Mimics of Host Defense Peptides Derived from Dendronized Polylysines for Antibacterial and Anticancer Therapy

Bacteria in tumor microenvironments promote carcinogenesis and trigger complications, suggesting the significance of intervening in bacterial growth in cancer treatment. Here, dendrimer-derived mimics (DMs) of host defense peptides (HDPs) were designed for antibacterial and anticancer therapy, which feature a dendronized polylysine core and polycaprolactone arms. DMs displayed not only remarkable activities against Staphylococcus aureus and human lung cancer cells, but also exceptional selectivity. The membranolytic mechanism revealed by morphology analysis explained their low susceptibility to induce resistance. Further, the optimized DM inhibited tumor growth in the subcutaneous tumor model when administered via intraperitoneal injection and exhibited negligible toxicity to tissues. Overall, we combined the superiority of dendrimers and the mechanism from HDPs to design agents with dual antibacterial and anticancer activities that possess great potential for clinical oncology therapy.

Polysulfonium: Unveiling a Bioactive Polymer to Induce Immunogenic Cell Death for Anticancer Therapy

Here we report a brand-new bioactive polymer featuring sulfonium moieties that exhibits the capability of inducing immunogenic cell death (ICD) for anticancer therapy. The optimized polysulfonium presents a wide spectrum of potent anticancer activity and remarkable selectivity. In-depth mechanistic studies reveal that the polymer exerts its cytotoxic effects on cancer cells through a membrane-disrupting mechanism. This further initiates the release of a plethora of damage-associated molecular patterns, effectively triggering ICD and resulting in systemic anticancer immune responses. Notably, the compound demonstrated significant efficacy in suppressing tumor growth in the B16-F10 melanoma tumor model. Furthermore, it exhibits robust immune memory effects, effectively suppressing tumor recurrence and metastasis in both the rechallenge model and the lung metastatic tumor model. To the best of our knowledge, the study represents the pioneering exportation of cationic polysulfoniums, showcasing not only their remarkable safety and efficacy against primary tumors but also their unique ability in activating long-term immune memory.

Melittin alcalase-hydrolysate: a novel chemically characterized multifunctional bioagent; antibacterial, anti-biofilm and anticancer

The prevalent life-threatening microbial and cancer diseases and lack of effective pharmaceutical therapies created the need for new molecules with antimicrobial and anticancer potential. Bee venom (BV) was collected from honeybee workers, and melittin (NM) was extracted from BV and analyzed by urea-polyacrylamide gel electrophoresis (urea-PAGE). The isolated melittin was hydrolyzed with alcalase into new bioactive peptides and evaluated for their antimicrobial and anticancer activity. Gel filtration chromatography fractionated melittin hydrolysate (HM) into three significant fractions (F1, F2, and F3), that were characterized by electrospray ionization mass spectrometry (ESI-MS) and evaluated for their antimicrobial, anti-biofilm, antitumor, and anti-migration activities. All the tested peptides showed antimicrobial and anti-biofilm activities against Gram-positive and Gram-negative bacteria. Melittin and its fractions significantly inhibited the proliferation of two types of cancer cells (Huh-7 and HCT 116). Yet, melittin and its fractions did not affect the viability of normal human lung Wi-38 cells. The IC50 and selectivity index data evidenced the superiority of melittin peptide fractions over intact melittin. Melittin enzymatic hydrolysate is a promising novel product with high potential as an antibacterial and anticancer agent.

Enzyme-responsive oncolytic polypeptide for tumor therapy

Host defense peptide-mimicking cationic oncolytic polymers have attracted increasing attention for cancer treatment in recent years. However, polymers with large amounts of positive charge may cause rapid clearance and severe off-target toxicity. To facilitate in vivo application, an alkaline phosphatase (ALP)-responsive oncolytic polypeptide precursor (C12-PLL/PA) has been reported in this work. C12-PLL/PA could be hydrolyzed into the active form of the oncolytic polypeptide (C12-PLL) by the extracellular alkaline phosphatase within solid tumors, thereby resulting in the conversion of the negative charge to positive charge and restoring its membrane-lytic activity. Detailed mechanistic studies showed that C12-PLL/PA could effectively destroy cancer cell membranes and subsequently result in rapid necrosis of cancer cells. More importantly, C12-PLL/PA significantly inhibited the tumor growth in the 4T1 orthotopic breast tumor model with negligible side effects. In summary, these findings demonstrated that the shielding of the amino groups with phosphate groups represents a secure and effective strategy to develop cationic oncolytic polypeptide, which represents a valuable reference for the design of enzyme-activated oncolytic polymers. STATEMENT OF SIGNIFICANCE: Recently, there has been a growing interest in fabricating host defense peptide-mimicking cationic oncolytic polymers for cancer therapy. However, there remain concerns about the tumor selectivity and off-target toxicity of these cationic polymers. In this study, an alkaline phosphatase-responsive oncolytic polypeptide precursor (C12-PLL/PA) has been developed to selectively target cancer cells while sparing normal cells. Mechanistic investigations demonstrated that C12-PLL/PA effectively disrupted cancer cell membranes, leading to rapid necrosis. Both in vitro and in vivo experiments showed promising anticancer activity and reliable safety of C12-PLL/PA. The findings suggest that this synthetic enzyme-responsive polypeptide holds potential as a tumor-specific oncolytic polymer, paving the way for future applications in cancer therapy.

