Toxic Peptides Research Articles

Abstract Mo042: Poison Peptides May Contribute to Dysregulation of SERCA in Heart Failure

Proteolysis increases in heart disease, resulting in an accumulation of degraded membrane protein fragments in heart cells. Previous studies have shown that diverse hydrophobic alpha helices can bind and non-specifically inhibit the cardiac calcium pump SERCA. Thus, we hypothesized that partially degraded fragments of transmembrane proteins may directly inhibit SERCA or compete with native peptide regulators of the pump, like such as phospholamban. The resulting dysregulation of SERCA may contribute to calcium mishandling in heart failure. To test these hypotheses, purified cell membranes from non-failing or dilated cardiomyopathy human hearts were subjected to SDS-PAGE and mass spectrometry to identify protein fragments smaller than 26 kD. 1800 proteins were detected, of which 92 were upregulated >3 fold in the failing heart samples, consistent with increased production of small fragments from these membrane proteins. 9 of the upregulated fragments containing transmembrane segments were selected for further analysis based on sequence hydrophobicity and structural similarity to endogenous micropeptide regulators of SERCA. Of these 9 fragments, 6 showed ER localization, suggesting potential for a toxic interaction with SERCA. The relative binding of these candidates to SERCA was quantified with FRET microscopy. Fragments of three membrane proteins (SEC61B, VAMP3, VAMP8) showed an avid interaction with SERCA. To measure SERCA inhibition, we utilized a calcium activity assay was performed usingin which HEK293T cells were transfected with SERCA, ryanodine receptor, candidate peptides and rCepia as an ER calcium indicator. ER-calcium level was normalized to minimum fluorescence after caffeine addition and an ionomycin induced maximum fluorescence after addition of ionomycin and calcium and caffeine induced minimum. Unregulated Candidate poison peptides were compared to SERCA alone and SERCA-PLB phospholamban controls were also performed. . SEC61B demonstrated the a strongest inhibition, most (similar to phospholamban, ) while VAMP3 and VAMP8’s effect was more modest. This type of non-nativeThus, we propose accumulation of toxic transmembrane peptide fragments may inhibition could be contributinginhibit SERCA and contribute to the calcium mishandling seen in heart failure. Future experiments will be performed to quantify the relative abundance of the fragments compared to endogenous regulatory micropeptides.

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However, this is difficult even for short peptides owing to the large combinatorial space of amino acid sequences. Here we describe a method for measuring the loss or maintenance of antimicrobial-peptide activity for thousands of peptide-sequence variants simultaneously, and its application to Protegrin-1, a potent yet toxic antimicrobial peptide, to determine the positional importance and flexibility of residues across its sequence while identifying variants with changes in membrane selectivity. More bacterially selective variants maintained a membrane-bound secondary structure while avoiding aromatic residues and cysteine pairs. A machine-learning model trained with our datasets accurately predicted membrane-specific activities for over 5.7 million Protegrin-1 variants, and identified one variant that showed substantially reduced toxicity and retention of activity in a mouse model of intraperitoneal infection. The high-throughput methodology may help elucidate sequence-structure-function relationships in antimicrobial peptides and inform the design of peptide-based synthetic drugs.

ToxTeller: Predicting Peptide Toxicity Using Four Different Machine Learning Approaches.

Examining the toxicity of peptides is essential for therapeutic peptide-based drug design. Machine learning approaches are frequently used to develop highly accurate predictors for peptide toxicity prediction. In this paper, we present ToxTeller, which provides four predictors using logistic regression, support vector machines, random forests, and XGBoost, respectively. For prediction model development, we construct a data set of toxic and nontoxic peptides from SwissProt and ConoServer databases with existence evidence levels checked. We also fully utilize the protein annotation in SwissProt to collect more toxic peptides than using keyword search alone. From this data set, we construct an independent test data set that shares at most 40% sequence similarity within itself and with the training data set. From a quite comprehensive list of 28 feature combinations, we conduct 10-fold cross-validation on the training data set to determine the optimized feature combination for model development. ToxTeller's performance is evaluated and compared with existing predictors on the independent test data set. Since toxic peptides must be avoided for drug design, we analyze strategies for reducing false-negative predictions of toxic peptides and suggest selecting models by top sensitivity instead of the widely used Matthews correlation coefficient, and also suggest using a meta-predictor approach with multiple predictors.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Molecular mechanisms of two novel and selective TRPV1 channel activators

