Synthesis and evaluation of potent yaku'amide A analogs

Two full-length analogs of the anticancer peptide yaku’amide A (1a) and four partial structures have been synthesized. These analogs were identified by computational studies in which the three E- and...

Fatty Acid Modification of the Anticancer Peptide LVTX-9 to Enhance Its Cytotoxicity against Malignant Melanoma Cells

Spider venom is a valuable resource for the development of novel anticancer drugs. In this study, we focused on novel linear amphipathic α-helical anticancer peptide LVTX-9, which was derived from the cDNA library of the venom gland of the spider Lycosa vittata. The cytotoxicity of LVTX-9 against murine melanoma cells in the range of 1.56–200 μM was tested and found to be significantly lower than those of most anticancer peptides reported. Its IC50 was determined to be 59.2 ± 19.8 μM in a serum or 76.3 ± 12.7 μM in serum-free medium. Fatty acid modification is a promising strategy for improving peptide performance. Therefore, to enhance the cytotoxic activity of LVTX-9, fatty acid modification of this peptide was performed, and five different carbon chain length lipopeptides named LVTX-9-C12-C20 were produced. Among them, the lipopeptide LVTX-9-C18 showed the highest cytotoxic activity in relation to B16-F10 cells, whether in a serum or serum-free medium. Most importantly, the cytotoxic activity of LVTX-9-C18 was improved by about 12.9 times in a serum medium or 19.3 times in a serum-free medium compared to that of LVTX-9. Subsequently, assays including scanning electron microscopy, trypan blue staining, lactate dehydrogenase leakage assay, and hemolytic activity could indicate that the potential direct cell membrane disruption is the main mechanism of LVTX-9-C18 to induce cancer cell death. Furthermore, the LVTX-9-C18 also showed strong cytotoxicity in relation to 3D B16-F10 spheroids, which indicates it might be a promising lead for developing anticancer drugs.

Evaluation of Apoptotic Activity of Modified Carvacrol and Anticancer Peptide Against AGS Gastric Cancer Cell Line

IntroductionTreating stomach cancer remains a major challenge. There have been many reports of the positive effect of modified carvacrol and anti-cancer peptides on gastric cancer. The main purpose of this study was to determine and design a fusion modified carvacrol and anti-cancer peptide against AGS gastric cancer cell line using flowcytometry technique.MethodDetermination of cell lethality was conducted with different concentrations of the modified carvacrol and anti-cancer peptides against AGS cell line by MTT technique. Treatment of AGS cells with different concentrations of components to evaluate the apoptosis of AGS cells was performed by flow cytometry. In this study used. The MTT test performed according to protocol with different concentrations of components.ResultsThe results showed that the percentage of cells treated with modified carvacrol and anti-cancer peptides at a concentration of 80μg/ml with total apoptosis was 46.91% and increased by 36.25% compared to untreated cells.ConclusionDue to the anticancer properties of modified carvacrol with anticancer peptide, the above combination can be used to treat and induction apoptotic activity against gastric cancer cell line.

Conformation and membrane interaction studies of the potent antimicrobial and anticancer peptide palustrin-Ca

Palustrin-Ca (GFLDIIKDTGKEFAVKILNNLKCKLAGGCPP) is a host defence peptide with potent antimicrobial and anticancer activities, first isolated from the skin of the American bullfrog Lithobates catesbeianus. The peptide is 31 amino acid residues long, cationic and amphipathic. Two-dimensional NMR spectroscopy was employed to characterise its three-dimensional structure in a 50/50% water/2,2,2-trifluoroethanol-$$d_{3}$$ d 3 mixture. The structure is defined by an $$\alpha$$ α -helix that spans between Ile$$^{6}$$ 6 -Ala$$^{26}$$ 26 , and a cyclic disulfide-bridged domain at the C-terminal end of the peptide sequence, between residues 23 and 29. A molecular dynamics simulation was employed to model the peptide’s interactions with sodium dodecyl sulfate micelles, a widely used bacterial membrane-mimicking environment. Throughout the simulation, the peptide was found to maintain its $$\alpha$$ α -helical conformation between residues Ile$$^{6}$$ 6 -Ala$$^{26}$$ 26 , while adopting a position parallel to the surface to micelle, which is energetically-favourable due to many hydrophobic and electrostatic contacts with the micelle.

Development of Breast Cancer Spheroids to Evaluate Cytotoxic Response to an Anticancer Peptide

Breast cancer (BC) is the most commonly diagnosed cancer in women and one of the most common causes of cancer-related deaths. Despite intense research efforts, BC treatment still remains challenging. Improved drug development strategies are needed for impactful benefit to patients. Current preclinical studies rely mostly on cell-based screenings, using two-dimensional (2D) cell monolayers that do not mimic in vivo tumors properly. Herein, we explored the development and characterization of three-dimensional (3D) models, named spheroids, of the most aggressive BC subtypes (triple-negative breast cancer-TNBC; and human-epidermal growth receptor-2-HER2+), using the liquid overlay technique with several selected cell lines. In these cell line-derived spheroids, we studied cell density, proliferation, ultrastructure, apoptosis, reactive oxygen species (ROS) production, and cell permeabilization (live/dead). The results showed a formation of compact and homogeneous spheroids on day 7 after seeding 2000 cells/well for MDA-MB-231 and 5000 cells/well for BT-20 and BT-474. Next, we compared the efficacy of a model anticancer peptide (ACP) in cell monolayers and spheroids. Overall, the results demonstrated spheroids to be less sensitive to treatment than cell monolayers, revealing the need for more robust models in drug development.

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.

Virtual Screening for Biomimetic Anti-Cancer Peptides from Cordyceps militaris Putative Pepsinized Peptidome and Validation on Colon Cancer Cell Line

Colorectal cancer is one of the leading causes of cancer-related death in Thailand and many other countries. The standard practice for curing this cancer is surgery with an adjuvant chemotherapy treatment. However, the unfavorable side effects of chemotherapeutic drugs are undeniable. Recently, protein hydrolysates and anticancer peptides have become popular alternative options for colon cancer treatment. Therefore, we aimed to screen and select the anticancer peptide candidates from the in silico pepsin hydrolysate of a Cordyceps militaris (CM) proteome using machine-learning-based prediction servers for anticancer prediction, i.e., AntiCP, iACP, and MLACP. The selected CM-anticancer peptide candidates could be an alternative treatment or co-treatment agent for colorectal cancer, reducing the use of chemotherapeutic drugs. To ensure the anticancer properties, an in vitro assay was performed with “CM-biomimetic peptides” on the non-metastatic colon cancer cell line (HT-29). According to the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay results from peptide candidate treatments at 0–400 µM, the IC50 doses of the CM-biomimetic peptide with no toxic and cancer-cell-penetrating ability, original C. militaris biomimetic peptide (C-ori), against the HT-29 cell line were 114.9 µM at 72 hours. The effects of C-ori compared to the doxorubicin, a conventional chemotherapeutic drug for colon cancer treatment, and the combination effects of both the CM-anticancer peptide and doxorubicin were observed. The results showed that C-ori increased the overall efficiency in the combination treatment with doxorubicin. According to the acridine orange/propidium iodine (AO/PI) staining assay, C-ori can induce apoptosis in HT-29 cells significantly, confirmed by chromatin condensation, membrane blebbing, apoptotic bodies, and late apoptosis which were observed under a fluorescence microscope.