ACP-DRL: an anticancer peptides recognition method based on deep representation learning

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

Synthesis and anticancer evaluation of [d-Ala]-nocardiotide A

Cancer is currently one of the biggest causes of death in the world. Like some microorganisms, cancer cells also develop resistance to various chemotherapy drugs and are termed multidrug resistant (MDR). In this regard, there is a need to develop new alternative anticancer agents. Anticancer peptides (ACPs) with high selectivity and high cell penetration ability are a promising candidate, as well as they are easy to modify. A cyclohexapeptide called nocardiotide A was isolated from the marine sponge Callyspongia sp., which is cytotoxic towards several cancer cells such as MM, 1S, HeLa, and CT26 cells. Previously, nocardiotide A was synthesized with a very low yield owing to its challenging cyclization process. In this study, we synthesized [d-Ala]-nocardiotide A as a derivative of nocardiotide A using a combination of solid phase peptide synthesis (SPPS) and liquid phase peptide synthesis (LPPS). The synthesis was carried out by selecting a d-alanine residue at the C-terminus to give a desired cyclic peptide product with a yield of 31% after purification. The purified [d-Ala]-nocardiotide A was characterized using HR-ToF MS and 1H and 13C-NMR spectroscopy to validate the desired product. The anticancer activity of the peptide was determined against HeLa cancer cell lines with an IC50 value of 52 μM compared to the parent nocardiotide A with an IC50 value of 59 μM. In the future, we aim to mutate various l-amino acids in nocardiotide A to d-amino acids to prepare nocardiotide A derivatives with a higher activity to kill cancer cells with higher membrane permeation. In addition, the mechanism of action of nocardiotide A and its derivatives will be evaluated.

From oncolytic peptides to oncolytic polymers: A new paradigm for oncotherapy

Traditional cancer therapy methods, especially those directed against specific intracellular targets or signaling pathways, are not powerful enough to overcome tumor heterogeneity and therapeutic resistance. Oncolytic peptides that can induce membrane lysis-mediated cancer cell death and subsequent anticancer immune responses, has provided a new paradigm for cancer therapy. However, the clinical application of oncolytic peptides is always limited by some factors such as unsatisfactory bio-distribution, poor stability, and off-target toxicity. To overcome these limitations, oncolytic polymers stand out as prospective therapeutic materials owing to their high stability, chemical versatility, and scalable production capacity, which has the potential to drive a revolution in cancer treatment. This review provides an overview of the mechanism and structure-activity relationship of oncolytic peptides. Then the oncolytic peptides-mediated combination therapy and the nano-delivery strategies for oncolytic peptides are summarized. Emphatically, the current research progress of oncolytic polymers has been highlighted. Lastly, the challenges and prospects in the development of oncolytic polymers are discussed.

Influence of Fatty Acid Modification on the Anticancer Activity of the Antimicrobial Peptide Figainin 1

Antimicrobial peptides derived from the skin secretions of amphibians have made important progress in tumor therapy due to their unique mechanism of destroying cell membranes. Figainin 1 (F1) is an 18-amino acid antimicrobial peptide from the skin secretions of Boana raniceps frogs. In a previous study, F1 was shown to inhibit cancer cell proliferation. F1 is composed entirely of natural amino acids; therefore, it is easily degraded by a variety of proteases, resulting in poor stability and a short half-life. In the present study, we used a fatty acid modification strategy to improve the stability of Figainin 1. Among the 8 peptides synthesized, A-10 showed the strongest antiproliferative activity against K562 cells and the other four tumor cell lines, and its stability against serum and proteinase K was improved compared with F1. We found that A-10 works through two mechanisms, cell membrane destruction and apoptosis, and can arrest the cell cycle in the G0/G1 phase. Moreover, A-10 exhibited self-assembly behavior. Overall, it is necessary to select a fatty acid with a suitable length for modification to improve the stability and antiproliferative activity of antimicrobial peptides. This study provides a good reference for the development of antimicrobial peptides as effective anticancer compounds.

Evaluation of glycyl-arginine and lysyl-aspartic acid dipeptides for their antimicrobial, antibiofilm, and anticancer potentials

Antibacterial resistance and cancer are worldwide challenges and have been defined as major threats by international health organizations. Peptides are produced naturally by all organisms and have a variety of immunomodulatory, physiological, and wound-healing properties. They can also provide protection against microorganisms and tumor cells. Therefore, we aimed to determine the antimicrobial, antibiofilm, and anticancer potentials of Glycyl-Arginine and Lysyl-Aspartic acid dipeptides. The Broth Dilution and Crystal Violet Binding assays assessed the antimicrobial tests and biofilm inhibitory effects. The MTT assay was used to measure the cytotoxic effects of dipeptides on HeLa cell viability. According to our results, Candida tropicalis T26 and Proteus mirabilis U15 strains were determined as more resistant to Staphylococcus epidermidis W17 against Glycyl-Arginine and Lysyl-Aspartic acid dipeptides with MICs higher than 2 mM (1 mg/mL). Sub-MICs of Glycyl-Arginine caused inhibitions against biofilm formation of all the tested clinical isolates, with the highest inhibition observed against S. epidermidisW17. Lysyl-Aspartic acid exhibited zero to no effect against biofilm formation of P. mirabilisU15, and S. epidermidisW17, whereas it exhibited 52% inhibition of biofilm formation of C. tropicalisT26. Cell viability results revealed that HeLa cell viability decreases with increasing concentration of both dipeptides. Also, parallel to antimicrobial tests, Glycyl-Arginine has a greater cytotoxic effect compared to Lysyl-Aspartic acid. The findings from this study will contribute to the advancement of novel strategies involving dipeptide-based synthesizable molecules and drug development studies. However, it is essential to note that there are still challenges, including the need for extensive experimental and clinical trials.

