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Dengue virus (DENV) is an arboviral disease affecting more than 400 million people annually. Only a single vaccine formulation is available commercially and many others are still under clinical trials. Despite all the efforts in vaccine designing, the improvement in vaccine formulation against DENV is very much needed. In this study, we used a roboust immunoinformatics approach, targeting all the four serotypes of DENV to design a multi-epitope vaccine. A total of 13501 MHC II binding CD4+ epitope peptides were predicted from polyprotein sequences of four dengue virus serotypes. Among them, ten conserved epitope peptides that were interferon-inducing were selected and found to be conserved among all the four dengue serotypes. The vaccine was formulated using antigenic, non-toxic and conserved multi epitopes discovered in the in-silico study. Further, the molecular docking and molecular dynamics predicted stable interactions between predicted vaccine and immune receptor, TLR-5. Finally, one of the mapped epitope peptides was synthesized for the validation of antigenicity and antibody production ability where the in-vivo tests on rabbit model was conducted. Our in-vivo analysis clearly indicate that the imunogen designed in this study could stimulate the production of antibodies which further suggest that the vaccine designed possesses good immunogenicity.

Idiosyncratic ximelagatran-induced hepatotoxicity has been reported to be associated with human leukocyte antigen (HLA)-DRB1*07:01 and ximelagatran has been reported to inhibit the binding of the ligand peptide to HLA-DRB1*07:01 in vitro. In order to predict the possible interaction modes of ximelagatran with HLA-DR molecules, in silico docking simulations were performed. Molecular dynamics (MD) simulations were also performed to predict the effect of ximelagatran on the binding mode of the ligand peptide to HLA-DRB1*07:01. A series of in silico simulations supported the inhibitory effect of ximelagatran on the binding of the ligand peptide to HLA-DRB1*07:01 in vitro. Furthermore, direct interactions of ximelagatran with HLA-DR molecules were evaluated in vitro, which supported the simulated interaction mode of ximelagatran with HLA-DRB1*07:01. These results indicated that ximelagatran directly interacts with the peptide binding groove of HLA-DRB1*07:01 and competes with the ligand peptide for the binding site, which could alter the immune response and lead to the idiosyncratic ximelagatran-induced hepatotoxicity.

is a highly resistant pathogen. It has multiple virulence factors, which makes it one of the most pathogenic bacteria for humankind. The vast increase in antibiotic resistance in these bacteria is a warning of existing healthcare policies. Most of the available antibiotics are ineffective due to resistance; this situation requires the development of drugs that target specific proteins and are not susceptible to resistance. In this study, we identified a compound that acts as an antagonist of ClfA and ClfB by inhibiting their binding to host cells. The shortlisted compound's binding activity was tested by docking and molecular dynamics during its interaction with proteins. The identified compound has excellent binding energy with both ClfA (-10.11 kcal/mol) and ClfB (-11.11 kcal/mol). The molecular dynamics of the protein and compound were stable and promising for further and tests. The performance of our compound was tested and compared with that of the control molecule allantodapsone, which was reported in a previous study as a pan inhibitor of the clumping factor. An ADMET study of our selected compound revealed its reliable drug likeliness. This compound is an ideal candidate for studies.

The myelin sheath ensures efficient nerve impulse transmission along the axons. Remyelination is a spontaneous process that restores axonal insulation, promoting neuroprotection and recovery after myelin damage. There is an urgent need for new pharmacological approaches to remyelination and to improve the most effective molecules. Some glucocorticoids (GC) were identified through phenotypical screens for their promyelinating properties. These GC compounds share the ability to bind the Smoothened (Smo) receptor of the Hedgehog (Hh) pathway. Gaining a deeper insight into how they modulate Smo receptor activity could guide structure-based studies to leverage the GCs' potent promyelinating activity for a more targeted approach to remyelination. Here we focused on clarifying the mechanism of action of Budesonide, a GC known to bind the Smo cysteine-rich domain (CRD) and prevent Smo translocation to the cilium in fibroblasts. Our study employed a combination of cellular, biochemical and molecular dynamics approaches. We show that treating oligodendroglial cells with Budesonide promotes myelination of synthetic axons and reduces Smo CRD conformational flexibility. This inhibits the Smo-mediated canonical signaling while activating the Liver Kinase B1 (LKB1)/ AMP-activated protein kinase (AMPK) pathway, leading to Myelin basic protein (MBP) expression. These insights pave the way for pharmacological targeting of Smo CRD to enhance oligodendrocyte precursor cells (OPCs) differentiation and improve remyelination.

