Anticancer peptide: Physicochemical property, functional aspect and trend in clinical application (Review)

Endogenous peptide CBDP1 inhibits clear cell renal cell carcinoma progression by targeting USP5/YTHDF2/TRPM5 axis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) has a high incidence rate and poor prognosis, and currently lacks effective therapies. Recently, peptide-based drugs have shown promise in cancer treatment. In this research, a new endogenous peptide called CBDP1 was discovered in ccRCC and its potential anti-cancer properties were examined.

METHODS

Peptide expression in ccRCC was analyzed using peptidomics technology to screen for potential antitumor peptides. The effects of the peptide on ccRCC growth and migration were studied through Colony Formation Assay, CCK-8 assay, Transwell Assays, Wound Healing Assay, and animal experiments. Further investigation into the antitumor mechanisms of the peptide was conducted using lentivirus transduction, Western Blot Analysis, qRT-PCR, Immunoprecipitation, Immunofluorescence, and Immunohistochemistry.

RESULTS

Our findings reveal that Cathepsin B Derived Peptide 1 (CBDP1) can inhibit the progression of ccRCC both in vitro and in vivo. Through mechanistic investigations, it was revealed that CBDP1 facilitates the interaction between YTHDF2 and the deubiquitinase USP5, thereby impeding the ubiquitination and degradation of YTHDF2. The upregulated YTHDF2 then binds to TRPM3 mRNA and promotes its degradation, ultimately reducing TRPM3 expression levels. These molecular events collectively contribute to the anti-cancer properties of CBDP1.

CONCLUSION

These data indicate that CBDP1 exerts its antitumor effects by regulating the USP5/YTHDF2/TRPM3 axis. CBDP1 emerges as a promising candidate for the treatment of ccRCC.

Conformationally adaptive therapeutic peptides for diseases caused by intrinsically disordered proteins (IDPs). New paradigm for drug discovery: Target the target, not the arrow

The traditional model of protein structure determined by the amino acid sequence is today seriously challenged by the fact that approximately half of the human proteome is made up of proteins that do not have a stable 3D structure, either partially or in totality. These proteins, called intrinsically disordered proteins (IDPs), are involved in numerous physiological functions and are associated with severe pathologies, e.g. Alzheimer, Parkinson, Creutzfeldt-Jakob, amyotrophic lateral sclerosis (ALS), and type 2 diabetes. Targeting these proteins is challenging for two reasons: i) we need to preserve their physiological functions, and ii) drug design by molecular docking is not possible due to the lack of reliable starting conditions. Faced with this challenge, the solutions proposed by artificial intelligence (AI) such as AlphaFold are clearly unsuitable. Instead, we suggest an innovative approach consisting of mimicking, in short synthetic peptides, the conformational flexibility of IDPs. These peptides, which we call adaptive peptides, are derived from the domains of IDPs that become structured after interacting with a ligand. Adaptive peptides are designed with the aim of selectively antagonizing the harmful effects of IDPs, without targeting them directly but through selected ligands, without affecting their physiological properties. This "target the target, not the arrow" strategy is promised to open a new route to drug discovery for currently undruggable proteins.

Anticarcinogenic cationic peptides derived from tryptic hydrolysis of β-lactoglobulin

Introduction

This study investigated the tryptic hydrolysis of β-lactoglobulin (BLG) for 30, 60, 90, and 120 min at 1/200 E/S (enzyme/substrate ratio, w/w) to prepare potentially anticarcinogenic peptides.

Methods

The properties of hydrolysates were characterized, including degree of hydrolysis, free amino acids, SDS-PAGE, FTIR, and antioxidant activity employing DPPH-assay, β-carotene/linoleic acid, and FRAP assay.

Results

BLG tryptic hydrolysate produced after 60 min hydrolysis recorded the highest antioxidant activity, and LCMS analysis revealed 162 peptides of molecular masses ranging from 800 to 5671Da, most of them are of hydrophobic nature. Within the low-MW peptide group (24 peptides), there were nine hydrophobic basic (HB) and seven hydrophobic acidic (HA), representing 38% and 29%, respectively. The HB peptides may be responsible for the considerable biological activity of the hydrolysate. With dominant basic character supporting the carcinogenic activity of this hydrolysate. The in vitro anticancer activity against Mcf-7, Caco-2, and A-549 human cancer cell lines proliferation by MTT assay recorded IC50% at 42.8, 76.92, and 45.93 μg/mL, respectively. Treating each cell line with IC50% of the hydrolysate for 24 h increased the apoptosis by enhancing the expression of caspase-9 by 5.66, 7.97, and 3.28 folds over the untreated control and inhibited angiogenesis by reducing VEGFR-2 expression by about 56, 76, and 70%, respectively, indicating strong anticancer and antiangiogenic actions on human cancer cells. BLG tryptic hydrolysate may serve as a natural anticarcinogenic agent. The results of this study demonstrated that BLG hydrolysates have direct anticancer and antiangiogenic effects on human cancer cells. The chemical composition and characteristics of the BLG tryptic hydrolysate influence these biological and anticancer activities. The tryptic hydrolysates were generally effective against the three cancer cell lines studied (Mcf-7, Caco-2, and A-549). This effectiveness was assessed by measuring cell proliferation using the MTT assay and by evaluating their impact on angiogenesis through inhibition of VEGFR-2 activity.

Discussion

Future studies may focus on enhancing the anticarcinogenic effectiveness of the peptides by isolating and evaluating the most prominent individual peptide and varying the treatment conditions.

Bioactive peptides with potential anticancer properties from various food protein sources: status of recent research, production technologies, and developments

Recently, bioactive peptides, from natural resources, have attracted remarkable attention as nutraceutical treasures and the health benefits of their consumption have extensively been studied. Therapies based on bioactive peptides have been recognized as an innovative and promising alternative method for dangerous diseases such as cancer. Indeed, there has been enormous interest in nutraceuticals and bioactive-based chemopreventive molecules as a potential opportunity to manage chronic diseases, including cancer at different stages, rather than the traditionally used therapies. The relative safety and efficacy of these peptides in targeting only the tumor cells without affecting the normal cells make them attractive alternatives to existing pharmaceuticals for the treatment, management, and prevention of cancer, being able to act as potential physiological modulators of metabolism during their intestinal digestion. Novel bioactive peptides derived from food sources can be beneficial as anticancer nutraceuticals and provide a basis for the pharmaceutical development of food-derived bioactive peptides. Bioactive peptides can be generated through different protein hydrolysis methods and purified using advanced chromatographic techniques. Moreover, establishing bioactive peptides' efficacy and mechanism of action can provide alternative methods for cancer prevention and management. Most of the research on anticancer peptides is carried out on cell lines with very limited research being investigated in animal models or human clinical models. In this context, this review article comprehensively discusses anticancer peptides': production, isolation, therapeutic strategies, mechanism of action, and application in cancer therapy.