A deep learning based ensemble approach for protein allergen classification

In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions. Bioinformatics and computational methods can be used to analyze the information contained in amino-acid sequences to detect possible allergens. The article presents a deep learning based ensemble approach to identify protein allergens using Extra Tree, Deep Belief Network (DBN), and CatBoost models. The proposed ensemble model achieves higher detection accuracy by combining the prediction results of the three models using majority voting. The evaluation of the proposed model was carried out on the benchmark protein allergen dataset, and the performance analysis revealed that the proposed model outperforms the other state-of-the-art literature techniques with a protein allergen detection accuracy of 89.16%.

Studying the effect of gene fusion of A and C types capsular synthesizing enzymes and anticancer sequence on inducing the expression of apoptotic BCL-2, BAX, and Caspase-3 genes by Real-time RT-PCR method

Background

Today, uterine cancer is one of the most important causes of death in the world and is one of the major problems in human health. There have been numerous reports of the effect of Streptococcus agalactiae peptide and capsular products against cancer cell lines. Objective: This study aimed to research recombinant peptide CPSA-CPSC-L-ACAN and investigate its apoptotic effect against the HeLa cell line by Real-Time-RT PCR.

Design

In this study confirmation of the recombinant fusion peptide was performed by Western blotting. The effect of cytotoxicity of different concentrations of recombinant fusion peptide against the HeLa cell line was investigated by the MTT technique. The expression of apoptotic genes including BAX, BCL-2, and Caspase-3 in comparison with the GAPDH reference gene before and after exposure to recombinant fusion peptide was measured by Real-Time RT-PCR.

Results

Recombinant fusion peptide at a concentration of 63 μg/ml destroyed 50% of the HeLa cell line in 24 h and cell treatment with this concentration increased gene expression of Caspase-3 genes by 16 times, bax by 6 times and decreased the expression of bcl-2 by 0.176 times.

Conclusions

The results showed that treatment of the HeLa cell line with recombinant fusion peptide induced an apoptotic effect. The recombinant fusion peptide could probably help the medical community as a prophylactic or therapeutic treatment for cervical cancer.

A helical oncolytic polypeptide with potent membranolytic activity for cancer therapy

Oncolytic peptides (OLPs) with membranolytic activity show great potential to combat multidrug-resistant cancer cells. Herein, we report a cationic helical oncolytic polypeptide (OLPP) with potent membranolytic activity for cancer therapy. The OLPP was synthesized by ring-opening polymerization of N-carboxyanhydrides (NCAs) and thiol-ene reaction. The OLPP was resistant to protease, showed high cytotoxicity to a series of cancer cells and caused cancer cell necrosis by quickly lysing cancer cell membrane independent of classic death-related intracellular pathways. Intra-tumoral injection of the OLPP effectively suppressed tumor growth in mice through the direct oncolytic effect. The OLPP represents a potential oncolytic chemotherapeutics for cancer therapy.

Structural Characterization, Functional Profiling, and Mechanism Study of Four Antimicrobial Peptides for Antibacterial and Anticancer Applications

Antimicrobial peptides (AMPs) are potent compounds for treating bacterial infection and cancer, drawing ever-increasing interest. However, the function and mechanism of most AMPs remain to be explored. In this research, we focused on investigating the antibacterial and anticancer activities of four AMPs (Dhvar4, Lasioglossin-III, Macropin 1, and Temporin La) and the possible corresponding mechanisms. All four AMPs are cationic α-helical with moderate hydrophobicity and high helicity. They have broad-spectrum antibacterial capacities, among which the antibacterial activities of Dhvar4 and Temporin La are not as effective as Lasioglossin-III and Macropin 1. Macropin 1 exhibited the highest antibacterial effect with a pretty low minimal inhibitory concentration (MIC) of 2-8 μM. Meanwhile, Lasioglossin-III exhibited the strongest anticancer activities, displaying the IC₅₀ of 26.36 μM for A549 and 7.75 μM for HepG2. Although Dhvar4 possessed the highest positive charge and entered the bacterial and animal cells in large amounts, it displayed the lowest bactericidal and anticancer activities which might be ascribed to its lowest hydrophobicity and thus the weakest cell membrane damage capability. It seems that the positive charge and cell internalization play a supporting rather than a determined role in antibacterial and anticancer activities of AMPs. All the four AMPs damaged the bacterial cell membrane with Macropin 1 damaging the cell membrane of Escherichia coli the most and Lasioglossin-III destroying the cell membrane of Staphylococcus aureus the worst. In addition, the animal cellular internalization of the four peptides was temperature-dependent and mainly mediated by caveolae-mediated endocytosis, and they were distributed in lysosomes once inside the cells. These findings expand our knowledge on the function and mechanism of AMPs, laying the fundamental theoretical basis for designing and engineering AMPs for infection and cancer treatment.

Changes in Length and Positive Charge of Palindromic Sequence RWQWRWQWR Enhance Cytotoxic Activity against Breast Cancer Cell Lines

Breast cancer is one of the main causes of premature death in women; current treatments have low selectivity, generating strong physical and psychological sequelae. The palindromic peptide R-1-R (RWQWRWQWR) has cytotoxic activity against different cell lines derived from cancer and selectivity against noncancerous cells. To determine if changes in the charge/length of this peptide increase its activity, six peptides were obtained by SPPS, three of them with addition of Arg at the N, C-terminal or both and three with deletion of Arg at the N, C-terminal or both. The cytotoxic and selective activities were evaluated against MCF-7, MDA-MB-231, and MCF-12 cell lines and fibroblast primary cell culture, evidencing that the RR-1-R peptide with the inclusion of Arg in the N-terminal end maintained selectivity and increased cytotoxicity against lines derived from breast cancer. The effect of this addition regarding the type of induced cell death was evaluated by flow cytometry, showing very low rates of necrosis and a significant majority of apoptotic events with activation of both Caspase 8 and Caspase 9. This work allowed us to find a modification that generates a peptide with greater cytotoxic effects and can be considered a promising molecule for other approaches to improve anticancer peptides.

ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .

Nanomolar LL-37 induces permeability of a biomimetic mitochondrial membrane

LL-37, the only human host cathelicidin peptide, is proposed to be able to induce host cell apoptosis through mitochondrial membrane permeabilization (MMP). Detailed pathways of the LL-37-triggered MMP are however still disputed. It is generally believed that cationic peptides permeate a membrane mostly in conditions of micromolar peptide concentrations and negatively charged membranes, which are not usually satisfied in the mitochondrial circumstance. Herein, using a variety of single-molecule techniques, we show that nanomolar LL-37 specifically induces permeability of a phosphoethanolamine (PE)-rich biomimetic mitochondrial membrane in a protein-independent manner. The insertion dynamics of single LL-37 molecules exhibit different metastable states in bilayers composed of different lipids. Moreover, the PE lipids significantly facilitate adsorption and accumulation of LL-37 on the PE-rich bilayer, and produce deeper insertion of peptide oligomers, especially tetramers, into the bilayer. This work offers an alternative pathway of the LL-37-triggered MMP and apoptosis.

In Silico Discovery of Anticancer Peptides from Sanghuang

Anticancer peptide (ACP) is a short peptide with less than 50 amino acids that has been discovered in a variety of foods. It has been demonstrated that traditional Chinese medicine or food can help treat cancer in some cases, which suggests that ACP may be one of the therapeutic ingredients. Studies on the anti-cancer properties of Sanghuangporus sanghuang have concentrated on polysaccharides, flavonoids, triterpenoids, etc. The function of peptides has not received much attention. The purpose of this study is to use computer mining techniques to search for potential anticancer peptides from 62 proteins of Sanghuang. We used mACPpred to perform sequence scans after theoretical trypsin hydrolysis and discovered nine fragments with an anticancer probability of over 0.60. The study used AlphaFold 2 to perform structural modeling of the first three ACPs discovered, which had blast results from the Cancer PPD database. Using reverse docking technology, we found the target proteins and interacting residues of two ACPs with an unknown mechanism. Reverse docking results predicted the binding modes of the ACPs and their target protein. In addition, we determined the active part of ACPs by quantum chemical calculation. Our study provides a framework for the future discovery of functional peptides from foods. The ACPs discovered have the potential to be used as drugs in oncology clinical treatment after further research.

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.

Towards in vivo photomediated delivery of anticancer peptides: Insights from pharmacokinetic and -dynamic data

An in vivo study of a photoswitchable cytotoxic peptide LMB040 has been undertaken on a chemically induced hepatocellular carcinoma model in immunocompetent rats. We analysed the pharmacokinetic profile of the less toxic photoform ("ring-closed" dithienylethene) of the compound in tumors, plasma, and healthy liver. Accordingly, the peptide can reach a tumor concentration sufficiently high to exert a cytotoxic effect upon photoconversion into the more active ("ring-open") photoform. Tissue morphology, histology, redox state of the liver, and hepatic biochemical parameters in blood serum were analysed upon treatment with (i) the less active photoform, (ii) the in vivo light-activated alternative photoform, and (iii) compared with a reference chemotherapeutic 5-fluorouracil. We found that application of the less toxic form followed by a delayed in vivo photoconversion into the more toxic ring-open form of LMB040 led to a higher overall survival of the animals, and signs of enhanced immune response were observed compared to the untreated animals.

Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides

Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.

Designing a novel fusion protein from Streptococcus agalactiae with apoptosis induction effects on cervical cancer cells

Cervical cancer remains life-threatening cancer in women around the world. Due to the limitations of conventional treatment approaches, there is an urgent need to develop novel and more efficient strategies against cervical cancer. Therefore, the researchers attend to the alternative anti-cancer compounds like bacterial products. Rib and α are known as surface proteins of Streptococcus agalactiae with immunologic effects. In the present study, we designed a new anti-cancer fusion protein (Rib-α) originating from S. agalactiae with in silico methods, and then, the recombinant gene was cloned in the pET-22 (+) expression vector. The recombinant protein was expressed in E. coli BL21. To purify the expressed protein, we applied the Ni-NTA column. The molecular mechanism by which Rib-α is cytotoxic to cancer cells has been discussed based on MTT, flow cytometry, and real-time PCR methods. The engineered fusion protein suppressed the proliferation of the cancer cells at 180 μg/ml. Cytotoxic assessment and morphological changes, augmentation of apoptotic-related genes, upregulation of caspase-3 mRNA, and flow cytometric analysis confirmed that apoptosis might be the principal mechanism of cell death. According to our findings, Rib-α fusion protein motivated the intrinsic apoptosis pathway. Therefore, it can be an exciting candidate to discover a new class of antineoplastic agents.

Influence of chain length on the anticancer activity of the antimicrobial peptide CAMEL with fatty acid modification

Antimicrobial peptides (AMPs) display promising potential in cancer therapy. Modification with fatty acids is a simple and effective approach to improve the activity of AMPs. In the present study, we investigated the effects of fatty acid chain lengths on the anticancer activity, self-assembly and mechanism of action of CAMEL (CM15, KWKLFKKIGAVLKVL-NH₂), an amphipathic AMP with 15 amino acids. Conjugation of fatty acids could obviously improve the in vitro anticancer activity of CAMEL. Among the tested peptides, C12-CAMEL showed the highest anticancer activity, while C16-CAMEL killed cancer cells with the slowest kinetics. This may be related to the self-assembly of C12-CAMEL and C16-CAMEL, which could form spherical nanoparticles and tightened nanofibers, respectively. In addition, necrosis and necroptosis rather than apoptosis were the major mechanisms underlying the anticancer activity of CAMEL, C12-CAMEL and C16-CAMEL, implying that modification with fatty acids did not obviously alter the mechanism of action of CAMEL. Notably, C12-CAMEL, with high and rapid cell-killing activity, exhibited significantly stronger in vivo anticancer activity than CAMEL and C16-CAMEL. Overall, the present work suggests that the choice of a suitable fatty acid for structural modification is necessary for improving the anticancer activity of AMPs.

