need for novel anticancer therapeutics or treatments with low cytotoxicity and drug resistance. The pharmaceutical groups have recognized the crucial role that peptide therapeutic agents can play in addressing unsatisfied healthcare demands and how these become great supplements or even preferable alternatives to biological therapies and small molecules. Anticancer peptides, as a vibrant therapeutic strategy against various cancer cells, have demonstrated incredible anticancer potential due to high specificity and selectivity, low toxicity, and the ability to target the surface of traditional "undruggable" proteins. This review will provide the research progression of anticancer peptides, mainly focusing on the discovery and modifications along with the optimization and application of these peptides in clinical practice., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Cancer is a worldwide issue. It has been observed that conventional therapies face many problems, such as side effects and drug resistance. Recent research reportedly used marine-derived products to treat various diseases and explored their potential in treating cancers., Objective: This study aims to discover short-length anticancer peptides derived from pardaxin 6 through an in silico approach., Methods: Fragmented peptides ranging from 5 to 15 amino acids were derived from the pardaxin 6 parental peptide. These peptides were further replaced with one residue and, along with the original fragmented peptides, were predicted for their SVM scores and physicochemical properties. The top 5 derivative peptides were further examined for their toxicity, hemolytic probability, peptide structures, docking models, and energy scores using various web servers. The trend of in silico analysis outputs across 5 to 15 amino acid fragments was further analyzed., Results: Results showed that when the amino acids were increased, SVM scores of the original fragmented peptides were also increased. Designed peptides had increased SVM scores, which was aligned with previous studies where the single residue replacement transformed the non-anticancer peptide into an anticancer agent. Moreover, in vitro studies validated that the designed peptides retained or enhanced anticancer effects against different cancer cell lines. Interestingly, a decreasing trend was observed in those fragmented derivative peptides., Conclusion: Single residue replacement in fragmented pardaxin 6 was found to produce stronger anticancer agents through in silico predictions. Through bioinformatics tools, fragmented peptides improved the efficiency of marine-derived drugs with higher efficacy and lower hemolytic effects in treating cancers., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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 N ε -((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., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Anticancer peptides (ACPs) are biologically anticancer active molecules that are produced by mammals, plants, insects and microorganisms. Here, a new peptide (TC22) with the amino acid sequence MTVVLLLIVLPLLGGVHSSGIL was identified and characterized from the beetle Tribolium castaneum. We found it inhibited the growth and viability of HeLa and MCF-7 cells. Flow cytometry analysis demonstrated the TC22 induced HeLa cell apoptosis, and activated caspase-9 and caspase-3. Furthermore, TC22 led to ROS generation, and triggered p53 transcription and expression. Taken together, our results indicated that TC22 exhibited high anticancer capacity via activating p53, inducing ROS generation and through a mitochondrial pathway. This research provided a novel natural source peptide with strong anticancer capacity. These findings provide some novel insights on the potential candidate reagent in cancer treatment. [ABSTRACT FROM AUTHOR]

Infectious diseases and cancers are leading causes of death and pose major challenges to public health. The human peptidome encompasses millions of compounds that display an enormous structural and functional diversity and represents an excellent source for the discovery of endogenous agents with antimicrobial and/or anticancer activity. Here, we discuss how to exploit the human peptidome for novel antimicrobial and anticancer agents through the generation of peptide libraries from human body fluids and tissues and stepwise purification of bioactive compounds. [ABSTRACT FROM AUTHOR]

To facilitate chlorambucil (CLB) to reach nucleus, a series of novel oncolytic peptide-chlorambucil (CLB) conjugates were rationally designed, efficiently synthesized and systematically evaluated both in vitro and in vivo. The NTP-385-CLB conjugate, named FXY-3, displayed prominent localization in the cancer cell nucleus, where it induced severe DNA double-strand breaks (DSBs) to trigger cell apoptosis. In particular, FXY-3 exhibited significantly increased in vitro cytotoxicity against a panel of cancer cell lines and superior in vivo anticancer efficiency in the mouse cancer model. [Display omitted] • Five oncolytic peptide-chlorambucil (CLB) conjugates were rationally designed and efficiently synthesized. • The in vivo and in vitro anticancer activity, and preliminary anticancer mechanism were systematically investigated. • The NTP-385-CLB conjugate displayed prominent localization in cancer cell nucleus and induced severe DNA double-strand breaks. • FXY-3 exhibited significantly increased in vitro cytotoxicity and superior in vivo anticancer efficiency. 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. [ABSTRACT FROM AUTHOR]