Molecular dynamics simulation based prediction of T-cell epitopes for the production of effector molecules for liver cancer immunotherapy

Liver cancer is the sixth most frequent malignancy and the fourth major cause of deaths worldwide. The current treatments are only effective in early stages of cancer. To overcome the therapeutic challenges and exploration of immunotherapeutic options, broad spectral therapeutic vaccines could have significant impact. Based on immunoinformatic and integrated machine learning tools, we predicted the potential therapeutic vaccine candidates of liver cancer. In this study, machine learning and MD simulation-based approach are effectively used to design T-cell epitopes that aid the immune system against liver cancer. Antigenicity, molecular weight, subcellular localization and expression site predictions were used to shortlist liver cancer associated proteins including AMBP, CFB, CDHR5, VTN, APOBR, AFP, SERPINA1 and APOE. We predicted CD8+ T-cell epitopes of these proteins containing LGEGATEAE, LLYIGKDRK, EDIGTEADV, QVDAAMAGR, HLEARKKSK, HLCIRHEMT, LKLSKAVHK, EQGRVRAAT and CD4+ T-cell epitopes of VLGEGATEA, WVTKQLNEI, VEEDTKVNS, FTRINCQGK, WGILGREEA, LQDGEKIMS, VKFNKPFVF, VRAATVGSL. We observed the substantial physicochemical properties of these epitopes with a significant binding affinity with MHC molecules. A polyvalent construct of these epitopes was designed using suitable linkers and adjuvant indicated significant binding energy (>-10.5 kcal/mol) with MHC class-I and II molecule. Based on in silico cloning, we found the considerable compatibility of this polyvalent construct with the E. coli expression system and the efficiency of its translation in host. The system-level and machine learning based cross validations showed the possible effect of these T-cell epitopes as potential vaccine candidates for the treatment of liver cancer.

Recent advances, strategies, and future perspectives of peptide-based drugs in clinical applications

Peptide-based therapies have attracted considerable interest in the treatment of cancer, diabetes, bacterial infections, and neurodegenerative diseases due to their promising therapeutic properties and enhanced safety profiles. This review provides a comprehensive overview of the major trends in peptide drug discovery and development, emphasizing preclinical strategies aimed at improving peptide stability, specificity, and pharmacokinetic properties. It assesses the current applications and challenges of peptide-based drugs in these diseases, illustrating the pharmaceutical areas where peptide-based drugs demonstrate significant potential. Furthermore, this review analyzes the obstacles that must be overcome in the future, aiming to provide valuable insights and references for the continued advancement of peptide-based drugs.

Discovery of anticancer peptides from natural and generated sequences using deep learning

Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However, many predictive methods lack sufficient wet lab validation, thereby constraining the progress of models and impeding the discovery of novel ACPs. This study proposes a comprehensive research strategy by introducing CNBT-ACPred, an ACP prediction model based on a three-channel deep learning architecture, supported by extensive in vitro and in vivo experiments. CNBT-ACPred achieved an accuracy of 0.9554 and a Matthews Correlation Coefficient (MCC) of 0.8602. Compared to existing excellent models, CNBT-ACPred increased accuracy by at least 5 % and improved MCC by 15 %. Predictions were conducted on over 3.8 million sequences from Uniprot, along with 100,000 sequences generated by a deep generative model, ultimately identifying 37 out of 41 candidate peptides from >30 species that exhibited effective in vitro tumor inhibitory activity. Among these, tPep14 demonstrated significant anticancer effects in two mouse xenograft models without detectable toxicity. Finally, the study revealed correlations between the amino acid composition, structure, and function of the identified ACP candidates.

Novel peptides derived from S. hispanica seeds induce selective cytotoxicity on human cancer cells

Cancer prevails as one of the major health concerns worldwide due to the consistent rise in incidence and lack of effective therapies. Previous studies identified the peptides KLKKNL, MLKSKR, and KKYRVF from Salvia hispanica seeds and stated their selective anticancer activity. Thus, this study aimed to determine the cell death pathway induced by these peptides on five cancer cell lines (MCF-7, Caco2, HepG2, DU145, and HeLa). Based on the results of this work, it is possible to suggest that KLKKNL primarily induces selective cancer cell death through the apoptotic pathway in the Caco2 and HeLa lines. On the other hand, the peptide KKYRVF reported the highest statistical (p < 0.05) selective cytotoxic effect on the MCF-7, Caco2, HepG2, and DU145 cancer cell lines by induction of the necrotic pathway. These findings offer some understanding of the selective anticancer effect of KLKKNL, MLKSKR, and KKYRVF.

Recent advances in liposomes and peptide-based therapeutics for glioblastoma treatment

In the context of glioblastoma treatment, the penetration of drugs is drastically limited by the blood-brain-barrier (BBB). Emerging therapies have focused on the field of therapeutic peptides for their excellent BBB targeting properties that promote a deep tumor penetration. Peptide-based strategies are also renowned for their abilities of driving cargo such as liposomal system allowing an active targeting of receptors overexpressed on GBM cells. This review provides a detailed description of the internalization mechanisms of specific GBM homing and penetrating peptides as well as the latest in vitro/in vivo studies of liposomes functionalized with them. The purpose of this review is to summarize a selection of promising pre-clinical results that demonstrate the advantages of this nanosystem, including an increase of tumor cell targeting, triggering drug accumulation and thus a strong antitumor effect. Aware of the early stage of these studies, many challenges need to be overcome to promote peptide-directed liposome at clinical level. In particular, the lack of suitable production, the difficulty to characterize the nanosystem and therapeutic competition leaded by antibodies.

