Integrating immunoinformatics and computational epitope prediction for a vaccine candidate against respiratory syncytial virus.

Respiratory syncytial virus (RSV) poses a significant global health threat, especially affecting infants and the elderly. Addressing this, the present study proposes an innovative approach to vaccine design, utilizing immunoinformatics and computational strategies. We analyzed RSV's structural proteins across both subtypes A and B, identifying potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Criteria such as antigenicity, allergenicity, toxicity, and cytokine-inducing potential were rigorously examined. Additionally, we evaluated the conservancy of these epitopes and their population coverage across various RSV strains. The comprehensive analysis identified six major histocompatibility complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes. These were integrated with suitable linkers and adjuvants to form the vaccine. Further, molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors (TLR4 and TLR5), with a notable preference for TLR4. Immune simulation analysis underscored the vaccine's potential to elicit a strong immune response. This study presents a promising RSV vaccine candidate and offers theoretical support, marking a significant advancement in vaccine development efforts. However, the promising in silico findings need to be further validated through additional in vivo studies.

Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis.

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a prevalent airborne infectious disease. Despite the availability of the Bacille Calmette-Guerin vaccine, its global efficacy remains modest, and tuberculosis persists as a significant global public health threat. Addressing this challenge and advancing towards the End MTB Strategy, we developed a multiepitope vaccine (MEV) based on immunoinformatics and computational approaches. Immunoinformatics screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex (MHC) allele binding, immunogenicity, antigenicity, allergenicity, toxicity, and cytokine inducibility. Selected epitopes were integrated into an MEV construct with adjuvant and linkers, forming a fully immunogenic vaccine candidate. Comprehensive analyses encompassed the evaluation of immunological and physicochemical properties, determination of tertiary structure, molecular docking with Toll-Like Receptors (TLR), molecular dynamics (MD) simulations for all atoms, and immune simulations. Our MEV comprises 534 amino acids, featuring 6 cytotoxic T lymphocyte, 8 helper T lymphocyte, and 7 linear B lymphocyte epitopes, demonstrating high antigenicity and stability. Notably, molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to TLR2. In addition, the immune simulation indicated the capacity to effectively induce elevated levels of antibodies and cytokines, emphasizing the vaccine's robust immunogenic response. This study presents a promising MEV against TB, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for TB vaccine development. Our study aligns with the global initiative of the End MTB Strategy, emphasizing its potential impact on addressing persistent challenges in TB control.

Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus.

Powassan virus (POWV) is an arthropod-borne virus (arbovirus) capable of causing severe illness in humans for severe neurological complications, and its incidence has been on the rise in recent years due to climate change, posing a growing public health concern. Currently, no vaccines to prevent or medicines to treat POWV disease, emphasizing the urgent need for effective countermeasures. In this study, we utilize bioinformatics approaches to target proteins of POWV, including the capsid, envelope, and membrane proteins, to predict diverse B-cell and T-cell epitopes. These epitopes underwent screening for critical properties such as antigenicity, allergenicity, toxicity, and cytokine induction potential. Eight selected epitopes were then conjugated with adjuvants using various linkers, resulting in designing of a potentially stable and immunogenic vaccine candidate against POWV. Moreover, molecular docking, molecular dynamics simulations, and immune simulations revealed a stable interaction pattern with the immune receptor, suggesting the vaccine's potential to induce robust immune responses. In conclusion, our study provided a set of derived epitopes from POWV's proteins, demonstrating the potential for a novel vaccine candidate against POWV. Further in vitro and in vivo studies are warranted to advance our efforts and move closer to the goal of combatting POWV and related arbovirus infections.

Designing a Multiepitope Vaccine against Eastern Equine Encephalitis Virus: Immunoinformatics and Computational Approaches.

Eastern equine encephalitis virus (EEEV) is a significant threat to human and animal populations, causing severe encephalitis, often leading to long-term neurological complications and even mortality. Despite this, no approved antiviral treatments or EEEV human vaccines currently exist. In response, we utilized immunoinformatics and computational approaches to design a multiepitope vaccine candidate for EEEV. By screening the structural polyprotein of EEEV, we predicted both T-cell and linear B-cell epitopes. These epitopes underwent comprehensive evaluations for their antigenicity, toxicity, and allergenicity. From these evaluations, we selected ten epitopes highly suitable for vaccine design, which were connected with adjuvants using a stable linker. The resulting vaccine construct demonstrated exceptional antigenic, nontoxic, nonallergenic, and physicochemical properties. Subsequently, we employed molecular docking and molecular dynamics simulations to reveal a stable interaction pattern between the vaccine candidate and Toll-like receptor 5. Besides, computational immune simulations predicted the vaccine's capability to induce robust immune responses. Our study addresses the urgent need for effective EEEV preventive strategies and offers valuable insights for EEEV vaccine development. As EEEV poses a severe threat with potential spread due to climate change, our research provides a crucial step in enhancing public health defenses against this menacing zoonotic disease.

Designing a multi-epitope candidate vaccine by employing immunoinformatics approaches to control African swine fever spread.

