p28 Bacterial Peptide, as an Anticancer Agent

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

Tumor-targeting engineered probiotic Escherichia coli Nissle 1917 inhibits colorectal tumorigenesis and modulates gut microbiota homeostasis in mice

AIMS

Preliminary studies have identified the use of probiotics as a potential treatment strategy against colorectal cancer (CRC). However, natural probiotics lack direct tumor-targeting and tumor-killing activity in the intestine. This study aimed to construct a tumor-targeting engineered probiotic to combat CRC.

MAIN METHODS

Standard adhesion assay was performed to analyze the adherence ability of tumor-binding protein HlpA to CT26 cells. CCK-8 assay, Hoechst 33258 staining and flow cytometry analysis were used for examining cytotoxicity of tumoricidal protein azurin toward CT26 cells. An engineered probiotic Ep-AH harboring azurin and hlpA genes was developed using Escherichia coli Nissle 1917 (EcN) chassis. Antitumor effects of Ep-AH were evaluated in the azoxymethane (AOM) and dextran sodium sulfate salt (DSS)-induced CRC mice. Moreover, analysis of gut microbiota was conducted via fecal 16S rRNA gene sequencing and shotgun metagenomic sequencing.

KEY FINDINGS

Azurin caused a dose-dependent increase of apoptosis in CT26 cells. Ep-AH treatment reversed weight loss (p < 0.001), fecal occult blood (p < 0.01), and shortening of colon length (p < 0.001) than model group, as well as reducing tumorigenesis by 36 % (p < 0.001). Both Ep-H and Ep-A (EcN expressing HlpA or azurin) were less effective than Ep-AH. Furthermore, Ep-AH enriched the members of beneficial bacteria (e.g., Blautia and Bifidobacterium) and reversed abnormal changes of genes associated with several metabolic pathways (e.g., lipopolysaccharide biosynthesis).

SIGNIFICANCE

These results demonstrated that Ep-AH had excellent therapeutic benefits on cancer remission and gut microbiota modulation. Our study provides an effective strategy for anti-CRC treatment.

Structural basis of bacterial effector protein azurin targeting tumor suppressor p53 and inhibiting its ubiquitination

Tumor suppressor p53 prevents tumorigenesis by promoting cell cycle arrest and apoptosis through transcriptional regulation. Dysfunction of p53 occurs frequently in human cancers. Thus, p53 becomes one of the most promising targets for anticancer treatment. A bacterial effector protein azurin triggers tumor suppression by stabilizing p53 and elevating its basal level. However, the structural and mechanistic basis of azurin-mediated tumor suppression remains elusive. Here we report the atomic details of azurin-mediated p53 stabilization by combining X-ray crystallography with nuclear magnetic resonance. Structural and mutagenic analysis reveals that the p28 region of azurin, which corresponds to a therapeutic peptide, significantly contributes to p53 binding. This binding stabilizes p53 by disrupting COP1-mediated p53 ubiquitination and degradation. Using the structure-based design, we obtain several affinity-enhancing mutants that enable amplifying the effect of azurin-induced apoptosis. Our findings highlight how the structure of the azurin-p53 complex can be leveraged to design azurin derivatives for cancer therapy.

Anticancer activity of Pseudomonas aeruginosa derived peptide with iRGD in colon cancer therapy

Objectives

Colon cancer is well-known as a life-threatening disease. Since the current treatment modalities for this type of cancer are powerful yet face some limitations, finding novel treatments is required to achieve better outcomes with fewer side effects. Here we investigated the therapeutic potential of Azurin-p28 alone or along with iRGD (Ac-CRGDKGPDC-amide) as a tumor-penetrating peptide and 5-fluorouracil (5-FU) for colon cancer.

Materials and Methods

Inhibitory effect of p28 with or without iRGD/5-FU was studied in CT26 and HT29, as well as the xenograft animal model of cancer. The effect of p28 alone or along with iRGD/5-FU on cell migration, apoptotic activity, and cell cycle of the cell lines was assessed. Level of the BAX and BCL2 genes, tumor suppressor genes [(p53 and collagen type-Iα1 (COL1A1), collagen type-Iα2 (COL1A2)] were assessed by quantitative RT-PCR.

Results

These findings show that using p28 with or without iRGD and 5-FU raised the level of p53 and BAX but decreased BCL2, compared with control and 5-FU groups in tissues of the tumor, which result in raising the apoptosis.

Conclusion

It seems that p28 may be used as a new therapeutic approach in colon cancer therapy that can enhance the anti-tumor effect of 5-FU.