Peptidomics and Machine Learning-based Evaluation of Noncoding RNA-Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. First, we constructed a 16,349 unique putative MP sequence data set by integrating 2 previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

Unnatural Amino Acids: Strategies, Designs, and Applications in Medicinal Chemistry and Drug Discovery

Peptides can operate as therapeutic agents that sit within a privileged space between small molecules and larger biologics. Despite examples of their potential to regulate receptors and modulate disease pathways, the development of peptides with drug-like properties remains a challenge. In the quest to optimize physicochemical parameters and improve target selectivity, unnatural amino acids (UAAs) have emerged as critical tools in peptide- and peptidomimetic-based drugs. The utility of UAAs is illustrated by clinically approved drugs such as methyldopa, baclofen, and gabapentin in addition to small drug molecules, for example, bortezomib and sitagliptin. In this Perspective, we outline the strategy and deployment of UAAs in FDA-approved drugs and their targets. We further describe the modulation of the physicochemical properties in peptides using UAAs. Finally, we elucidate how these improved pharmacological parameters and the role played by UAAs impact the progress of analogs in preclinical stages with an emphasis on the role played by UAAs.

iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention

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

Anticancer effects of pomegranate-derived peptide PG2 on CDK2 and miRNA-339-5p-mediated apoptosis via extracellular vesicles in acute leukemia

Acute leukemia has rapid onset and severe complications. Anticancer peptides from natural sources have demonstrated efficacy in eliminating various cancers through apoptosis signaling pathways. Additionally, extracellular vesicles containing microRNAs play pivotal roles in promoting tumorigenesis. Therefore, this study aimed to investigate the impact of PG2, a pomegranate peptide that regulates extracellular vesicles, on the induction of acute leukemia cell apoptosis. NB4 and MOLT-4 leukemia cell lines were treated with PG2 alone or in combination with daunorubicin to assess cell viability using the MTT assay. Extracellular vesicles were extracted from PG2-treated NB4 and MOLT-4 cells. Bioinformatic tools were utilized to predict target proteins and microRNAs, following which mRNA and protein expression were determined by using RT‒qPCR and western blotting, respectively. PG2 significantly reduced the viability of NB4 and MOLT-4 cells. Furthermore, the combination of PG2 with daunorubicin had a synergistic effect on NB4 and MOLT-4 cells. Subsequent treatment with PG2 or PG2-treated extracellular vesicles decreased CDK2 expression while increasing microRNA-339-5p and caspase-3 expression in NB4 and MOLT-4 cells. Our findings revealed that the anticancer activity of PG2 through the CDK2/miR-339-5p/caspase-3 pathway is mediated by extracellular vesicles, ultimately inducing apoptosis. PG2 holds promise as a potential antileukemic drug.

Moringa oleifera Lam. Leaf Peptides: Antioxidant and Antiproliferative Activity in Human Colon Cancer Caco-2 Cell Line

Reactive oxygen species are produced as part of the cellular metabolism. However, lifestyle can promote an excess in their concentration. Free radicals react with DNA, promoting the appearance of cancer cells. Therefore, natural antioxidants have been suggested as an alternative to prevent this disorder. Peptides are protein fragments that have been produced from various plants. In previous work, Moringa oleifera leaf peptides (MOPHs) with antioxidant potential were generated and identified. However, the spectrophotometric methods used to evaluate their antioxidant activity do not fully reflect its potential. In this work, the antioxidant activity of MOPHs was assessed by the ferric reducing antioxidant power assay (FRAP) and cellular antioxidant activity method on the human colon cancer cell line Caco-2. Also, their antiproliferative activity was evaluated. The MOPHs exhibited a FRAP activity of 1435 µmol TE/g, and at 500 µg/mL; the peptides did not show a cytotoxic effect on healthy colon CCD-18Co cells. Moreover, the MOPHs increased Caco-2 antioxidative activity to a greater extent by 73.45% and 83.62% at 250 and 500 µg/mL, respectively. Regarding cellular proliferation, the MOPHs inhibited it by 78.19% and 90.20% at 200 and 500 µg/mL, respectively. These findings highlight the potential of Moringa oleifera leaf peptides as functional ingredients with significant health benefits, demonstrating antioxidant and antiproliferative properties.

Cytotoxic Effects of Plant Secondary Metabolites and Naturally Occurring Bioactive Peptides on Breast Cancer Model Systems: Molecular Mechanisms

Breast cancer is the second leading cause of death among women, and the number of mortal cases in diagnosed patients is constantly increasing. The search for new plant compounds with antitumor effects is very important because of the side effects of conventional therapy and the development of drug resistance in cancer cells. The use of plant substances in medicine has been well known for centuries, but the exact mechanism of their action is far from being elucidated. The molecular mechanisms of cytotoxicity exerted by secondary metabolites and bioactive peptides of plant origin on breast cancer cell lines are the subject of this review.

Soybean β-Conglycinin and Cowpea β-Vignin Peptides Inhibit Breast and Prostate Cancer Cell Growth: An In Silico and In Vitro Approach

B-cell lymphoma 2 protein (Bcl-2) is an important regulator of cell apoptosis. Inhibitors that mirror the structural domain 3 (BH3) of Bcl-2 can activate apoptosis in cancer cells, making them a promising target for anticancer treatment. Hence, the present study aimed to investigate potential BH3-mimetic peptides from two vicilin-derived legume proteins from soybean and cowpea bean. The proteins were isolated and sequentially hydrolyzed with pepsin/pancreatin. Peptides < 3 kDa from vicilin-derived proteins from soybean and cowpea beans experimentally inhibited the growth of cultivated breast and prostate cancer cells. In silico analysis allowed the identification of six potential candidates, all predicted to be able to interact with the BH3 domain. The VIPAAY peptide from the soybean β-conglycinin β subunit showed the highest potential to interact with Bcl-2, comparable to Venetoclax, a well-known anticancer drug. Further experiments are needed to confirm this study's findings.