Since May 2022, cases of monkeypox, a zoonotic disease caused by the monkeypox virus (MPXV), have been increasingly reported worldwide. There are, however, no proven therapies or vaccines available for monkeypox. In this study, several multi-epitope vaccines were designed against the MPXV using immunoinformatics approaches. Three target proteins, A35R and B6R, enveloped virion (EV) form-derived antigens, and H3L, expressed on the mature virion (MV) form, were selected for epitope identification. The shortlisted epitopes were fused with appropriate adjuvants and linkers to vaccine candidates. The biophysical andbiochemical features of vaccine candidates were evaluated. The Molecular docking and molecular dynamics(MD) simulation were run to understand the binding mode and binding stability between the vaccines and Toll-like receptors (TLRs) and major histocompatibility complexes (MHCs). The immunogenicity of the designed vaccines was evaluated via immune simulation. Five vaccine constructs (MPXV-1-5) were formed. After the evaluation of various immunological and physicochemical parameters, MPXV-2 and MPXV-5 were selected for further analysis. The results of molecular docking showed that the MPXV-2 and MPXV-5 had a stronger affinity to TLRs (TLR2 and TLR4) and MHC (HLA-A*02:01 and HLA-DRB1*02:01) molecules, and the analyses of molecular dynamics (MD) simulation have further confirmed the strong binding stability of MPXV-2 and MPXV-5 with TLRs and MHC molecules. The results of the immune simulation indicated that both MPXV-2 and MPXV-5 could effectively induce robust protective immune responses in the human body. The MPXV-2 and MPXV-5 have good efficacy against the MPXV in theory, but further studies are required to validate their safety and efficacy.

Carbon nanotubes (CNTs) can improve the storage properties of modified asphalt by enhancing the interfacial adhesion of recycled polyethylene (RPE) and base asphalt. In this study, the interaction of CNT/RPE asphalt was investigated using molecular dynamics simulation. The base asphalt was examined using a 12-component molecular model and verified by assessing the following properties: its four-component content, elemental contents, radial distribution function (RDF) and glass transition temperature. Then, the adhesion properties at the interface of the CNT/RPE-modified asphalt molecules were studied by measuring binding energy. The molecular structural stability of CNTs at the interface between RPE and asphalt molecules was analyzed through the relative concentration distribution. The motion of molecules in the modified asphalt was studied in terms of the mean square displacement (MSD) and diffusion coefficient. The results showed that CNTs improved the binding energy between RPE and base asphalt. CNTs not only weakened the repulsion of RPE with asphaltenes and resins, but also promoted the interaction of RPE with light components, which facilitated the compatibility of RPE with the base asphalt. The change in the interaction affected the molecular motion, and the molecular diffusion coefficient in the CNT/RPE-modified asphalt system was significantly smaller than that of RPE-modified asphalt. Moreover, the distribution of the asphaltene component was promoted by CNTs, resulting in the enhancement of the storage stability of RPE-modified asphalt. The property indexes indicated that the storage stability was significantly improved by CNTs, and better viscoelastic properties were also observed. Our research provides a foundation for the application of RPE in pavement engineering.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken research on complex disorders, enabling the discovery of effective treatments. This study explores anticancer targets by assessing the potential of naturally occurring compounds derived from various plants to cure colorectal cancer. Twenty compounds were sourced from PubChem, and the RPS20 protein structure was obtained from AlphaFold, and mutation “V50S” was added. Validation of mutated RPS20 protein was performed using the Ramachandran plot and ERRAT. Binding sites on the mutated RPS20 protein were identified with DeepSite, followed by virtual screening to pinpoint the most promising natural lead drug candidate. Indirubin emerged as the lead drug candidate, fulfilling all ADMET criteria and exhibiting a good binding affinity. Further development included designing an AI-based drug using the WADDAICA server, which was validated through molecular docking, molecular dynamics (MD) simulation, and MMGBSA. The electronic properties of indirubin were studied using DFT calculations. The results show a moderate HOMO-LUMO gap, indicating its potential reactivity and the possible capability for biological target interactions. These findings indicate that indirubin could serve as a potent and effective cancer inhibitor, offering high efficacy with minimal side effects.