Cell-Penetrating Peptides (CPPs) as Therapeutic and Diagnostic Agents for Cancer

Cell-Penetrating Peptides (CPPs) are short peptides consisting of <30 amino acids. Their ability to translocate through the cell membrane while carrying large cargo biomolecules has been the topic of pre-clinical and clinical trials. The ability to deliver cargo complexes through membranes yields potential for therapeutics and diagnostics for diseases such as cancer. Upon cellular entry, some CPPs have the ability to target specific organelles. CPP-based intracellular targeting strategies hold tremendous potential as they can improve efficacy and reduce toxicities and side effects. Further, recent clinical trials show a significant potential for future CPP-based cancer treatment. In this review, we summarize recent advances in CPPs based on systematic searches in PubMed, Embase, Web of Science, and Scopus databases until 30 September 2022. We highlight targeted delivery and explore the potential uses for CPPs as diagnostics, drug delivery, and intrinsic anti-cancer agents.

A Genomics Resource for 12 Edible Seaweeds to Predict Seaweed-Secreted Peptides with Potential Anti-Cancer Function

Seaweeds are multicellular marine macroalgae with natural compounds that have potential anticancer activity. To date, the identification of those compounds has relied on purification and assay, yet few have been documented. Additionally, the genomes and associated proteomes of edible seaweeds that have been identified thus far are scattered among different resources and with no systematic summary available, which hinders the development of a large-scale omics analysis. To enable this, we constructed a comprehensive genomics resource for the edible seaweeds. These data could be used for systematic metabolomics and a proteome search for anti-cancer compound and peptides. In brief, we integrated and annotated 12 publicly available edible seaweed genomes (8 species and 268,071 proteins). In addition, we integrate the new seaweed genomic resources with established cancer bioinformatics pipelines to help identify potential seaweed proteins that could help mitigate the development of cancer. We present 7892 protein domains that were predicted to be associated with cancer proteins based on a protein domain-domain interaction. The most enriched protein families were associated with protein phosphorylation and insulin signalling, both of which are recognised to be crucial molecular components for patient survival in various cancers. In addition, we found 6692 seaweed proteins that could interact with over 100 tumour suppressor proteins, of which 147 are predicted to be secreted proteins. In conclusion, our genomics resource not only may be helpful in exploring the genomics features of these edible seaweed but also may provide a new avenue to explore the molecular mechanisms for seaweed-associated inhibition of human cancer development.

Bacteria-derived chimeric toxins as potential anticancer agents

Cancer is one of the major causes of death globally, requiring everlasting efforts to develop novel, specific, effective, and safe treatment strategies. Despite advances in recent years, chemotherapy, as the primary treatment for cancer, still faces limitations such as the lack of specificity, drug resistance, and treatment failure. Bacterial toxins have great potential to be used as anticancer agents and can boost the effectiveness of cancer chemotherapeutics. Bacterial toxins exert anticancer effects by affecting the cell cycle and apoptotic pathways and regulating tumorigenesis. Chimeric toxins, which are recombinant derivatives of bacterial toxins, have been developed to address the low specificity of their conventional peers. Through their targeting moieties, chimeric toxins can specifically and effectively detect and kill cancer cells. This review takes a comprehensive look at the anticancer properties of bacteria-derived toxins and discusses their potential applications as therapeutic options for integrative cancer treatment.

p53: From Fundamental Biology to Clinical Applications in Cancer

Simple Summary

p53 tumour suppressor gene is the most altered in cancer. Several decades of research have established that it is of pivotal importance in prompting neoplastic phenomena, including cancer initiation and progression. However, it has crucial functions for cellular life. Knowledge and awareness about these multifaceted properties should be part of the cultural background of all scientists. In this review, we describe and discuss the multifaceted roles of p53, from its discovery to clinical applications in cancer therapy.

Abstract

p53 tumour suppressor gene is our major barrier against neoplastic transformation. It is involved in many cellular functions, including cell cycle arrest, senescence, DNA repair, apoptosis, autophagy, cell metabolism, ferroptosis, immune system regulation, generation of reactive oxygen species, mitochondrial function, global regulation of gene expression, miRNAs, etc. Its crucial importance is denounced by the high percentage of amino acid sequence identity between very different species (Homo sapiens, Drosophila melanogaster, Rattus norvegicus, Danio rerio, Canis lupus familiaris, Gekko japonicus). Many of its activities allowed life on Earth (e.g., repair from radiation-induced DNA damage) and directly contribute to its tumour suppressor function. In this review, we provide paramount information on p53, from its discovery, which is an interesting paradigm of science evolution, to potential clinical applications in anti-cancer treatment. The description of the fundamental biology of p53 is enriched by specific information on the structure and function of the protein as well by tumour/host evolutionistic perspectives of its role.

Do Bacteria Provide an Alternative to Cancer Treatment and What Role Does Lactic Acid Bacteria Play?