Facile Peptide Macrocyclization and Multifunctionalization via Cyclen Installation

Cyclen-peptide bioconjugates are usually prepared in multiple steps that require individual preparation and purification of the cyclic peptide and hydrophilic cyclen derivatives. An efficient strategy is discovered for peptide cyclization and functionalization toward lanthanide probe via three components intermolecular crosslinking on solid-phase peptide synthesis with high conversion yield. Multifunctionality can be conferred by introducing different modular parts or/and metal ions on the cyclen-embedded cyclopeptide. As a proof-of-concept, a luminescent Eu3+ complex and a Gd3+-based contrasting agent for in vitro optical imaging and in vivo magnetic resonance imaging, respectively, are demonstrated through utilizing this preparation of cyclen-embedded cyclic arginylglycylaspartic acid (RGD) peptide.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Design anticancer potential of Zn(II)isoleucinedithiocarbamate complex on MCF-7 cell lines: synthesis, characterization, molecular docking, molecular dynamic, ADMET, and in-vitro studies

Cisplatin is a cancer medication widely used today, but it still poses some problems due to its toxic properties in the body. To overcome this issue, a new complex has been developed as a potential anticancer drug prospect by minimizing its toxic consequences. A novel Zn(II)IleDTC complex containing isoleucine dithiocarbamate ligands has been produced and analyzed using a range of analytical and spectroscopic methods. The Zn(II) IleDTC complex were characterized using various methods, including UV-Vis spectroscopy, FT-IR, determination of melting point, conductivity, and HOMO-LUMO analysis. Furthermore, computational NMR spectrum analysis was conducted in this study. Molecular docking studies was conducted to evaluate the potential of Zn(II) isoleucine dithiocarbamate as an HIF1 inhibitor. The results showed that the Zn complex exhibited a good docking score of -6.6 and formed hydrogen bonds with ARG 17, VAL264, and GLU15, alkyl bonds with TRP27 and LEU32, and Pi-Alkyl bonds with PRO41 and ARG44. This suggests that the Zn(II) isoleucine dithiocarbamate complex could be a promising candidate for cancer treatment with potential HIF1 inhibition properties. To assess the dynamic stability and efficacy of protein-ligand interactions over time, molecular dynamics simulations was conducted for both individual proteins and protein complexes. The cytotoxicity evaluation of Zn(II) isoleucine dithiocarbamate against MCF-7 cells obtained an IC50 value of 362.70 µg/mL indicating moderate cytotoxicity and morphological changes of cancer cells causing cancer cells to undergo apoptosis. The Zn(II) isoleucine dithiocarbamate complex may have promising potential as an anticancer compound due to its significant inhibitory effect on the breast cancer cell line (MCF7). According to the ADMET study, the complex exhibits drug-like characteristics with low toxicity, further supporting its potential as a viable drug candidate.

Plant-Derived as Alternatives to Animal-Derived Bioactive Peptides: A Review of the Preparation, Bioactivities, Structure-Activity Relationships, and Applications in Chronic Diseases

Background/Objectives: At present, a large number of bioactive peptides have been found from plant sources with potential applications for the prevention of chronic diseases. By promoting plant-derived bioactive peptides (PDBPs), we can reduce dependence on animals, reduce greenhouse gas emissions, and protect the ecological environment. Methods: In this review, we summarize recent advances in sustainably sourced PDBPs in terms of preparation methods, biological activity, structure-activity relationships, and their use in chronic diseases. Results: Firstly, the current preparation methods of PDBPs were summarized, and the advantages and disadvantages of enzymatic method and microbial fermentation method were introduced. Secondly, the biological activities of PDBPs that have been explored are summarized, including antioxidant, antibacterial, anticancer and antihypertensive activities. Finally, based on the biological activity, the structure-activity relationship of PDBPs and its application in chronic diseases were discussed. All these provide the foundation for the development of PDBPs. However, the study of PDBPs still has some limitations. Conclusions: Overall, PDBPs is a good candidate for the prevention and treatment of chronic diseases in humans. This work provides important information for exploring the source of PDBPs, optimizing its biological activity, and accurately designing functional foods or drugs.

ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information

Peptide drugs have demonstrated enormous potential in treating a variety of diseases, yet toxicity prediction remains a significant challenge in drug development. Existing models for prediction of peptide toxicity largely rely on sequence information and often neglect the three-dimensional (3D) structures of peptides. This study introduced a novel model for short peptide toxicity prediction, named ToxGIN. The model utilizes Graph Isomorphism Network (GIN), integrating the underlying amino acid sequence composition and the 3D structures of peptides. ToxGIN comprises three primary modules: (i) Sequence processing module, converting peptide 3D structures and sequences into information of nodes and edges; (ii) Feature extraction module, utilizing GIN to learn discriminative features from nodes and edges; (iii) Classification module, employing a fully connected classifier for toxicity prediction. ToxGIN performed well on the independent test set with F1 score = 0.83, AUROC = 0.91, and Matthews correlation coefficient = 0.68, better than existing models for prediction of peptide toxicity. These results validated the effectiveness of integrating 3D structural information with sequence data using GIN for peptide toxicity prediction. The proposed ToxGIN and data can be freely accessible at https://github.com/cihebiyql/ToxGIN.

Epidermal Growth Factor Receptor (EGFR) Inhibitors Screened from Autodisplayed Fv-Antibody Library

Inhibitors of the epithermal growth factor receptor (EGFR) were screened from an autodisplayed Fv-antibody library using an anti-EGF antibody. The Fv-antibody library was expressed on the outer membrane of Escherichia coli, which corresponds to the heavy chain VH region of immunoglobulin G. The library was constructed by randomizing the CDR3 region of expressed VH regions (11 amino acid residues) by site-directed mutagenesis. Using an anti-EGF antibody as a screening probe, amino acid sequences (CDR3 region) with antibody binding affinity were screened from the Fv-antibody library. These amino acid sequences were considered to have similar chemical properties to EGF, which can bind to EGFR. Two autodisplayed clones with Fv-antibodies against EGFR were screened from the Fv-antibody library, and the screened Fv-antibodies were expressed as soluble proteins. The binding affinity (KD) was estimated using an SPR biosensor, and the inhibitory activity of expressed Fv-antibodies was observed for PANC-1 pancreatic tumor cells and T98G glioblastoma cells using Western blot analysis of proteins in the EGFR-mediated signaling pathway. The viability of PANC-1 and T98G cells was observed to decrease via the inhibitory activity of expressed Fv-antibodies. Finally, interactions between Fv-antibodies and EGFR were analyzed by using molecular docking simulations.