Venomous toxins hold immense value as tools in elucidating the intricate structure and underlying mechanisms of ion channels. In this article, we identified of two novel toxins, Hainantoxin-XXI (HNTX-XXI) and Hainantoxin-XXII (HNTX-XXII), derived from the venom of the Chinese spider Ornithoctonus hainana. HNTX-XXI, boasting a molecular weight of 6869.095 Da, comprises 64 amino acid residues and contains 8 cysteines. Meanwhile, HNTX-XXII, with a molecular weight of 8623.732 Da, comprises 77 amino acid residues and contains 12 cysteines. Remarkably, we discovered that both HNTX-XXI and HNTX-XXII possess the ability to activate TRPV1. They activated TRPV1 with EC50 values of 3.6 ± 0.19 μM and 862 ± 56 nM, respectively. Furthermore, the current generated by the activation of TRPV1 by these toxins can be rapidly blocked by ruthenium red. Intriguingly, our analysis revealed that the interaction between HNTX-XXI and TRPV1 is mediated by three key amino acid residues: L465, V469, and D471. Similarly, the interaction between HNTX-XXII and TRPV1 is facilitated by four key amino acid residues: A657, F659, E600, and R601. These findings provide profound insights into the molecular basis of toxin-TRPV1 interactions and pave the way for future research exploring the therapeutic potential of these toxic peptides.

Toxic Peptides from the Mexican Scorpion Centruroides villegasi: Chemical Structure and Evaluation of Recognition by Human Single-Chain Antibodies.

Alternative recombinant sources of antivenoms have been successfully generated. The application of such strategies requires the characterization of the venoms for the development of specific neutralizing molecules against the toxic components. Five toxic peptides to mammals from the Mexican scorpion Centruroides villegasi were isolated by chromatographic procedures by means of gel filtration on Sephadex G-50, followed by ion-exchange columns on carboxy-methyl-cellulose (CMC) resins and finally purified by high-performance chromatography (HPLC) columns. Their primary structures were determined by Edman degradation. They contain 66 amino acids and are maintained well packed by four disulfide bridges, with molecular mass from 7511.3 to 7750.1 Da. They are all relatively toxic and deadly to mice and show high sequence identity with known peptides that are specific modifiers of the gating mechanisms of Na+ ion channels of type beta-toxin (β-ScTx). They were named Cv1 to Cv5 and used to test their recognition by single-chain variable fragments (scFv) of antibodies, using surface plasmon resonance. Three different scFvs generated in our laboratory (10FG2, HV, LR) were tested for recognizing the various new peptides described here, paving the way for the development of a novel type of scorpion antivenom.

Structure-aware deep learning model for peptide toxicity prediction.

Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming and costly. We introduce tAMPer, a novel multi-modal deep learning model designed to predict peptide toxicity by integrating the underlying amino acid sequence composition and the three-dimensional structure of peptides. tAMPer adopts a graph-based representation for peptides, encoding ColabFold-predicted structures, where nodes represent amino acids and edges represent spatial interactions. Structural features are extracted using graph neural networks, and recurrent neural networks capture sequential dependencies. tAMPer's performance was assessed on a publicly available protein toxicity benchmark and an AMP hemolysis data we generated. On the latter, tAMPer achieves an F1-score of 68.7%, outperforming the second-best method by 23.4%. On the protein benchmark, tAMPer exhibited an improvement of over 3.0% in the F1-score compared to current state-of-the-art methods. We anticipate tAMPer to accelerate AMP discovery and development by reducing the reliance on laborious toxicity screening experiments.

Screening of the Skin-Regenerative Potential of Antimicrobial Peptides: Clavanin A, Clavanin-MO, and Mastoparan-MO.