Scorpions belong to class: Arachnida, order: Scorpionida represented now by approximately 1500 species. These are one of the most ancient group of the animals on the earth conserving their morphology almost unaltered and are the most successful inhabitants of the earth. Scorpions when stimulated secrete venom which is a cocktail of variable concentration of neurotoxins, cardiotoxins, nephrotoxins, hemolytic toxins, phosphodiesterases, phospholipases, hyaluronidase, glucosaminoglycans, histamine, seratonin, tryptophan and cytokine releasers. According to an estimate, frequency of deaths caused by scorpion sting is higher in comparison to that of caused by snake-bite. Almost all of these lethal scorpions except Hemiscorpious species belong to scorpion family Buthidae comprising 500 species. Scorpion venoms show variable reactions in envenomated patients. However, closer the phylogenic relationship among the scorpions, more similar the immunological properties. Furthermore, various constituents of venom may act directly or indirectly and individually or synergistically to exert their effects. Scorpion stings cause a wide range of conditions from severe local skin reactions to neurologic, respiratory and cardiovascular collapse. Lethal members of Buthidae family include Buthus, Parabuthus, Mesobuthus, Tityus, Leiurus, Androctonus and Centruroides. Besides their lethal properties, scorpion venoms have some unique properties beneficial to mankind. These contain anti-insect, antimicrobial and anticancer properties and thus, can play a key role in the insect pest management programmes, treatment of microbial infection and in the treatment of various cancer types. DOI: http://dx.doi.org/10.5281/zenodo.998076

Antimicrobial peptides (AMPs) have been described as excellent candidates to overcome antibiotic resistance. Frequently, AMPs exhibit a wide therapeutic window, with low cytotoxicity and broad-spectrum antimicrobial activity against a variety of pathogens. In addition, some AMPs are also able to modulate the immune response, decreasing potential harmful effects such as sepsis. Despite these benefits, only a few formulations have successfully reached clinics. A common flaw in the druggability of AMPs is their poor pharmacokinetics, common to several peptide drugs, as they may be degraded by a myriad of proteases inside the organism. The combination of AMPs with carrier nanoparticles to improve delivery may enhance their half-life, decreasing the dosage and thus, reducing production costs and eventual toxicity. Here, we present the most recent advances in lipid and metal nanodevices for AMP delivery, with a special focus on metal nanoparticles and liposome formulations.

Dermaseptins belonging to a large family of cationic membrane-disruption antimicrobial peptides display extensive antibacterial and antiproliferative activities depending on a coil-to-helix transition and the specific structural parameters. Herein, a novel dermaseptin peptide named Der-PS4 was discovered from the skin secretion of the waxy monkey tree frog, Phyllomedusa sauvagii. The complementary DNA (cDNA)-encoding precursor was obtained relying on “shotgun” cloning, and afterwards, a mature peptide amino acid sequence was identified by reverse-phase high performance liquid chromatography (RP-HPLC) and MS/MS. Specimens were chemically synthesized and applied for further functional studies. Structural analysis demonstrated a higher α-helical content in the membrane-mimetic environment compared with that in the ammonium acetate/water circumstance. Der-PS4 displayed a broad spectrum of antimicrobial activities against tested pathogenic microorganisms, however, exhibiting slight membrane-damaging effectiveness towards horse red blood cells. Coincident with the inhibitory activities on pathogens, Der-PS4 also showed considerable biofilm eradicating impact. Also, Der-PS4 penetrated cell membrane in a relative short period under each minimum bactericidal concentration. In addition, Der-PS4 possessed antiproliferative capacity against five cancer cell lines, while presenting slight suppressing effect on human microvascular endothelial, HMEC-1. These findings provide a promising insight for the discovery and development of novel drugs from a natural source.

Amphibians are a natural source of abundant antimicrobial peptides and thus have been widely investigated for isolation of such biomolecules. Many new antimicrobial peptide families have been discovered from amphibians. In this study, a novel antimicrobial peptide named Limnonectes fujianensis Brevinvin (LFB) has been identified in the skin secretion from the Fujian large headed frog, Limnonectes fujianensis. The cDNA sequence was cloned from a skin secretion library and the predicted mature peptide was identified through MS/MS fragmentation sequencing of reverse phase HPLC fractions on the same sample. LFB was predicted to be an amphipathic, hydrophobic, alpha helical, and beta turn peptide that inserts into a lipid bilayer in order to kill the cells. In antimicrobial assays, a synthetic replicate of this novel antimicrobial peptide demonstrated significant activity against the Gram-positive bacterium Staphylococcus aureus, the Gram-negative bacterium Escherichia coli and the yeast, Candida albicans. This novel peptide was highly potent (MIC 4.88 uM) against Gram-negative bacterium, and also has the ability to inhibit the growth of human cancer cell lines with IC50 values ranging from 18.9 μM down to 2.0 μM. These findings help to enrich our understanding of Brevinin-like peptides. Moreover, the data presented here validate frog secretion as a source of potential novel antimicrobial peptides, that also exhibit anti-tumor properties, that could be useful for the treatment of cancer.

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.