Dual folate/biotin-decorated liposomes mediated delivery of methylnaphthazarin for anti-cancer activity

Chemotherapy is an effective strategy for mitigating the global challenge of cancer treatment, which often encounters drug resistance and negative side effects. Methylnaphthazarin (MNZ), a natural compound with promising anti-cancer properties, has been underexplored due to its poor aqueous solubility and low selectivity. This study introduces a novel approach to overcome these limitations by developing MNZ-encapsulating liposomes decorated with folate and biotin (F/B-LP-MNZ). This dual-targeting strategy aims to enhance the anti-cancer efficacy and specificity of MNZ delivery. Our innovative F/B-LP-MNZ formulation demonstrated excellent physicochemical properties, stability, and controlled drug release profiles. In vitro studies revealed that MNZ-loaded liposomes attenuate the toxicity associated with free MNZ while F/B-LP-MNZ significantly increased cytotoxicity against HeLa cells, which express high levels of folate and biotin receptors, compared to non-targeted liposomes. Enhanced cellular uptake and improved dynamic flow attachment further confirmed the superior specificity of F/B-LP in targeting cancer cells. Additionally, our results revealed that F/B-LP-MNZ effectively inhibits HeLa cell migration and adhesion through EMT suppression and apoptotic induction, indicating its potential to prevent cancer metastasis. These findings highlight the potential of dual folate and biotin receptors-targeting liposomes as an effective delivery system for MNZ, offering a promising new avenue for targeted cancer therapy.

Enhanced efficacy of combined VEGFR peptide-drug conjugate and anti-PD-1 antibody in treating hepatocellular carcinoma

This study aimed to design a VEGFR-targeting peptide-drug conjugate with the ability to decrease tumor burden and suppress tumor angiogenesis, and to further evaluate the therapeutic effect of anti-PD-1 antibody in HCC therapy. A VEGFR-targeting peptide VEGF125 - 136 (QR) was conjugated with a lytic peptide (KLU) to form a peptide-drug conjugate QR-KLU. And the efficacy of QR-KLU in combination with anti-PD-1 antibody for HCC therapy in vivo and in vitro were evaluated. QR-KLU inhibited the proliferation and migration of mouse HCC cell line (Hepa1-6) cells under normoxic and hypoxic conditions in a dose-dependent manner. In the subcutaneous Hepa1-6 tumor model, QR-KLU combined with the anti-PD-1 antibody substantially inhibited tumor growth, promoted tumor necrosis, and prolonged the survival time of tumor-bearing mice. QR-KLU substantially inhibited hypoxia-induced expression of VEGF, promoted tumor vascular normalization, and increased cluster of differentiation 8+ (CD8+) T cell infiltration in the tumor. In addition, QR-KLU and anti-PD-1 antibody demonstrated a strong synergistic effect in promoting the activation of intratumoral CD8+ T cells, reducing the expression of immune-inhibitory factors, and increasing the expression of immune-stimulatory factors. This study proposed a novel approach for enhancing the efficacy of anti-PD-1 antibody using a VEGFR-targeting peptide-drug conjugate in HCC therapy.

Jackfruit Leaf Protein Hydrolysates Obtained by Enzymatic Hydrolysis of Leaf Protein Concentrate with Pepsin and Pancreatin: Molecular Weight, Cytotoxicity, Antiproliferative Activity, and Oxidative Stress

Jackfruit leaf protein hydrolysates obtained from the enzymatic hydrolysis of leaf protein concentrate with gastrointestinal enzymes have shown good techno-functional properties and high antioxidant capacity. However, molecular weight, antiproliferative activity, cytotoxicity and the ability to reduce reactive oxygen species (ROS) are still unknown. Therefore, this study aimed to evaluate the effect of jackfruit leaf protein hydrolysates obtained by enzymatic hydrolysis with pepsin and pancreatin at different hydrolysis times (30-240 min) on molecular weights, cytotoxicity, antiproliferation of cancer cells, and the reduction of reactive oxygen species in H2O2-induced HaCaT cells. The electrophoretic profile indicated that H-Pep contains peptides with molecular weights between 25 - 20 kDa. Meanwhile, H-Pan is composed of molecular weight products between 25 - 20 kDa and < 20 kDa. H-Pan and H-Pep (125-500 µg/mL) did not show significant cytotoxicity on HaCaT (human keratinocytes) and J774A.1 (murine macrophage cells). Antiproliferative activity was achieved in human cervical, ovarian, and liver cancer cells. H-Pan-240 min (1000 µg/mL) reduced the cell viability of cervical cancer cells by 23% while H-Pan-60 min significantly reduced cell viability of ovarian and liver cancer cells by 14.5 (500 µg/mL) and 17% (1000 µg/mL), respectively (P < 0.05). The protective effect against oxidative stress on H2O2-stressed HaCaT cells was obtained with H-Pep-60 min, which reduced 25% of ROS at 250 µg/mL (P < 0.05). The findings demonstrate the safe use of green biomass as a source of plant protein hydrolysates.

Synergistic collaboration between AMPs and non-direct antimicrobial cationic peptides

Non-direct antimicrobial cationic peptides (NDACPs) are components of the animal innate immune system. But their functions and association with antimicrobial peptides (AMPs) are incompletely understood. Here, we reveal a synergistic interaction between the AMP AW1 and the NDACP AW2, which are co-expressed in the frog Amolops wuyiensis. AW2 enhances the antibacterial activity of AW1 both in vitro and in vivo, while mitigating the development of bacterial resistance and eradicating biofilms. AW1 and AW2 synergistically damage bacterial membranes, facilitating cellular uptake and interaction of AW2 with the intracellular target bacterial genomic DNA. Simultaneously, they trigger the generation of ROS in bacteria, contributing to cell death upon reaching a threshold level. Moreover, we demonstrate that this synergistic antibacterial effect between AMPs and NDACPs is prevalent across diverse animal species. These findings unveil a robust and previously unknown correlation between AMPs and NDACPs as a widespread antibacterial immune defense strategy in animals.