The African swine fever virus has been circulating for decades and is highly infectious, often fatal to farmed and wild pigs. There is currently no approved vaccine or treatment for the disease, making prevention even more difficult. Therefore, vaccine development is necessary and urgent to limit the consequences of ASF and ensure the food chain and sustainability of the swine industry. This research study was conducted to design a multi-epitope vaccine for controlling veterinary diseases caused by the African swine fever virus. We employed the immunoinformatics approaches to reveal 37 epitopes from different viral proteins of ASFV. These epitopes were linked to adjuvants and linkers to form a full-fledged immunogenic vaccine construct. The tertiary structure of the final vaccine was predicted using a deep-learning approach. The molecular docking and molecular dynamics predicted stable interactions between the vaccine and immune receptor TLR5 of Sus scrofa (Pig). The MD simulation studies reflect that the calculated parameters like RMSD, RMSF, number of hydrogen bonds, and finally, the buried interface surface area for the complex remained stable throughout the simulation time. This analysis suggests the stability of interface interactions between the TLR5 and the multi-epitope vaccine construct. Further, the physiochemical analysis demonstrated that our designed vaccine construct was expected to have high stability and prolonged half-life time in mammalian cells. Traditional vaccine design experiments require significant time and financial input from the development stage to the final product. Studies like this can assist in accelerating vaccine development while minimizing the cost.Communicated by Ramaswamy H. Sarma.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer.

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

Immunogenic Epitope-Based Vaccine Prediction from Surface Glycoprotein of MERS-CoV by Deploying Immunoinformatics Approach.

Middle East respiratory syndrome coronavirus (MERS-CoV) has caused a high mortality rate since its emergence in 2012 in the Middle East. Currently, no effective drug or vaccine is available for MERS-CoV. Supportive care and prevention are the only ways to manage infection. In this study, we identified an epitope-based vaccine that could be an optimal solution for the prevention of MERS-CoV infection. By deploying an immunoinformatics approach, we predicted a subunit vaccine based on the surface glycoprotein (S protein) of MERS-CoV. For this purpose, the proteome of the MERS-CoV spike protein was obtained from the NCBI GenBank database. Then, it was subjected to a check for allergenicity using the Allergen FP v.1.0 tool. The Vaxijen v.2.0 tool was used to conduct antigenicity tests for binding with major histocompatibility complex class I and II molecules. The solidity of the predicted epitope-allele docked complex was evaluated by a molecular dynamics simulation. After docking a total of eight epitopes from the MERS-CoV S protein, further analyses predicted their non-toxicity and therapeutic immunogenic properties. These epitopes have potential utility as vaccine candidates against MERS-CoV, to be validated by wet-lab testing. Supplementary Information: The online version contains supplementary material available at 10.1007/s10989-022-10382-5.

MiR-21 in the Cancers of the Digestive System and Its Potential Role as a Diagnostic, Predictive, and Therapeutic Biomarker.

MicroRNAs (miRNAs) are small non-coding RNAs. They can regulate the expression of their target genes, and thus, their dysregulation significantly contributes to the development of cancer. Growing evidence suggests that miRNAs could be used as cancer biomarkers. As an oncogenic miRNA, the roles of miR-21 as a diagnostic and prognostic biomarker, and its therapeutic applications have been extensively studied. In this review, the roles of miR-21 are first demonstrated via its different molecular networks. Then, a comprehensive review on the potential targets and the current applications as a diagnostic and prognostic cancer biomarker and the therapeutic roles of miR-21 in six different cancers in the digestive system is provided. Lastly, a brief discussion on the challenges for the use of miR-21 as a therapeutic tool for these cancers is added.

Observing Phase Transition of a Temperature-Responsive Polymer Using Electrochemical Collisions on an Ultramicroelectrode.

Herein, a study on a new lower critical solution temperature (LCST) polymer in an organic solvent by an electrochemical technique has been reported. The phase-transition behavior of poly(arylene ether sulfone) (PAES) was examined on 1,2-dimethoxyethane (DME). At a temperature above the LCST point, polymer molecules aggregated to create polymer droplets. These droplets subsequently collided with an ultramicroelectrode (UME), resulting in a new form of staircase current decrease. The experimental collision frequency and collision signal were analyzed in relation to the concentration of the polymer. In addition, the degree of polymer aggregation associated with temperature change was also observed.

Electrochemical Detection of Hydrazine Using Poly(dopamine)-Modified Electrodes.

We have developed a simple and selective method for the electrochemical detection of hydrazine (HZ) using poly(dopamine) (pDA)-modified indium tin oxide (ITO) electrodes. Modification with pDA was easily achieved by submerging the ITO electrode in a DA solution for 30 min. The electrocatalytic oxidation of HZ on the pDA-modified ITO electrode was measured by cyclic voltammetry. In buffer solution, the concentration range for linear HZ detection was 100 µM-10 mM, and the detection limit was 1 µM. The proposed method was finally used to determine HZ in tap water to simulate the analysis of real samples. This method showed good recovery (94%-115%) and was not affected by the other species present in the tap water samples.