Skin wound healing is coordinated by a delicate balance between proinflammatory and anti-inflammatory responses, which can be affected by opportunistic pathogens and metabolic or vascular diseases. Several antimicrobial peptides (AMPs) possess immunomodulatory properties, suggesting their potential to support skin wound healing. Here, we evaluated the proregenerative activity of three recently described AMPs (Clavanin A, Clavanin-MO, and Mastoparan-MO). Human primary dermal fibroblasts (hFibs) were used to determine peptide toxicity and their capacity to induce cell proliferation and migration. Furthermore, mRNA analysis was used to investigate the modulation of genes associated with skin regeneration. Subsequently, the regenerative potential of the peptides was further confirmed using an ex vivo organotypic model of human skin (hOSEC)-based lesion. Our results indicate that the three molecules evaluated in this study have regenerative potential at nontoxic doses (i.e., 200 μM for Clavanin-A and Clavanin-MO, and 6.25 μM for Mastoparan-MO). At these concentrations, all peptides promoted the proliferation and migration of hFibs during in vitro assays. Such processes were accompanied by gene expression signatures related to skin regenerative processes, including significantly higher KI67, HAS2 and CXCR4 mRNA levels induced by Clavanin A and Mastoparan-MO. Such findings translated into significantly accelerated wound healing promoted by both Clavanin A and Mastoparan-MO in hOSEC-based lesions. Overall, the data demonstrate the proregenerative properties of these peptides using human experimental skin models, with Mastoparan-MO and Clavanin A showing much greater potential for inducing wound healing compared to Clavanin-MO.

Investigation of cytotoxic effect and action mechanism of a synthetic peptide derivative of rabbit cathelicidin against MDA-MB-231 breast cancer cell line

Antimicrobial peptides (AMPs) have sparked significant interest as potential anti-cancer agents, thereby becoming a focal point in pursuing novel cancer-fighting strategies. These peptides possess distinctive properties, underscoring the importance of developing more potent and selectively targeted versions with diverse mechanisms of action against human cancer cells. Such advancements would offer notable advantages compared to existing cancer therapies. This research aimed to examine the toxicity and selectivity of the nrCap18 peptide in both cancer and normal cell lines. Furthermore, the rate of cellular death was assessed using apoptosis and acridine orange/ethidium bromide (AO/EB) double staining at three distinct incubation times. Additionally, the impact of this peptide on the cancer cell cycle and migration was evaluated, and ultimately, the expression of cyclin-dependent kinase 4/6 (CDK4/6) genes was investigated. The results obtained from the study demonstrated significant toxicity and selectivity in cancer cells compared to normal cells. Moreover, a strong progressive increase in cell death was observed over time. Furthermore, the peptide exhibited the ability to halt the progression of cancer cells in the G1 phase of the cell cycle and impede their migration by suppressing the expression of CDK4/6 genes.

VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.

Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over $10\%$ on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics.

Dual-function antimicrobial-antibiofilm peptide hybrid to tackle biofilm-forming Staphylococcus epidermidis

BackgroundDue to their resistance and difficulty in treatment, biofilm-associated infections are problematic among hospitalized patients globally and account for 60% of all bacterial infections in humans. Antibiofilm peptides have recently emerged as an alternative treatment since they can be effectively designed and exert a different mode of biofilm inhibition and eradication.MethodsA novel antibiofilm peptide, BiF, was designed from the conserved sequence of 18 α-helical antibiofilm peptides by template-assisted technique and its activity was improved by hybridization with a lipid binding motif (KILRR). Novel antibiofilm peptide derivatives were modified by substituting hydrophobic amino acids at positions 5 or 7, and both, with positively charged lysines (L5K, L7K). These peptide derivatives were tested for antibiofilm and antimicrobial activities against biofilm-forming Staphylococcus epidermidis and multiple other microbes using crystal violet and broth microdilution assays, respectively. To assess their impact on mammalian cells, the toxicity of peptides was determined through hemolysis and cytotoxicity assays. The stability of candidate peptide, BiF2_5K7K, was assessed in human serum and its secondary structure in bacterial membrane-like environments was analyzed using circular dichroism. The action of BiF2_5K7K on planktonic S. epidermidis and its effect on biofilm cell viability were assessed via viable counting assays. Its biofilm inhibition mechanism was investigated through confocal laser scanning microscopy and transcription analysis. Additionally, its ability to eradicate mature biofilms was examined using colony counting. Finally, a preliminary evaluation involved coating a catheter with BiF2_5K7K to assess its preventive efficacy against S. epidermidis biofilm formation on the catheter and its surrounding area.ResultsBiF2_5K7K, the modified antibiofilm peptide, exhibited dose-dependent antibiofilm activity against S. epidermidis. It inhibited biofilm formation at subinhibitory concentrations by altering S. epidermidis extracellular polysaccharide production and quorum-sensing gene expression. Additionally, it exhibited broad-spectrum antimicrobial activity and no significant hemolysis or toxicity against mammalian cell lines was observed. Its activity is retained when exposed to human serum. In bacterial membrane-like environments, this peptide formed an α-helix amphipathic structure. Within 4 h, a reduction in the number of S. epidermidis colonies was observed, demonstrating the fast action of this peptide. As a preliminary test, a BiF2_5K7K-coated catheter was able to prevent the development of S. epidermidis biofilm both on the catheter surface and in its surrounding area.ConclusionsDue to the safety and effectiveness of BiF2_5K7K, we suggest that this peptide be further developed to combat biofilm infections, particularly those of biofilm-forming S. epidermidis.