Anticancer peptides as novel immunomodulatory therapeutic candidates for cancer treatment

Cancer remains a concern after years of research in this field. Conventional therapies such as chemotherapy, radiation, and surgery are available for cancer treatment, but they are characterized by various side effects. There are several immunological challenges that make it difficult for the immune system and conventional therapies to treat cancer. Some of these challenges include heterogeneity, resistance to medicines, and cancer relapse. Even advanced treatments like immune checkpoint inhibitors (ICIs), which revolutionized cancer treatment, have associated toxicity and resistance further necessitate the exploration of alternative therapies. Anticancer peptides (ACPs) offer promising potential as cancer-fighting agents and address challenges such as treatment resistance, tumor heterogeneity, and metastasis. Although these peptides exist as components of the defense system in various plants, animals, fungi, etc., but can also be created synthetically and used as a new treatment measure. These peptides possess properties that make them appealing for cancer therapy, such as apoptosis induction, inhibition of angiogenesis, and cell membrane breakdown with low toxicity. Their capacity to specifically target cancer cells selectively holds promise for enhancing treatment environments as well as improving patients' quality of life. This review provides detailed insights into the different prospects of ACPs, including their characterization, use as immunomodulatory agents in cancer treatment, and their mechanistic details after addressing various immunological challenges in existing cancer treatment strategies. In conclusion, ACPs have promising potential as novel cancer therapeutics due to their target specificity and fewer side effects than conventional therapies.

In silico analysis of the interaction of de novo peptides derived from Salvia hispanica with anticancer targetsEvaluation of the anticancer potential of de novo peptides derived from Salvia hispanica through molecular docking

Cancer is one of the leading causes of death worldwide. Conventional cancer therapies are not selective to cancer cells resulting in serious side effects on patients. Thus, the need for complementary treatments that improve the patient's response to cancer therapy is highly important. To predict and evaluate the physicochemical characteristics and potential anticancer activity of the peptides identified from S. hispanica protein fraction <1 kDa through the use of in silico tools. Peptides derived from Salvia hispanica's protein fraction <1 kDa were identified and analyzed for the prediction of their physicochemical properties. The characterized peptide sequences were then submitted to a multi-criteria decision analysis to identify the peptides that possess the characteristics to potentially exert anticancer activity. Through molecular docking analysis, the potential anticancer activity of the Potentially Anticancer Peptide (PAP)-1, PAP-2, PAP-3, PAP-4, and PAP-5 was estimated by their binding interactions with cancer and apoptosis-related molecules. All five evaluated PAPs exhibited strong binding interactions (< -100 kcal/mol). However, PAP-3 showed the lowest binding free energies with several of the targets. Thus, PAP-3 shows potential to be used as a nutraceutical or ingredient for functional foods that adjuvate in cancer treatment. Conclusions: Through the molecular docking studies, the binding of the PAPs to target molecules of interest for cancer treatment was successfully simulated, from which PAP-3 exhibited the lowest binding free energies. Further in vitro and in vivo studies are required to validate the predictions obtained by the in silico analysis.Communicated by Ramaswamy H. Sarma.

A Lipid-Sensitive Spider Peptide Toxin Exhibits Selective Anti-Leukemia Efficacy through Multimodal Mechanisms

Anti-cancer peptides (ACPs) represent a promising potential for cancer treatment, although their mechanisms need to be further elucidated to improve their application in cancer therapy. Lycosin-I, a linear amphipathic peptide isolated from the venom of Lycosa singorensis, shows significant anticancer potential. Herein, it is found that Lycosin-I, which can self-assemble into a nanosphere structure, has a multimodal mechanism of action involving lipid binding for the selective and effective treatment of leukemia. Mechanistically, Lycosin-I selectively binds to the K562 cell membrane, likely due to its preferential interaction with negatively charged phosphatidylserine, and rapidly triggers membrane lysis, particularly at high concentrations. In addition, Lycosin-I induces apoptosis, cell cycle arrest in the G1 phase and ferroptosis in K562 cells by suppressing the PI3K-AKT-mTOR signaling pathway and activating cell autophagy at low concentrations. Furthermore, intraperitoneal injection of Lycosin-I inhibits tumor growth of K562 cells in a nude mouse xenograft model without causing side effects. Collectively, the multimodal effect of Lycosin-I can provide new insights into the mechanism of ACPs, and Lycosin-I, which is characterized by high potency and specificity, can be a promising lead for the development of anti-leukemia drugs.

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Unlocking the potential of protein-derived peptides to target G-quadruplex DNA: from recognition to anticancer activity

Noncanonical nucleic acid structures, particularly G-quadruplexes, have garnered significant attention as potential therapeutic targets in cancer treatment. Here, the recognition of G-quadruplex DNA by peptides derived from the Rap1 protein is explored, with the aim of developing novel peptide-based G-quadruplex ligands with enhanced selectivity and anticancer activity. Biophysical techniques were employed to assess the interaction of a peptide derived from the G-quadruplex-binding domain of the protein with various biologically relevant G-quadruplex structures. Through alanine scanning mutagenesis, key amino acids crucial for G-quadruplex recognition were identified, leading to the discovery of two peptides with improved G-quadruplex-binding properties. However, despite their in vitro efficacy, these peptides showed limited cell penetration and anticancer activity. To overcome this challenge, cell-penetrating peptide (CPP)-conjugated derivatives were designed, some of which exhibited significant cytotoxic effects on cancer cells. Interestingly, selected CPP-conjugated peptides exerted potent anticancer activity across various tumour types via a G-quadruplex-dependent mechanism. These findings underscore the potential of peptide-based G-quadruplex ligands in cancer therapy and pave the way for the development of novel therapeutic strategies targeting these DNA structures.

Androcin 18-1, a novel scorpion-venom peptide, shows a potent antitumor activity against human U87 cells via inducing mitochondrial dysfunction

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

Biological Activity of Natural and Synthetic Peptides as Anticancer Agents

Cancer is one of the leading causes of morbidity and death worldwide, making it a serious global health concern. Chemotherapy, radiotherapy, and surgical treatment are the most used conventional therapeutic approaches, although they show several side effects that limit their effectiveness. For these reasons, the discovery of new effective alternative therapies still represents an enormous challenge for the treatment of tumour diseases. Recently, anticancer peptides (ACPs) have gained attention for cancer diagnosis and treatment. ACPs are small bioactive molecules which selectively induce cancer cell death through a variety of mechanisms such as apoptosis, membrane disruption, DNA damage, immunomodulation, as well as inhibition of angiogenesis, cell survival, and proliferation pathways. ACPs can also be employed for the targeted delivery of drugs into cancer cells. With over 1000 clinical trials using ACPs, their potential for application in cancer therapy seems promising. Peptides can also be utilized in conjunction with imaging agents and molecular imaging methods, such as MRI, PET, CT, and NIR, improving the detection and the classification of cancer, and monitoring the treatment response. In this review we will provide an overview of the biological activity of some natural and synthetic peptides for the treatment of the most common and malignant tumours affecting people around the world.