Cancer is one of the leading causes of mortality and morbidity worldwide. According to 2022 statistics from the World Health Organization (WHO), close to 10 million deaths have been reported in 2020 and it is estimated that the number of cancer cases world-wide could increase to 21.6 million by 2030. Breast, lung, thyroid, pancreatic, liver, prostate, bladder, kidney, pelvis, colon, and rectum cancers are the most prevalent. Each year, approximately 400,000 children develop cancer. Treatment between countries vary, but usually includes either surgery, radiotherapy, or chemotherapy. Modern treatments such as hormone-, immuno- and antibody-based therapies are becoming increasingly popular. Several recent reports have been published on toxins, antibiotics, bacteriocins, non-ribosomal peptides, polyketides, phenylpropanoids, phenylflavonoids, purine nucleosides, short chain fatty acids (SCFAs) and enzymes with anticancer properties. Most of these molecules target cancer cells in a selective manner, either directly or indirectly through specific pathways. This review discusses the role of bacteria, including lactic acid bacteria, and their metabolites in the treatment of cancer.

ACP-check: An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy

The anticancer peptide is an emerging anticancer drug that has become an effective alternative to chemotherapy and targeted therapy due to fewer side effects and resistance. The traditional biological experimental method for identifying anticancer peptides is a time-consuming and complicated process that hinders large-scale, rapid, and effective identification. In this paper, we propose a model based on a bidirectional long short-term memory network and multi-features fusion, called ACP-check, which employs a bidirectional long short-term memory network to extract time-dependent information features from peptide sequences, and combines them with amino acid sequence features including binary profile feature, dipeptide composition, the composition of k-spaced amino acid group pairs, amino acid composition, and sequence-order-coupling number. To verify the performance of the model, six benchmark datasets are selected, including ACPred-Fuse, ACPred-FL, ACP240, ACP740, main and alternate datasets of AntiCP2.0. In terms of Matthews correlation coefficients, ACP-check obtains 0.37, 0.82, 0.80, 0.75, 0.56, and 0.86 on six datasets respectively, which is an improvement by 2%-86% than existing state-of-the-art anticancer peptides prediction methods. Furthermore, ACP-check achieves prediction accuracy with 0.91, 0.91, 0.90, 0.87, 0.78, and 0.93 respectively, which increases range from 1%-49%. Overall, the comparison experiment shows that ACP-check can accurately identify anticancer peptides by sequence-level information. The code and data are available at http://www.cczubio.top/ACP-check/.

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.

Cell-Penetrating Peptides with Unexpected Anti-Amyloid Properties

Cell-penetrating peptides (CPPs) with sequences derived originally from a prion protein (PrP) have been shown to exhibit both anti-prion and anti-amyloid properties particularly against prion proteins and the amyloid-β (Aβ) peptide active in Alzheimer’s disease. These disease-modifying properties are so far observed in cell cultures and in vitro. The CPP sequences are composed of a hydrophobic signal sequence followed by a highly positively charged hexapeptide segment. The original signal sequence of the prion protein can be changed to the signal sequence of the NCAM1 protein without losing the anti-prion activity. Although the detailed molecular mechanisms of these CPP peptides are not fully understood, they do form amyloid aggregates by themselves, and molecular interactions between the CPPs and PrP/Aβ can be observed in vitro using various spectroscopic techniques. These initial intermolecular interactions appear to re-direct the aggregation pathways for prion/amyloid formation to less cell-toxic molecular structures (i.e., co-aggregates), which likely is why the disease-inducing PrP/Aβ aggregation is counteracted in vivo.

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.

Cell-penetrating peptides improve pharmacokinetics and pharmacodynamics of anticancer drugs

This review concentrates on the research concerning conjugates of anticancer drugs with versatile cell-penetrating peptides (CPPs). For a better insight into the relationship between the components of the constructs, it starts with the characteristic of the peptides and considers its following aspects: mechanisms of cellular internalization, interaction with cancer-modified membranes, selectivity against tumor tissue. Also, CPPs with anticancer activity have been distinguished and summarized with their mechanisms of action. With respect to the conjugates, the preclinical studies (in vitro, in vivo) indicated that they possess several merits in comparison to the parent drugs. They concerned not only better cellular internalization but also other improvements in pharmacokinetics (e.g. access to the brain tissue) and pharmacodynamics (e.g. overcoming drug resistance). The anticancer activity of the conjugates was usually superior to that of the unconjugated drug. Certain anticancer CPPs and conjugates entered clinical trials.