Mass spectrometric analysis of Odonthobuthus Doriae scorpion venom and its non-neutralized fractions after interaction with commercial antivenom

It is believed that antivenoms play a crucial role in neutralizing venoms. However, uncontrolled clinical effects appear in patients stung by scorpions after the injection of antivenom. In this research, non-neutralized components of the venom of the Iranian scorpion Odonthobuthus doriae were analyzed after interacting with the commercial antivenom available in the market. The venom and antivenom interaction was performed, then centrifuged, and the supernatant was analyzed by high-performance liquid chromatography (HPLC). Two peaks of Odonthobuthus doriae venom were observed in the chromatogram of the supernatant. Two components were isolated by HPLC and analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) instruments. Peptide sequencing was done by Liquid Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry (LC-Q-TOF MS/MS). Results indicate that the components of scorpion venom mainly have a molecular weight below 10 kDa, consisting of toxic peptides that disrupt the function of sodium and potassium channels. The MALDI-TOF MS results show that two toxic peptides with molecular masses of 6941 Da and 6396 Da were not neutralized by the antivenom. According to the MS/MS sequencing data, the components have been related to peptides A0A5P8U2Q6_MESEU and A0A0U4FP89_ODODO, which belong to the sodium and potassium channels toxins family, respectively.

Integrated convolution and self-attention for improving peptide toxicity prediction.

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

Small GTPases control macropinocytosis of amyloid precursor protein and cleavage to amyloid-β

The overproduction of the toxic peptide amyloid-beta (Aβ) generated from the cleavage of amyloid precursor protein (APP) is proposed to be a critical event in the development of Alzheimer's disease. Evidence suggests that the cleavage of APP occurs after its internalization from the cell surface. Previously, we identified a novel pathway for APP internalization, which trafficks cell surface APP directly to lysosomes by macropinocytosis, leading to its processing into Aβ. We also demonstrated that ADP-ribosylation factor 6 (Arf6) is required for the macropinocytosis of APP. Here, we characterized the roles of Arf6's downstream effectors Rac1, Cdc42 and RhoA. Both pharmacological inhibition and siRNA knockdown of these proteins reduced the amount of APP colocalized with LAMP1-labeled lysosomes without affecting APP transport to early endosomes. Decreases in the production of both Aβ40 and Aβ42 were also observed by ELISA in response to inhibitor treatment. These findings together demonstrate that Rac1, Cdc42 and RhoA are components of the mechanism regulating the macropinocytosis of APP and targeting these components can reduce the production of Aβ.

