Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body. Gene and epigenetic alterations disrupt normal cellular control, leading to abnormal cell proliferation, resistance to cell death, blood vessel development, and metastasis (spread to other organs). One of the several routes that play an important role in the development and progression of cancer is the phosphoinositide 3-kinase (PI3K) signaling pathway. Moreover, the gene PIK3CG encodes the catalytic subunit gamma (p110γ) of phosphoinositide 3-kinase (PI3Kγ), a member of the PI3K family. Therefore, in this study, PIK3CG was targeted to inhibit cancer by identifying a novel inhibitor through computational methods. The study screened 1015 chemical fragments against PIK3CG using machine learning-based binding estimation and docking to select the potential compounds. Later, the analogues were generated from the selected hits, and 414 analogues were selected, which were further screened, and as most potential candidates, three compounds were obtained: (a) 84,332, 190,213, and 885,387. The protein-ligand complex\'s stability and flexibility were then investigated by dynamic modeling. The 100 ns simulation revealed that 885,387 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 885,387 demonstrated a superior binding free energy (ΔG = -18.80 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 885,387 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the PIK3CG target implicated in cancer.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Brucellosis, a zoonotic disease caused by Brucella, presents a significant threat to both animal and human health. In animals, the disease can lead to infertility, miscarriage, and high fever, while in humans, symptoms may include recurrent fever, fatigue, sweating, hepatosplenomegaly, and joint and muscle pain following infection. Treatment often involves long-term antibiotic therapy, placing a substantial psychological and financial burden on patients. While vaccination is crucial for prevention, current animal vaccines have drawbacks such as residual virulence, and a safe and effective human vaccine is lacking. Hence, the development of a vaccine for brucellosis is imperative. In this study, we utilized bioinformatics methods to design a multi-epitope vaccine targeting Brucella. Targeting Heme transporter BhuA and polysaccharide export protein, we identified antigenic epitopes, including six cytotoxic T lymphocyte (CTL) dominant epitopes, six helper T lymphocyte (HTL) dominant epitopes, one conformation B cell dominant epitope, and three linear B cell dominant epitopes. By linking these epitopes with appropriate linkers and incorporating a Toll-like receptor (TLR) agonist (human beta-defensin-2) and an auxiliary peptide (Pan HLA-DR epitopes), we constructed the multi-epitope vaccine (MEV). The MEV demonstrated high antigenicity, non-toxicity, non-allergenicity, non-human homology, stability, and solubility. Molecular docking analysis and molecular dynamics simulations confirmed the interaction and stability of the MEV with receptors (MHCI, MHCII, TLR4). Codon optimization and in silico cloning validated the translation efficiency and successful expression of MEV in Escherichia coli. Immunological simulations further demonstrated the efficacy of MEV in inducing robust immune responses. In conclusion, our findings suggest that the engineered MEVs have the potential to stimulate both humoral and cellular immune responses, offering valuable insights for the future development of safe and efficient Brucella vaccines.

For a series of tropolones, the nature of the intramolecular O-H×××O=C hydrogen bond closing the five-membered quasi-cycle was studied. Enhancement of conjugation in the hydrogen-bonded rotamer was revealed. Quantification of hydrogen bond energy in tropolones via the molecular tailoring approach yields values in the range from 15 to 20 kcal/mol suggesting that the intramolecular interaction in tropolones has nature of the resonance-assisted hydrogen bond. The total resonance-assisted hydrogen bond energy in the tropolones was divided into σ- and π-components. The magnitudes of total energy of resonance-assisted hydrogen bond in the substituted tropolones can be controlled by the electronic properties of the substituents at the tropone ring. In 3-, 4-, and 5-substituted tropolones, the resonance-assisted hydrogen bond energy is raised due to electron-donating substituents and lowered due to electron-withdrawing ones. The opposite trend is observed in 7-substituted tropolones. The size of the π-shares plays a crucial role in establishing the total energy of resonance-assisted hydrogen bond. The reason for the occurrence of a resonance-assisted hydrogen bond in the tropolones is the molecular backbone aromaticity, since, in accordance with the Hückel rule, 10 π-electrons are delocalized.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

The data on the synthesis of N-aminomorpholine hydrazones are presented. It is shown that the interaction of N-aminomorpholine with functionally substituted benzaldehydes and 4-pyridinaldehyde in isopropyl alcohol leads to the formation of corresponding hydrazones. The structure of the synthesized compounds was studied by 1H and 13C NMR spectroscopy methods, including the COSY (1H-1H), HMQC (1H-13C) and HMBC (1H-13C) methodologies. The values of chemical shifts, multiplicity, and integral intensity of 1H and 13C signals in one-dimensional NMR spectra were determined. The COSY (1H-1H), HMQC (1H-13C), and HMBC (1H-13C) results revealed homo- and heteronuclear interactions, confirming the structure of the studied compounds. The antiviral, cytotoxic, and antimicrobial activity of some synthesized hydrazones were investigated. It is shown that 2-((morpholinoimino)methyl)benzoic acid has a pronounced viral inhibitory property, comparable in its activity to commercial drugs Tamiflu and Remantadine. A docking study was performed using the influenza virus protein models (1930 Swine H1 Hemagglutinin and Neuraminidase of 1918 H1N1 strain). The potential binding sites that are complementary with 2-((morpholinoimino)methyl)benzoic acid were found.

We present a methodology for the synthesis of inorganic-organic Janus-type molecules based on mono-T8 and difunctionalized double-decker silsesquioxanes (DDSQs) via hydrosilylation reactions, achieving exceptionally high yields and selectivities. The synthesized compounds were extensively characterized using various spectroscopic techniques, and their sizes and spatial arrangements were predicted through molecular modelling and density functional theory (DFT) calculations. Quantum chemical calculations were employed to examine the interactions among four molecules of the synthesized compounds. These computational results allowed us to determine the propensity for molecular aggregation, identify the functional groups involved in these interactions, and understand the changes in interatomic distances during aggregation. Understanding the aggregation behaviour of silsesquioxane molecules is crucial for tailoring their properties for specific applications, such as nanocomposites, surface coatings, drug delivery systems, and catalysts. Through a combination of experimental and computational approaches, this study provides valuable insights into the design and optimization of silsesquioxane-based Janus-type molecules for enhanced performance across various fields.

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.Scientific ContributionTo our knowledge, there is currently no open computational pipeline for differential pair interaction energies at all. This work aims to contribute an (at least academically available, open) approach based on molecular force fields that provides a robust and efficient computational scheme for their automated calculation for small to medium-sized (organic) molecular dimers. The usefulness of the proposed new calculation scheme is demonstrated for the generation of mesoscopic particles with their mutual repulsive interactions.

In this chapter, we predict the structure of the Arabidopsis receptor-homology-transmembrane-RING-H2 isoform 1 (RMR1) in complex with the C-terminal sorting determinant of cruciferin (CRU1) by AlphaFold2 using the ColabFold web interface and to perform molecular dynamics simulation to probe the dynamics of the predicted structures. Our results predict that the C-terminal carboxylate group of ctVSD of CRU1 is recognized by the conserved Arg89 of the cargo-binding loop of RMR1 and Arg468 of CRU1 by negative charge residues in the cargo-binding pocket of RMR1. The procedures described here are useful for modeling of other protein complexes.