Nipah virus is one of the most harmful emerging viruses with deadly effects on both humans and animals. Because of the severe outbreaks, in 2018, the World Health Organization focused on the urgent need for the development of effective solutions against the virus. However, up to date, there is no effective vaccine against the Nipah virus in the market. In the current study, the complete proteome of the Nipah virus (nine proteins) was analyzed for the antigenicity score and the virulence role of each protein, where we came up with fusion glycoprotein (F), glycoprotein (G), protein (V), and protein (W) as the candidates for epitope prediction. Following that, the multitope vaccine was designed based on top-ranking CTL, HTL, and BCL epitopes from the selected proteins. We used suitable linkers, adjuvant, and PADRE peptides to finalize the constructed vaccine, which was analyzed for its physicochemical features, antigenicity, toxicity, allergenicity, and solubility. The designed vaccine passed these assessments through computational analysis and, as a final step, we ran a docking analysis between the designed vaccine and TLR-³ and validated the docked complex through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for Nipah virus. Here, we describe the computational approach for design and analysis of this vaccine.

Lake Mariout is one of the polluted coastal marine ecosystems in Egypt which is considered to be a reservoir of serious effluents from different anthropogenic activities. Such selective pressure enforces indigenous microbial populations to acquire new advantageous themes. Thus, in this study, two Streptomyces strains were screened, from Lake Mariout’s sediment for bioreduction of 5 mM AgNO₃. Both strains were identified molecularly; their biochemical and physiological characterization revealed their ability to secrete bioactive metabolites with antagonistic activity. The cultural and incubation conditions influencing AgNPs productivity were evaluated. Subsequently, the physicochemical properties of the biofabricated AgNPs were pursued. UV-Vis spectroscopy detected surface plasmon resonance at range 458–422 nm. XRD indicated crystalline, pure, face-centered cubic AgNPs; EDX demonstrated strong silver signal at 3.5 keV. Besides, FT-IR and TGA analysis unveiled self-stabilization and functionalization of AgNPs by bioorganic molecules. However, electron microscopy micrographs depicted numerous uniform spherical AgNPs (1.17–13.3 nm). Potent bactericidal and fungicide activity were recorded by zone of inhibition assay at 50 μg/mL. Further, the antibiofilm activity was exerted in a dose-dependent manner. Moreover, the conjugation of AgNPs with the crude bioactive metabolites of both bionanofactories ameliorated the antimicrobial potency, reflecting a synergistic efficiency versus examined pathogens (free-living and biofilm).

Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.

During the current era of the COVID-19 pandemic, the dissemination of Mucorales has been reported globally, with elevated rates of infection in India, and because of the high rate of mortality and morbidity, designing an effective vaccine against mucormycosis is a major health priority, especially for immunocompromised patients. In the current study, we studied shared Mucorales proteins, which have been reported as virulence factors, and after analysis of several virulent proteins for their antigenicity and subcellular localization, we selected spore coat (CotH) and serine protease (SP) proteins as the targets of epitope mapping. The current study proposes a vaccine constructed based on top-ranking cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell lymphocyte (BCL) epitopes from filtered proteins. In addition to the selected epitopes, β-defensins adjuvant and PADRE peptide were included in the constructed vaccine to improve the stimulated immune response. Computational tools were used to estimate the physicochemical and immunological features of the proposed vaccine and validate its binding with TLR-2, where the output data of these assessments potentiate the probability of the constructed vaccine to stimulate a specific immune response against mucormycosis. Here, we demonstrate the approach of potential vaccine construction and assessment through computational tools, and to the best of our knowledge, this is the first study of a proposed vaccine against mucormycosis based on the immunoinformatics approach.