genus, a complex group of species classified to be aerobic actinomycete, can lead to severe concurrent infection as well as disseminated infection, typically in immunocompromised patients. With the expansion of the susceptible population, the incidence of Nocardia has been gradually growing, accompanied by increased resistance of the pathogen to existing therapeutics. However, there is no effective vaccine against this pathogen yet. In this study, a multi-epitope vaccine was designed against the Nocardia infection using reverse vaccinology combined with immunoinformatics approaches. First, the proteomes of 6 Nocardia subspecies Nocardia subspecies (Nocardia farcinica, Nocardia cyriacigeorgica, Nocardia abscessus, Nocardia otitidiscaviarum, Nocardia brasiliensis and Nocardia nova) were download NCBI (National Center for Biotechnology Information) database on May 1st, 2022 for the target proteins selection. The essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome proteins were selected for epitope identification. The shortlisted T-cell and B-cell epitopes were fused with appropriate adjuvants and linkers to construct vaccines. The physicochemical properties of the designed vaccine were predicted using multiple online servers. The Molecular docking and molecular dynamics (MD) simulation were performed to understand the binding pattern and binding stability between the vaccine candidate and Toll-like receptors (TLRs). The immunogenicity of the designed vaccines was evaluated via immune simulation. 3 proteins that are essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome were selected from 218 complete proteome sequences of the 6 Nocardia subspecies epitope identification. After screening, only 4 cytotoxic T lymphocyte (CTL) epitopes, 6 helper T lymphocyte (HTL) epitopes, and 8 B cell epitopes that were antigenic, non-allergenic, and non-toxic were included in the final vaccine construct. The results of molecular docking and MD simulation showed that the vaccine candidate has a strong affinity for TLR2 and TLR4 of the host and the vaccine-TLR complexes were dynamically stable in the natural environment. The results of the immune simulation indicated that the designed vaccine had the potential to induce strong protective immune responses in the host. The codon optimization and cloned analysis showed that the vaccine was available for mass production. The designed vaccine has the potential to stimulate long-lasting immunity in the host, but further studies are required to validate its safety and efficacy.

Acinetobacter baumannii is a gram-negative bacillus prevalent in nature, capable of thriving under various environmental conditions. As an opportunistic pathogen, it frequently causes nosocomial infections such as urinary tract infections, bacteremia, and pneumonia, contributing to increased morbidity and mortality in clinical settings. Consequently, developing novel vaccines against Acinetobacter baumannii is of utmost importance. In our study, we identified 10 highly conserved antigenic proteins from the NCBI and UniProt databases for epitope mapping. We subsequently screened and selected 8 CTL, HTL, and LBL epitopes, integrating them into three distinct vaccines constructed with adjuvants. Following comprehensive evaluations of immunological and physicochemical parameters, we conducted molecular docking and molecular dynamics simulations to assess the efficacy and stability of these vaccines. Our findings indicate that all three multi-epitope mRNA vaccines designed against Acinetobacter baumannii are promising; however, further animal studies are required to confirm their reliability and effectiveness.

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound ( 1 ), and the potent standard ( III ) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.