Celiac Disease Review: Background and Emphasis on the Important Role of Health Education in the Prevention and Control of Symptom

Celiac disease is related to dietary gluten found in wheat, rye, and barley, CD is an immune-mediated enteropathy and it is one of the most prevalent lifelong food-related illnesses globally. In addition, CD is also thought to be a systemic ailment marked by a variety of gluten-related signs and symptoms as well as disease-specific antibodies. When gluten is consumed, it produces toxic gluten peptides that can trigger both innate and adaptive immune responses in people who are predisposed to them. The transglutaminase enzyme plays a role in the development of the situation by deaminating gluten to produce glutamic acid. Furthermore, the peptidase enzyme finds it difficult to break down the prolamin component of gluten, which is high in proline and glutamine. This leads to incomplete protein digestion and the creation of peptides that trigger the immunological response. Moreover, CD causes remarkable damage to the small intestinal mucosa in the jejunum, resulting in inflammation of the villi and villous atrophy, which hinders the absorption of nutrients. Combining small intestine mucosal histology under a gluten-containing diet with CD serology allows for the diagnosis of the condition. A stringent lifelong gluten-free diet is now the only effective treatment for CD; nevertheless, the diet is demanding, and avoiding gluten is challenging. Further research is needed to improve and develop the diagnosis and treatment of CD via healthcare professional education programs.

Oligomeric Amyloid-β and Tau Alter Cell Adhesion Properties and Induce Inflammatory Responses in Cerebral Endothelial Cells Through the RhoA/ROCK Pathway.

Dysfunction of cerebral endothelial cells (CECs) has been implicated in the pathology of Alzheimer's disease (AD). Despite evidence showing cytotoxic effects of oligomeric amyloid-β (oAβ) and Tau (oTau) in the central nervous system, their direct effects on CECs have not been fully investigated. In this study, we examined the direct effects of oAβ, oTau, and their combination on cell adhesion properties and inflammatory responses in CECs. We found that both oAβ and oTau increased cell stiffness, as well as the p-selectin/Sialyl-LewisX (sLeX) bonding-mediated membrane tether force and probability of adhesion in CECs. Consistent with these biomechanical alterations, treatments with oAβ or oTau also increased actin polymerization and the expression of p-selectin at the cell surface. These toxic oligomeric peptides also triggered inflammatory responses, including upregulations of p-NF-kB p65, IL-1β, and TNF-α. In addition, they rapidly activated the RhoA/ROCK pathway. These biochemical and biomechanical changes were further enhanced by the treatment with the combination of oAβ and oTau, which were significantly suppressed by Fasudil, a specific inhibitor for the RhoA/ROCK pathway. In conclusion, our data suggest that oAβ, oTau, and their combination triggered subcellular mechanical alterations and inflammatory responses in CECs through the RhoA/ROCK pathway.

A Novel Drosophila Model of Alzheimer's Disease to Study Aβ Proteotoxicity in the Digestive Tract.

Amyloid-β (Aβ) proteotoxicity is associated with Alzheimer's disease (AD) and is caused by protein aggregation, resulting in neuronal damage in the brain. In the search for novel treatments, Drosophila melanogaster has been extensively used to screen for anti-Aβ proteotoxic agents in studies where toxic Aβ peptides are expressed in the fly brain. Since drug molecules often are administered orally there is a risk that they fail to reach the brain, due to their inability to cross the brain barrier. To circumvent this problem, we have designed a novel Drosophila model that expresses the Aβ peptides in the digestive tract. In addition, a built-in apoptotic sensor provides a fluorescent signal from the green fluorescent protein as a response to caspase activity. We found that expressing different variants of Aβ1-42 resulted in proteotoxic phenotypes such as reduced longevity, aggregate deposition, and the presence of apoptotic cells. Taken together, this gut-based Aβ-expressing fly model can be used to study the mechanisms behind Aβ proteotoxicity and to identify different substances that can modify Aβ proteotoxicity.

Immunoinformatics Approach to Design a Chimeric CD70-Peptide Vaccine against Renal Cell Carcinoma.