Effect of hydrophobicity on distinct anticancer mechanism of antimicrobial peptide chensinin-1b and its lipoanalog PA-C1b in breast cancer cells

Chensinin-1b and its lipoanalogs demonstrate different anticancer activities against selected cancer cells, and the anticancer activity of PA-C1b is improved up to 3-fold compared with that of the parent peptide chensinin-1b. However, detailing the mechanism of action of these peptides is required to better understand the structure-function relationship. In this study, chensinin-1b and PA-C1b were selected as the representative peptides to investigate the mode of action in cancer cells. The results indicated that the boundary of the cell membrane was broken when the cells were treated with chensinin-1b, while that of cells treated with PA-C1b remained intact based on morphological changes. Apoptosis assays indicated that PA-C1b induced MCF-7 cancer cell apoptosis, while chensinin-1b mainly damaged the cell membrane. MCF-7 cancer cells treated with the peptides induced the loss of mitochondrial membrane potential, and cytochrome c was released from mitochondria, but PA-C1b enhanced ROS generation. Additionally, PA-C1b uptake occurred via an energy-dependent pathway and was inhibited by selected endocytosis inhibitors. Furthermore, treatment of MCF-7 cells with PA-C1b suppressed Bcl-2 mRNA levels and increased Bax mRNA levels, upregulated the expression of the proapoptotic protein Bax and downregulated the expression of the antiapoptotic protein Bcl-2. These results indicate that the anticancer mechanism of AMPs may be considerably affected by only a slight difference in the hydrophobicity of the two peptides; and such a study may facilitate the design of novel peptide-based anticancer agents.

Conjugation with gold nanoparticles improves the stability of the KT2 peptide and maintains its anticancer properties

One of the major weaknesses of therapeutic peptides is their sensitivity to degradation by proteolytic enzymes in vivo. Gold nanoparticles (GNPs) are a good carrier for therapeutic peptides to improve their stability and cellular uptake in vitro and in vivo. We conjugated the anticancer KT2 peptide as an anticancer peptide model to PEGylated GNPs (GNPs-PEG) and investigated the peptide stability, cellular uptake and ability of the GNPs-KT2-PEG conjugates to induce MDA-MB-231 human breast cancer cell death. We found that 11 nm GNPs protected the conjugated KT2 peptide from trypsin proteolysis, keeping it stable up to 0.128% trypsin, which is higher than the serum trypsin concentration (range 0.0000285 ± 0.0000125%) reported by Lake-Bakaar, G. et al., 1979. GNPs significantly enhanced the cellular uptake of KT2 peptides after conjugation. Free KT2 peptides pretreated with trypsin were not able to kill MDA-MB-231 cells due to proteolysis, while GNPs-KT2-PEG was still able to exert effective cancer cell killing after trypsin treatment at levels comparable to GNPs-KT2-PEG without enzyme pretreatment. The outcome of this study highlights the utility of conjugated anticancer peptides on nanoparticles to improve peptide stability and retain anticancer ability.

One of the major weaknesses of therapeutic peptides is their sensitivity to degradation by proteolytic enzymes in vivo.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

The Azurin-Derived Peptide CT-p19LC Exhibits Membrane-Active Properties and Induces Cancer Cell Death

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.

Purification and erythrocyte-membrane perturbing activity of a ketose-specific lectin from Moringa oleifera seeds

This study purified a hemagglutinating protein (MoL) from Moringa oleifera seed, and investigated its hemolytic activity. Molecular weight and stability of MoL were also determined. Modification of some amino acid residues was carried out and the effect on MoL hemagglutinating activity determined. Other investigated parameters are the effects of temperature, concentration, incubation period, pH, and sugars on the protein's hemagglutinating and hemolytic activities. The native and subunit molecular weights were estimated as 30 and 27.5 kDa respectively. Hemagglutinating activity of MoL was slightly inhibited by fructose and sucrose, stable at temperature up to 90°C and within pH range of 2-4. Modification of tryptophan and arginine residues resulted in partial loss of hemagglutinating activity. The hemolytic activity of MoL was concentration, temperature, pH, and time-dependent. The study concluded that MoL showed hemolytic (membrane-perturbing) activity in moderate acidic conditions. This suggests its potential exploitation in improved intracellular delivery of bioactive compounds.

Membrane Association Modes of Natural Anticancer Peptides: Mechanistic Details on Helicity, Orientation, and Surface Coverage

Anticancer peptides (ACPs) could potentially offer many advantages over other cancer therapies. ACPs often target cell membranes, where their surface mechanism is coupled to a conformational change into helical structures. However, details on their binding are still unclear, which would be crucial to reach progress in connecting structural aspects to ACP action and to therapeutic developments. Here we investigated natural helical ACPs, Lasioglossin LL-III, Macropin 1, Temporin-La, FK-16, and LL-37, on model liposomes, and also on extracellular vesicles (EVs), with an outer leaflet composition similar to cancer cells. The combined simulations and experiments identified three distinct binding modes to the membranes. Firstly, a highly helical structure, lying mainly on the membrane surface; secondly, a similar, yet only partially helical structure with disordered regions; and thirdly, a helical monomeric form with a non-inserted perpendicular orientation relative to the membrane surface. The latter allows large swings of the helix while the N-terminal is anchored to the headgroup region. These results indicate that subtle differences in sequence and charge can result in altered binding modes. The first two modes could be part of the well-known carpet model mechanism, whereas the newly identified third mode could be an intermediate state, existing prior to membrane insertion.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.