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

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

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

Tumor-Homing Peptides as Crucial Component of Magnetic-Based Delivery Systems: Recent Developments and Pharmacoeconomical Perspective

According to data from the World Health Organization (WHO), cancer is considered to be one of the leading causes of death worldwide, and new therapeutic approaches, especially improved novel cancer treatment regimens, are in high demand. Considering that many chemotherapeutic drugs tend to have poor pharmacokinetic profiles, including rapid clearance and limited on-site accumulation, a combined approach with tumor-homing peptide (THP)-functionalized magnetic nanoparticles could lead to remarkable improvements. This is confirmed by an increasing number of papers in this field, showing that the on-target peptide functionalization of magnetic nanoparticles improves their penetration properties and ensures tumor-specific binding, which results in an increased clinical response. This review aims to highlight the potential applications of THPs in combination with magnetic carriers across various fields, including a pharmacoeconomic perspective.

DCTPep, the data of cancer therapy peptides

With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.

Molecular and functional insight into anti-EGFR nanobody: Theranostic implications for malignancies

Targeted therapy and imaging are the most popular techniques for the intervention and diagnosis of cancer. A potential therapeutic target for the treatment of cancer is the epidermal growth factor receptor (EGFR), primarily for glioblastoma, lung, and breast cancer. Over-production of ligand, transcriptional up-regulation due to autocrine/paracrine signalling, or point mutations at the genomic locus may contribute to the malfunction of EGFR in malignancies. This exploit makes use of EGFR, an established biomarker for cancer diagnostics and treatment. Despite considerable development in the last several decades in making EGFR inhibitors, they are still not free from limitations like toxicity and a short serum half-life. Nanobodies and antibodies share similar binding properties, but nanobodies have the additional advantage that they can bind to antigenic epitopes deep inside the target that conventional antibodies are unable to access. For targeted therapy, anti-EGFR nanobodies can be conjugated to various molecules such as drugs, peptides, toxins and photosensitizers. These nanobodies can be designed as novel immunoconjugates using the universal modular antibody-based platform technology (UniCAR). Furthermore, Anti-EGFR nanobodies can be expressed in neural stem cells and visualised by effective fluorescent and radioisotope labelling.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

Integrated convolution and self-attention for improving peptide toxicity prediction

MOTIVATION

Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development.

RESULTS

We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs.

AVAILABILITY AND IMPLEMENTATION

The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.

Cationic nanocarriers: A potential approach for targeting negatively charged cancer cell

Cancer, a widespread and lethal disease, necessitates precise therapeutic interventions to mitigate its devastating impact. While conventional chemotherapy remains a cornerstone of cancer treatment, its lack of specificity towards cancer cells results in collateral damage to healthy tissues, leading to adverse effects. Thus, the quest for targeted strategies has emerged as a critical focus in cancer research. This review explores the development of innovative targeting methods utilizing novel drug delivery systems tailored to recognize and effectively engage cancer cells. Cancer cells exhibit morphological and metabolic traits, including irregular morphology, unchecked proliferation, metabolic shifts, genetic instability, and a higher negative charge, which serve as effective targeting cues. Central to these strategies is the exploitation of the unique negative charge characteristic of cancer cells, attributed to alterations in phospholipid composition and the Warburg effect. Leveraging this distinct feature, researchers have devised cationic carrier systems capable of enhancing the specificity of therapeutic agents towards cancer cells. The review delineates the underlying causes of the negative charge in cancer cells and elucidates various targeting approaches employing cationic compounds for drug delivery systems. Furthermore, it delves into the methods employed for the preparation of these systems. Beyond cancer treatment, the review also underscores the multifaceted applications of cationic carrier systems, encompassing protein and peptide delivery, imaging, photodynamic therapy, gene delivery, and antimicrobial applications. This comprehensive exploration underscores the potential of cationic carrier systems as versatile tools in the fight against cancer and beyond.

CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only

Anticancer peptides (ACPs) recently have been receiving increasing attention in cancer therapy due to their low consumption, few adverse side effects, and easy accessibility. However, it remains a great challenge to identify anticancer peptides via experimental approaches, requiring expensive and time-consuming experimental studies. In addition, traditional machine-learning-based methods are proposed for ACP prediction mainly depending on hand-crafted feature engineering, which normally achieves low prediction performance. In this study, we propose CACPP (Contrastive ACP Predictor), a deep learning framework based on the convolutional neural network (CNN) and contrastive learning for accurately predicting anticancer peptides. In particular, we introduce the TextCNN model to extract the high-latent features based on the peptide sequences only and exploit the contrastive learning module to learn more distinguishable feature representations to make better predictions. Comparative results on the benchmark data sets indicate that CACPP outperforms all the state-of-the-art methods in the prediction of anticancer peptides. Moreover, to intuitively show that our model has good classification ability, we visualize the dimension reduction of the features from our model and explore the relationship between ACP sequences and anticancer functions. Furthermore, we also discuss the influence of data set construction on model prediction and explore our model performance on the data sets with verified negative samples.