Renal cell carcinoma (RCC) accounts for the majority of cancer-related deaths worldwide. Overexpression of CD70 has been linked to advanced stages of RCC. Therefore, this study aims to develop a multiepitope vaccine targeting the overexpressed CD70 using immunoinformatics techniques. In this investigation, in silico multiepitope vaccines were constructed by linking specific CD70 protein epitopes for helper T lymphocytes and CD8+ T lymphocytes. To enhance immunogenicity, sequences of cell-penetrating peptide (CPP), penetratin (pAntp), along with the entire sequence of tumor necrosis factor-α (TNF-α), were attached to the N-terminal and C-terminal of the CD70 epitopes. Computational assessments were performed on these chimeric vaccines for antigenicity, allergenicity, peptide toxicity, population coverage, and physicochemical properties. Furthermore, refined 3D constructs were subjected to a range of analyses, encompassing structural B-cell epitope prediction and molecular docking. The chosen vaccine construct underwent diverse assessments such as molecular dynamics simulation, immune response simulation, and in silico cloning. All vaccines comprised antigenic, nontoxic, and nonallergenic epitopes, ensuring extensive global population coverage. The vaccine constructs demonstrated favorable physicochemical characteristics. The binding affinity of chimeric vaccines to the TNF receptor remained relatively stable, influenced by the alignment of vaccine components. Molecular docking and dynamics analyses predicted stable interactions between CD70-CPP-TNF and the TNF receptor, indicating potential efficacy. In silico codon optimization and cloning of the vaccine nucleic acid sequence were accomplished using the pET28a plasmid. Furthermore, this vaccine displayed the capacity to modulate humoral and cellular immune responses. Overall, the results suggest therapeutic potential for the chimeric CD70-CPP-TNF vaccine against RCC. However, validation through in vitro and in vivo experiments is necessary. This trial is registered with NCT04696731 and NCT04046445.

De novo designed aliphatic and aromatic peptides assemble into amyloid-like cytotoxic supramolecular nanofibrils.

Peptides are very interesting biomolecules that upon self-association form a variety of thermodynamically stable supramolecular structures of nanometric dimension e.g. nanotubes, nanorods, nanovesicles, nanofibrils, nanowires and many others. Herein, we report six peptide molecules having a general chemical structure, H-Gaba-X-X-OH (Gaba: γ-aminobutyric acid, X: amino acid). Out of these six peptides, three are aromatic and the others are aliphatic. Atomic force microscopic (AFM) studies reveal that except peptide 6 (H-Gaba-Trp-Trp-OH), all the reported peptides adopt nanofibrillar morphology upon aggregation in aqueous medium. These supramolecular assemblies can recognize amyloid-specific molecular probe congo red (CR) and thioflavine t (ThT) and exhibit all the characteristic properties of amyloids. The MTT cell viability assay reveals that the toxicity of both aliphatic and aromatic peptides increases with increasing concentration of the peptides to both cancer (HeLa) and non-cancer (HEK 293) cells. Of note, the aromatic peptides show a slightly higher cytotoxic effect compared to the aliphatic peptides. Overall, the studies highlight the self-assembling nature of the de novo designed aliphatic and aromatic peptides and pave the way towards elucidating the intricacies of pathogenic amyloid assemblies.

Porous organic cages as inhibitors of Aβ42 peptide aggregation: a simulation study.

The aggregation of Aβ monomers into oligomers with β-sheet structures is an important cause of Alzheimer's disease (AD), while the Aβ42 peptide is more toxic and prone to aggregate. It is of great significance to study the inhibition mechanism of Aβ42 monomer aggregation and find excellent inhibitors for the treatment of AD. Research in recent years has focused on small molecule compounds and nanoparticles, but they all have certain limitations. As a new type of porous material, a porous organic cage (POC) has potential application feasibility in the biomedical field due to its unique physicochemical properties. In this work, molecular dynamics simulations were used for the first time to explore the interaction and conformational transformation of the Aβ42 peptide in CC3 crystals with different morphologies (planar and spherical). The results show that the adsorption of the Aβ42 peptide on different CC3 crystals is mainly achieved through strong van der Waals forces. During the simulations, the Aβ42 peptide undergoes various degrees of structural changes. Compared to that in water, this binding induces more irregular structures, such as turns and 3-helices, and inhibits the production of β-sheets, while enhancing the overall backbone rigidity of the Aβ42 peptide. The transformation analysis of peptide conformation is further complemented by free energy landscape and cluster analysis. These findings provide a strong basis for CC3 crystals as novel inhibitors to inhibit the toxicity and aggregation of the Aβ42 peptide.