Thiazole and its derivatives have received a lot of attention from researchers due to its wide biological, pharmacological, and anticancer properties. A novel series of 2-[2-[4-Hydroxy-3-substituted benzylidene hydrazinyl]-thiazole-4[5H]-ones (4a–c) and acetoxy derivative (5) were synthesized via using thiosemicarbazones (2a–c). The structure of the thiazole derivatives (4a–c) and 5 in these compounds was confirmed by spectroscopic techniques (IR and NMR), as well as elemental investigations. The synthesized derivatives biological activity was assessed based on their capacity to suppress the growth of the cancer cell lines MCF-7 and HepG2, as well as to halt the cell cycle and trigger apoptosis. Among the synthesized thiazole derivatives, compound 4c was found the most active derivative, with inhibitory concentrations IC₅₀ = 2.57 ± 0.16 and 7.26 ± 0.44 µM in MCF-7 and HepG2, respectively, compared to Staurosporine as the standard drug with IC₅₀ 6.77 ± 0.41 and 8.4 ± 0.51 µM, respectively. Additionally, compound 4c blocked vesicular endothelial growth factor receptor-2 (VEGFR-2), according to our results (IC₅₀ = 0.15 µM), compared to Sorafenib (IC₅₀ = 0.059 µM) as the standard drug. Moreover, compound 4c induced cell cycle arrest at the G1/S phase, increasing the percentage and accumulation of cancer cells (DNA content) in the pre-G1 phase by 37.36% in MCF-7 cancer cells compared to untreated MCF-7 cells at 2.02%. Also, compound 4c increased the percentage of early and late apoptosis from 0.51% and 0.29%, respectively, in the case of the MCF-7 untreated control sample to 22.39% and 9.51%, respectively, in the MCF-7 treated sample. Furthermore, molecular docking studies of compounds 4a–c and 5 were conducted with four key proteins (aromatase, epidermal growth factor receptor (EGFR), cyclin-dependent kinase 2 (CDK2), and B-cell lymphoma 2 (Bcl-2)) that stimulate the growth, proliferation, and development of cancer cells. Compound 4c exhibited good docking scores with a promising and potential binding affinity toward the active site of selected docked proteins. According to these results, compound 4c showed efficient cytotoxic activity against the tested cancer cells.

Cancer is a multifaceted disease. With the development of multi drug resistance, the need for the arousal of novel targets in order to avoid these drawbacks increased. A new series of acrylamide derivatives was synthesized from starting material 4-(furan-2-ylmethylene)-2-(3,4,5-trimethoxyphenyl)oxazol-5(4H)–one (1), and they are evaluated for their inhibitory activity against β-tubulin polymerization. The target molecules 2–5 d were screened for their cytotoxic activity against breast cancer MCF-7 cell line. The results of cytotoxicity screening revealed that compounds 4e and 5d showed good cytotoxic profile against MCF-7 cells. Compounds 4e produced significant reduction in cellular tubulin with excellent β-tubulin polymerization inhibition activity. In addition, compound 4e exhibited cytotoxic activity against MCF-7 cells by cell cycle arrest at pre-G1 and G2/M phases, as shown by DNA flow cytometry assay. Aiming to enhance the limited aqueous solubility and, hence, poor oral bioavailability of the prepared lead acrylamide molecule, 4e-charged PEGylated bilosomes were successfully fabricated via thin film hydration techniques as an attempt to improve these pitfalls. 2³ full factorial designs were manipulated to examine the influence of formulation variables: types of bile salt including either sodium deoxy cholate (SDC) or sodium tauro cholate (STC), amount of bile salt (15 mg or 30 mg) and amount of DSPE–mPEG-2000 amount (25 mg or 50 mg) on the characteristics of the nanosystem. The F7 formula of entrapment efficiency (E.E% = 100 ± 5.6%), particle size (PS = 280.3 ± 15.4 nm) and zeta potential (ZP = −22.5 ± 3.4 mv) was picked as an optimum formula with a desirability value of 0.868. Moreover, prominent enhancement was observed at the compound’s cytotoxic activity (IC₅₀ = 0.75 ± 0.03 µM) instead of (IC₅₀ = 2.11 ± 0.19 µM) for the unformulated 4e after being included in the nano-PEGylated bilosomal system.