The Azurin Coding Gene: Origin and Phylogenetic Distribution

Azurin is a bacterial-derived cupredoxin, which is mainly involved in electron transport reactions. Interest in azurin protein has risen in recent years due to its anticancer activity and its possible applications in anticancer therapies. Nevertheless, the attention of the scientific community only focused on the azurin protein found in Pseudomonas aeruginosa (Proteobacteria, Gammaproteobacteria). In this work, we performed the first comprehensive screening of all the bacterial genomes available in online repositories to assess azurin distribution in the three domains of life. The Azurin coding gene was not detected in the domains Archaea and Eucarya, whereas it was detected in phyla other than Proteobacteria, such as Bacteroidetes, Verrucomicrobia and Chloroflexi, and a phylogenetic analysis of the retrieved sequences was performed. Observed patchy distribution and phylogenetic data suggest that once it appeared in the bacterial domain, the azurin coding gene was lost in several bacterial phyla and/or anciently horizontally transferred between different phyla, even though a vertical inheritance appeared to be the major force driving the transmission of this gene. Interestingly, a shared conserved domain has been found among azurin members of all the investigated phyla. This domain is already known in P. aeruginosa as p28 domain and its importance for azurin anticancer activity has been widely explored. These findings may open a new and intriguing perspective in deciphering the azurin anticancer mechanisms and to develop new tools for treating cancer diseases.

A novel chimeric protein with enhanced cytotoxic effects on breast cancer in vitro and in vivo

In our previous study, we reported the design and recombinant production of the p28-apoptin as a novel chimeric protein for breast cancer (BC) treatment. This study aimed to evaluate the inhibitory activity of the chimeric protein against BC cells in vitro and in vivo. We developed a novel multifunctional protein, consisting of p28, as a tumor-homing killer peptide fused to apoptin as a tumor-selective killer. The chimeric protein showed significantly higher toxicity in BC cell lines dose-dependently than in non-cancerous control cell lines. IC₅₀ values were 1.41, 1.38, 6.13, and 264.49 μM for 4T1, MDA-MB-468, Vero, and HEK293 cells, respectively. The protein showed significantly enhanced uptake in 4T1 cancer cells compared with non-cancerous Vero cells. We also showed that the p28-apoptin chimeric protein binds significantly higher to human breast cancer tumor sections than the normal human breast tissue section. Also, significant apoptosis induction and tumor growth inhibition were observed in established tumor-bearing mice accompanied by a decreased frequency of metastases. Our results support that the chimeric protein has inhibitory activity in vitro and in vivo, making it a promising choice in targeted cancer therapy.

TAT for Enzyme/Protein Delivery to Restore or Destroy Cell Activity in Human Diseases

Much effort has been dedicated in the recent decades to find novel protein/enzyme-based therapies for human diseases, the major challenge of such therapies being the intracellular delivery and reaching sub-cellular organelles. One promising approach is the use of cell-penetrating peptides (CPPs) for delivering enzymes/proteins into cells. In this review, we describe the potential therapeutic usages of CPPs (mainly trans-activator of transcription protein, TAT) in enabling the uptake of biologically active proteins/enzymes needed in cases of protein/enzyme deficiency, concentrating on mitochondrial diseases and on the import of enzymes or peptides in order to destroy pathogenic cells, focusing on cancer cells.

Delivery of Various Cargos into Cancer Cells and Tissues via Cell-Penetrating Peptides: A Review of the Last Decade

Cell-penetrating peptides (CPPs), also known as protein transduction domains, are a class of diverse amino acid sequences with the ability to cross cellular membranes. CPPs can deliver several bioactive cargos, including proteins, peptides, nucleic acids and chemotherapeutics, into cells. Ever since their discovery, synthetic and natural CPPs have been utilized in therapeutics delivery, gene editing and cell imaging in fundamental research and clinical experiments. Over the years, CPPs have gained significant attention due to their low cytotoxicity and high transduction efficacy. In the last decade, multiple investigations demonstrated the potential of CPPs as carriers for the delivery of therapeutics to treat various types of cancer. Besides their remarkable efficacy owing to fast and efficient delivery, a crucial benefit of CPP-based cancer treatments is delivering anticancer agents selectively, rather than mediating toxicities toward normal tissues. To obtain a higher therapeutic index and to improve cell and tissue selectivity, CPP-cargo constructions can also be complexed with other agents such as nanocarriers and liposomes to obtain encouraging outcomes. This review summarizes various types of CPPs conjugated to anticancer cargos. Furthermore, we present a brief history of CPP utilization as delivery systems for anticancer agents in the last decade and evaluate several reports on the applications of CPPs in basic research and preclinical studies.

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

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.

Challenge to overcome current limitations of cell-penetrating peptides

The penetration of biological membranes is a prime obstacle for the delivery of pharmaceutical drugs. Cell-penetrating peptide (CPP) is an efficient vehicle that can deliver various cargos across the biological membranes. Since the discovery, CPPs have been rigorously studied to unveil the underlying penetrating mechanism as well as to exploit CPPs for various biomedical applications. This review will focus on the various strategies to overcome current limitations regarding stability, selectivity, and efficacy of CPPs.