Computational studies and synthesis of 131iodine-labeled nocardiotide A analogs as a peptide-based theragnostic radiopharmaceutical ligand for cancer targeting SSTR2

Radiolabeled peptides belong to a highly specific group of radiotracers used in oncology, particularly for diagnostics and cancer therapy. With the notable advantages of high binding affinity and selectivity to cancer cells, they have proven to be very useful in nuclear medicine. As a result, efforts have been focused on discovering new peptide sequences for radiopeptide preparation. Nocardiotide A, a cyclic hexapeptide comprising the amino acids cyclo-Trp-Ile-Trp-Leu-Val-Ala (cWIWLVA) isolated from Nocardiopsis sp., has shown significant cytotoxicity against cancer cells, rendering it a suitable candidate for the process. Therefore, the present study aimed to design a stable and effective radiopeptide by labeling nocardiotide A with iodine-131 (131I), ensuring that its affinity to SSTR2 is not compromised. In silico study showed that structural modification of nocardiotide A labeled with 131iodine exhibited good affinity value, forming hydrogen bonds with key residues, such as Q.102 and T.194, which are essential in SSTR2. Based on the results, cyclic hexapeptides of cWIWLYA were selected for further synthesis, and its peptide product was confirmed by the presence of an ionic molecule peak m/z [M + Na]+ 855.4332 (yield, 25.60%). In vitro tests conducted on cWIWLYA showed that cWIWLYA can bind to HeLa cancer cells. Radiopeptide synthesis was initiated with radiolabeling of cWIWLYA by 131I using the chloramine-T method that showed a radiochemical yield of 93.37%. Non-radioactive iodine labeling reaction showed that iodination was successful, which detected the presence of di-iodinated peptide (I2-cWIWLYA) with m/z [M + Na]+ 1107.1138. In summary, a radiopeptide derived from nocardiotide A showed great potential for further development as a diagnostic and therapeutic agent in cancer treatment.

Chemokine-derived oncolytic peptide induces immunogenic cancer cell death and significantly suppresses tumor growth

Chemokinostatin-1 (CKS1) is a 24-mer peptide originally discovered as an anti-angiogenic peptide derived from the CXCL1 chemokine. Here, we demonstrate that CKS1 acts not only as an anti-angiogenic peptide but also as an oncolytic peptide due to its structural and physical properties. CKS1 induced both necrotic and apoptotic cell death specifically in cancer cells while showing minimal toxicity in non-cancerous cells. Mechanistically, CKS1 disrupted the cell membrane of cancer cells quickly after treatment and activated the apoptotic pathway at later time points. Furthermore, immunogenic molecules were released from CKS1-treated cells, indicating that CKS1 induces immunogenic cell death. CKS1 effectively suppressed tumor growth in vivo. Collectively, these data demonstrate that CKS1 functions as an oncolytic peptide and has a therapeutic potential to treat cancer.

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.

Recent advances in antimicrobial peptide-based therapy

Antimicrobial peptides (AMPs) are a group of polypeptide chains that have the property to target and kill a myriad of microbial organisms including viruses, bacteria, protists, etc. The first discovered AMP was named gramicidin, an extract of aerobic soil bacteria. Further studies discovered that these peptides are present not only in prokaryotes but in eukaryotes as well. They play a vital role in human innate immunity and wound repair. Consequently, they have maintained a high level of intrigue among scientists in the field of immunology, especially so with the rise of antibiotic-resistant pathogens decreasing the reliability of antibiotics in healthcare. While AMPs have promising potential to substitute for common antibiotics, their use as effective replacements is barred by certain limitations. First, they have the potential to be cytotoxic to human cells. Second, they are unstable in the blood due to action by various proteolytic agents and ions that cause their degradation. This review provides an overview of the mechanism of AMPs, their limitations, and developments in recent years that provide techniques to overcome those limitations. We also discuss the advantages and drawbacks of AMPs as a replacement for antibiotics as compared to other alternatives such as synthetically modified bacteriophages, traditional medicine, and probiotics.

Precisely Activating cGAS-STING Pathway with a Novel Peptide-Based Nanoagonist to Potentiate Immune Checkpoint Blockade Cancer Immunotherapy

As an essential intracellular immune activation pathway, the cGAS-STING pathway has attracted broad attention in cancer treatment. However, low bioavailability, nonspecificity, and adverse effects of small molecule STING agonists severely limit their therapeutic efficacy and in vivo application. In this study, a peptide-based STING agonist is first proposed, and KLA is screened out to activate the cGAS-STING pathway by promoting mitochondrial DNA (mtDNA) leakage. To precisely activate the cGAS-STING pathway and block the PD-1/PD-L1 pathway, a multi-stimuli activatable peptide nanodrug (MAPN) is developed for the effective delivery of KLA and PD-L1 antagonist peptide (CVR). With rational design, MAPN achieved the site-specific release of KLA and CVR in response to multiple endogenous stimuli, simultaneously activating the cGAS-STING pathway and blocking PD-1/PD-L1 pathway, ultimately initiating robust and durable T cell anti-tumor immunity with a tumor growth inhibition rate of 78% and extending the median survival time of B16F10 tumor-bearing mice to 40 days. Overall, antimicrobial peptides, which can promote mtDNA leakage through damaging mitochondrial membranes, may be potential alternatives for small molecule STING agonists and giving a new insight for the design of novel STING agonists. Furthermore, MAPN presents a universal delivery platform for the effective synergy of multiple peptides.

Contrastive learning for enhancing feature extraction in anticancer peptides

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

Theranostic applications of peptide-based nanoformulations for growth factor defective cancers

Growth factors play a pivotal role in orchestrating cellular growth and division by binding to specific cell surface receptors. Dysregulation of growth factor production or activity can contribute to the uncontrolled cell proliferation observed in cancer. Peptide-based nanoformulations (PNFs) have emerged as promising therapeutic strategies for growth factor-deficient cancers. PNFs offer multifaceted capabilities including targeted delivery, imaging modalities, combination therapies, resistance modulation, and personalized medicine approaches. Nevertheless, several challenges remain, including limited specificity, stability, pharmacokinetics, tissue penetration, toxicity, and immunogenicity. To address these challenges and optimize PNFs for clinical translation, in-depth investigations are warranted. Future research should focus on elucidating the intricate interplay between peptides and nanoparticles, developing robust spectroscopic and computational methodologies, and establishing a comprehensive understanding of the structure-activity relationship governing peptide-nanoparticle interactions. Bridging these knowledge gaps will propel the translation of peptide-nanoparticle therapies from bench to bedside. While a few peptide-nanoparticle drugs have obtained FDA approval for cancer treatment, the integration of nanostructured platforms with peptide-based medications holds tremendous potential to expedite the implementation of innovative anticancer interventions. Therefore, growth factor-deficient cancers present both challenges and opportunities for targeted therapeutic interventions, with peptide-based nanoformulations positioned as a promising avenue. Nonetheless, concerted research and development endeavors are essential to optimize the specificity, stability, and safety profiles of PNFs, thereby advancing the field of peptide-based nanotherapeutics in the realm of oncology research.