Plant Bioactive Peptides: Current Status and Prospects Towards Use on Human Health

Large numbers of bioactive peptides with potential applications in protecting against human diseases have been identified from plant sources. In this review, we summarized recent progress in the research of plant-derived bioactive peptides, encompassing their production, biological effects, and mechanisms. This review focuses on antioxidant, antimicrobial, antidiabetic, and anticancer peptides, giving special attention to evidence derived from cellular and animal models. Studies investigating peptides with known sequences and well-characterized peptidic fractions or protein hydrolysates will be discussed. The use of molecular docking tools to elucidate inter-molecular interactions between bioactive peptides and target proteins is highlighted. In conclusion, the accumulating evidence from in silico, in vitro and in vivo studies to date supports the envisioned applications of plant peptides as natural antioxidants as well as health-promoting agents. Notwithstanding, much work is still required before the envisioned applications of plant peptides can be realized. To this end, future researches for addressing current gaps were proposed.

Copper-binding anticancer peptides from the piscidin family: an expanded mechanism that encompasses physical and chemical bilayer disruption

In the search for novel broad-spectrum therapeutics to fight chronic infections, inflammation, and cancer, host defense peptides (HDPs) have garnered increasing interest. Characterizing their biologically-active conformations and minimum motifs for function represents a requisite step to developing them into efficacious and safe therapeutics. Here, we demonstrate that metallating HDPs with Cu2+ is an effective chemical strategy to improve their cytotoxicity on cancer cells. Mechanistically, we find that prepared as Cu2+-complexes, the peptides not only physically but also chemically damage lipid membranes. Our testing ground features piscidins 1 and 3 (P1/3), two amphipathic, histidine-rich, membrane-interacting, and cell-penetrating HDPs that are α-helical bound to membranes. To investigate their membrane location, permeabilization effects, and lipid-oxidation capability, we employ neutron reflectometry, impedance spectroscopy, neutron diffraction, and UV spectroscopy. While P1-apo is more potent than P3-apo, metallation boosts their cytotoxicities by up to two- and seven-fold, respectively. Remarkably, P3-Cu2+ is particularly effective at inserting in bilayers, causing water crevices in the hydrocarbon region and placing Cu2+ near the double bonds of the acyl chains, as needed to oxidize them. This study points at a new paradigm where complexing HDPs with Cu2+ to expand their mechanistic reach could be explored to design more potent peptide-based anticancer therapeutics.

Anticancer Mechanisms of Bioactive Peptides

Despite technological advancement, there is no 100% effective treatment against metastatic cancer. Increasing resistance of cancer cells towards chemotherapeutic drugs along with detrimental side effects remained a concern. Thus, the urgency in developing new anticancer agents has been raised. Anticancer peptides have been proven to display potent activity against a wide variety of cancer cells. Several mode of actions describing their cytostatic and cytotoxic effect on cancer cells have been proposed which involves cell surface binding leading to membranolysis or internalization to reach their intracellular target. Understanding the mechanism of action of these anticancer peptides is important in achieving full therapeutic success. In the present article, we discuss the anticancer action of peptides accompanied by the mechanisms underpinning their toxicity to cancer cells.

Net charge tuning modulates the antiplasmodial and anticancer properties of peptides derived from scorpion venom

VmCT1, a linear helical antimicrobial peptide isolated from the venom of the scorpion Vaejovis mexicanus, displays broad spectrum antimicrobial activity against bacteria, fungi, and protozoa. Analogs derived from this peptide containing single Arg-substitutions have been shown to increase antimicrobial and antiparasitic activities against Trypanossoma cruzi. Here, we tested these analogs against malaria, an infectious disease caused by Plasmodium protozoa, and assessed their antitumoral properties. Specifically, we tested VmCT1 synthetic variants [Arg]³ -VmCT1-NH₂ , [Arg]⁷ -VmCT1-NH₂ , and [Arg]¹¹ -VmCT1-NH₂ , against Plasmodium gallinaceum sporozoites and MCF-7 mammary cancer cells. Our screen identified peptides [Arg]³ -VmCT1-NH₂ and [Arg]⁷ -VmCT1-NH₂ as potent antiplasmodial agents (IC₅₀ of 0.57 and 0.51 μmol L^-1 , respectively), whereas [Arg]¹¹ -VmCT1-NH₂ did not show activity against P. gallinaceum sporozoites. Interestingly, all peptides presented activity against MCF-7 and displayed lower cytotoxicity toward healthy cells. We demonstrate that increasing the net positive charge of VmCT1, through arginine substitutions, modulates the biological properties of this peptide family yielding novel antiplasmodial and antitumoral molecules.

DRACP: a novel method for identification of anticancer peptides

Background

Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs.

Results

Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets.

Conclusion

We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Uncoupling the Folding-Function Paradigm of Lytic Peptides to Deliver Impermeable Inhibitors of Intracellular Protein-Protein Interactions

Here, we describe the use of peptide backbone N-methylation as a new strategy to transform membrane-lytic peptides (MLPs) into cytocompatible intracellular delivery vehicles. The ability of lytic peptides to engage with cell membranes has been exploited for drug delivery to carry impermeable cargo into cells, but their inherent toxicity results in narrow therapeutic windows that limit their clinical translation. For most linear MLPs, a prerequisite for membrane activity is their folding at cell surfaces. Modification of their backbone with N-methyl amides inhibits folding, which directly correlates to a reduction in lytic potential but only minimally affects cell entry. We synthesized a library of N-methylated peptides derived from MLPs and conducted structure-activity studies that demonstrated the broad utility of this approach across different secondary structures, including both β-sheet and helix-forming peptides. Our strategy is highlighted by the delivery of a notoriously difficult class of protein-protein interaction inhibitors that displayed on-target activity within cells.

Machine learning-guided discovery and design of non-hemolytic peptides

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.

Antineoplastic Drug-Free Anticancer Strategy Enabled by Host-Defense-Peptides-Mimicking Synthetic Polypeptides

An antineoplastic drug-free anticancer strategy enabled by host defense peptides (HDPs)-mimicking synthetic polypeptides is reported. The polypeptide exhibits a broad spectrum of anticancer activity in 12 cancer cell lines, including drug-resistant and highly metastatic tumor cells. Detailed mechanistic studies reveal that the cationic anticancer polypeptide (ACPP) can directly induce rapid necrosis of cancer cells within minutes through a membrane-lytic mechanism. Moreover, a pH-sensitive zwitterionic derivative of ACPP (DA-ACPP) is prepared for in vivo application. DA-ACPP shows negligible hemolysis under neutral physiological conditions, and can be converted back to ACPP in slightly acidic tumor environments, resulting in selective killing of cancer cells. Consequently, DA-ACPP shows an effective inhibition of tumor growth in both 4T1 orthotopic breast tumor models and B16-F10 melanoma pulmonary metastatic models. Overall, these findings demonstrate that synthetic HDPs-mimicking polypeptides represent safe and effective antineoplastic agents, which sheds new light on the development of drug-free synthetic polymers for cancer therapy.

Selective Cytotoxicity of a Novel Trp-Rich Peptide against Lung Tumor Spheroids Encapsulated inside a 3D Microfluidic Device

There is a globally rising healthcare need to develop new anticancer therapies as well as to test them on biologically relevant in vitro cancer models instead of overly simplistic 2D models. To address both these needs, a 3D lung cancer spheroid model is developed using human A549 cells trapped inside a collagen gel in a compartmentalized microfluidic device and homogenously sized (35-45 µm) multicellular tumor spheroids are obtained in 5 days. The novel tryptophan-rich peptide P1, identified earlier as a potential anticancer peptide (ACP), shows enhanced cytotoxic efficacy against A549 tumor spheroids (>75%) in clinically relevant low concentrations, while it does not affect human amniotic membrane mesenchymal stem cells at the same concentrations (<15%). The peptide also inhibits the formation of tumor spheroids by reducing cell viability as well as lowering the proliferative capacity, which is confirmed by the expression of cell proliferation marker Ki-67. The ACP offers a novel therapeutic strategy against lung cancer cells without affecting healthy cells. The microfluidic device used is likely to be useful in helping develop models for several other cancer types to test new anticancer agents.

A biotechnological approach for the production of branched chain amino acid containing bioactive peptides to improve human health: A review

Improper nutrition provokes many types of chronic diseases and health problems, which consequently are associated with particularly high costs of treatments. Nowadays, consumer's interest in healthy eating is shifting towards specific foods or food ingredients. As a consequence, bioactive peptides as a promising source of health promoting food additives are currently an intensely debated topic in research. Process design is still on its early stages and is significantly influenced by important preliminary decisions. Thus, parameters like peptide bioactivity within the product, selection of the protein source, enzyme selection for hydrolysis, peptide enrichment method, as well as stability of the peptides within the food matrix and bioavailability are sensitive decision points, which have to be purposefully coordinated, as they are directly linked to amino acid content and structure properties of the peptides. Branched chain amino acids (BCAA) are essential components for humans, possessing various important physiologic functions within the body. Incorporated within peptide sequences, they may induce dual functions, when used as nutraceuticals in functional food, thus preserving the foodstuff and prevent several widespread diseases. Furthermore, there is evidence that consuming this peptide-class can be a nutritional support for elderly people or improve human health to prevent diseases caused by incorrect nutrition. Based on the knowledge about the role of BCAA within various peptide functions, discussed in the review, special attention is given to different approaches for systematic selection of the protein source and enzymes used in hydrolysis, as well as suitable peptide enrichment methods, thereby showing current trends in research.

Cyclic Analogues of Horseshoe Crab Peptide Tachyplesin I with Anticancer and Cell Penetrating Properties

Tachyplesin-I (TI) is a host defense peptide from the horseshoe crab Tachypleus tridentatus that has outstanding potential as an anticancer therapeutic lead. Backbone cyclized TI (cTI) has similar anticancer properties to TI but has higher stability and lower hemolytic activity. We designed and synthesized cTI analogues to further improve anticancer potential and investigated structure-activity relationships based on peptide-membrane interactions, cellular uptake, and anticancer activity. The membrane-binding affinity and cytotoxic activity of cTI were found to be highly dependent on peptide hydrophobicity and charge. We describe two analogues with increased selectivity toward melanoma cells and one analogue with the ability to enter cells with high efficacy and low toxicity. Overall, the structure-activity relationship study shows that cTI can be developed as a membrane-active antimelanoma lead, or be employed as a cell penetrating peptide scaffold that can target and enter cells without damaging their integrity.

Covalent Graft of Lipopeptides and Peptide Dendrimers to Cellulose Fibers

Introduction: Bacterial proliferation in health environments may lead to the development of specific pathologies, but can be highly dangerous under particular conditions, such as during chemotherapy. To limit the spread of infections, it is helpful to use gauzes and clothing containing antibacterial agents. As cotton tissues are widespread in health care environments, in this contribution we report the preparation of cellulose fibers characterized by the covalent attachment of lipopeptides as possible antimicrobial agents. Aim: To covalently link peptides to cotton samples and characterize them. Peptides are expected to preserve the features of the fabrics even after repeated washing and use. Peptides are well tolerated by the human body and do not induce resistance in bacteria. Materials and Methods: A commercially available cotton tissue (specific weight of 150 g/m2, 30 Tex yarn fineness, fabric density of 270/230 threads/10 cm in the warp and weft) was washed with alkali and bleached and died. A piece of this tissue was accurately weighed, washed with methanol (MeOH) and N,N-dimethylformamide (DMF), and air-dried. Upon incubation with epibromohydrin, followed by treatment with Fmoc-NH-CH2CH2-NH2 and Fmoc removal, the peptides were synthesized by incorporating one amino acid at a time, beginning with the formation of an amide bond with the free NH2 of 1,2–diaminoethane. We also linked to the fibers a few peptide dendrimers, because the mechanism of action of these peptides often requires the formation of clusters. We prepared and characterized seven peptide-cotton samples. Results: The new peptide-cotton conjugates were characterized by means of FT-IR spectroscopy and X-ray Photoelectron Spectroscopy (XPS). This latter technique allows for discriminating among different amino acids and thus different peptide-cotton samples. Some samples maintain a pretty good whiteness degree even after peptide functionalization. Interestingly, these samples also display encouraging activities against a Gram positive strain. Conclusions: Potentially antimicrobial lipopeptides can be covalently linked to cotton fabrics, step-by-step. It is also possible to build on the cotton Lys-based dendrimers. XPS is a useful technique to discriminate among different types of nitrogen. Two samples displaying some antibacterial potency did also preserve their whiteness index.

Bacterial Proteinaceous Compounds With Multiple Activities Toward Cancers and Microbial Infection

In recent decades, cancer and multidrug resistance have become a worldwide problem, resulting in high morbidity and mortality. Some infectious agents like Streptococcus pneumoniae, Stomatococcus mucilaginous, Staphylococcus spp., E. coli. Klebsiella spp., Pseudomonas aeruginosa, Candida spp., Helicobacter pylori, hepatitis B and C, and human papillomaviruses (HPV) have been associated with the development of cancer. Chemotherapy, radiotherapy and antibiotics are the conventional treatment for cancer and infectious disease. This treatment causes damage in healthy cells and tissues, and usually triggers systemic side-effects, as well as drug resistance. Therefore, the search for new treatments is urgent, in order to improve efficacy and also reduce side-effects. Proteins and peptides originating from bacteria can thus be a promising alternative to conventional treatments used nowadays against cancer and infectious disease. These molecules have demonstrated specific activity against cancer cells and bacterial infection; indeed, proteins and peptides can be considered as future antimicrobial and anticancer drugs. In this context, this review will focus on the desirable characteristics of proteins and peptides from bacterial sources that demonstrated activity against microbial infections and cancer, as well as their efficacy in vitro and in vivo.

In silico design and optimization of selective membranolytic anticancer peptides

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

Purification and identification of novel cytotoxic oligopeptides from soft coral Sarcophyton glaucum

Globally, peptide-based anticancer therapies have drawn much attention. Marine organisms are a reservoir of anticancer peptides that await discovery. In this study, we aimed to identify cytotoxic oligopeptides from Sarcophyton glaucum. Peptides were purified from among the S. glaucum hydrolysates produced by alcalase, chymotrypsin, papain, and trypsin, guided by a methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay on the human cervical cancer (HeLa) cell line for cytotoxicity evaluation. Purification techniques adopted were membrane ultrafiltration, gel filtration chromatography, solid phase extraction (SPE), and reversed-phase high-performance liquid chromatography (RP-HPLC). Purified peptides were identified by de novo peptide sequencing. From papain hydrolysate, three peptide sequences were identified: AGAPGG, AERQ, and RDTQ (428.45, 502.53, and 518.53 Da, respectively). Peptides synthesized from these sequences exhibited cytotoxicity on HeLa cells with median effect concentration (EC₅₀) values of 8.6, 4.9, and 5.6 mmol/L, respectively, up to 5.8-fold stronger than the anticancer drug 5-fluorouracil. When tested at their respective EC₅₀, AGAPGG, AERQ, and RDTQ showed only 16%, 25%, and 11% cytotoxicity to non-cancerous Hek293 cells, respectively. In conclusion, AERQ, AGAPGG, and RDTQ are promising candidates for future development as peptide-based anticancer drugs.

De novo design of anticancer peptides by ensemble artificial neural networks

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.

Are peptides a solution for the treatment of hyperactivated JAK3 pathways?

While the inactivation mutations that eliminate JAK3 function lead to the immunological disorders such as severe combined immunodeficiency, activation mutations, causing constitutive JAK3 signaling, are known to trigger various types of cancer or are responsible for autoimmune diseases, such as rheumatoid arthritis, psoriasis, or inflammatory bowel diseases. Treatment of hyperactivated JAK3 is still an obstacle, due to different sensibility of mutation types to conventional drugs and unwanted side effects, because these drugs are not absolutely specific for JAK3, thus inhibiting other members of the JAK family, too. Lack of information, in which way sole inhibition of JAK3 is necessary for elimination of the disease, calls for the development of isoform-specific JAK3 inhibitors. Beside this strategy, up to date peptides are a rising alternative as chemo- or immunotherapeutics, but still sparsely represented in drug development and clinical trials. Beyond a possible direct inhibition function, crossing the cancer cell membrane and interfering in disease-causing pathways or triggering apoptosis, peptides could be used in future as adjunct remedies to potentialize traditional therapy and preserve non-affected cells. To discuss such feasible topics, this review deals with the knowledge about the structure-function of JAK3 and the actual state-of-the-art of isoform-specific inhibitor development, as well as the function of currently approved drugs or those currently being tested in clinical trials. Furthermore, several strategies for the application of peptide-based drugs for cancer therapy and the physicochemical and structural relations to peptide efficacy are discussed, and an overview of peptide sequences, which were qualified for clinical trials, is given.