Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols were further validated by Molecular Docking and via ADME and Toxicity predictions. The shortlisted compounds were then subjected to MD Simulations of the membrane-bound FFAR4-ligand complexes for 100 ns each. Molecular analyses, encompassing binding interactions, RMSD, RMSF, RoG, PCA, and FEL, were conducted to scrutinize the protein-ligand complexes at the inter-atomic level. The analyses revealed significant interactions of the shortlisted compounds with the crucial residues of FFAR4 previously documented. FFAR4 as part of the complexes demonstrated consistent RMSDs, ranging from 3.57 to 3.64, with minimal residue fluctuations 5.27 to 6.03 nm, suggesting stable complexes. The gyration values fluctuated between 22.8 to 23.5 nm, indicating structural compactness and orderliness across the studied systems. Additionally, distinct conformational motions were observed in each complex, with energy contours shifting to broader energy basins throughout the simulation, suggesting thermodynamically stable protein-ligand complexes. The two compounds CHEMBL2012662 and CHEMBL64616 are presented as potential FFAR4 agonists, based on these insights and in-depth analyses. Collectively, these findings advance our comprehension of FFAR4's functions and mechanisms, highlighting these compounds as potential FFAR4 agonists worthy of further exploration as innovative treatments for metabolic and immune-related conditions.

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.

Structure-Based Virtual Screening to Identify Negative Allosteric Modulators of NMDA.

BACKGROUND: NMDA (N-methyl-D-aspartate) receptor is one of the ionotropic receptor subtypes of glutamate, the most abundant excitatory neurotransmitter in the human brain. Besides physiological roles in learning and memory, neuronal plasticity and somatosensory function NMDAR overstimulation are also implicated in a pathophysiological mechanism of 'excitotoxicity.' In this study, an allosteric site has been focused on to design inhibitors of the most abundant form of this receptor of utility in many acute (stroke, traumatic brain injury) and chronic neurodegenerative diseases such as Parkinson's disease, Huntington's, Alzheimer's, and others. METHODS: In order to target this specific site at the interdimer interface of the ligand-binding domain of GluN2A-containing NMDA-Rs, blood-brain barrier-permeable potentially therapeutic compounds, as opposed to only pharmacological tools currently available, were sought. Pharmacophorebased virtual screening, docking, computational ADME prediction techniques, and MD simulation studies were used. RESULTS: Proceeding through the in-silico methodology, the study was successful at reaching 5 compounds from ChEMBL Database, which were predicted to be potential NMDA inhibitor drugs. CONCLUSION: The products of the study are compounds that have been validated through pharmacophore and score-based screening and MD simulation techniques to be allosterically inhibiting NMDA receptors and with favorable pharmacokinetic profiles. They are likely to be therapeutic agents ready for in-vitro and in-vivo testing.

Neuroprotective role of Ginkgo biloba against cognitive deficits associated with Bisphenol A exposure: An animal model study.

Our study aimed to elucidate to what extent Ginkgo biloba (Gb) can protect rats from cognitive deficits induced by exposure to Bisphenol A (BPA) at high dose. Therefore, sixty male Wistar rats were randomly divided into four groups of 15 animals in each group: Vehicle group, Gb-control group, BPA-exposed group and Gb pre-treated group. All administrations were given daily by an oral gavage once a day for eight weeks. Cognitive function was assessed using Morris water maze; Y-maze and Novel object recognition tasks. Additionally, hippocampal levels of DA, NE and 5-HT were measured. BPA-induced oxidative stress was evaluated by determining SOD activity, NO and MDA levels in rat hippocampus as well as level of circulating adiponectin. Moreover, histopathological changes in CA3 region of rat hippocampus and immunohistochemical expression of NF-κB and Caspase-3 were investigated. We found that Gb pretreatment significantly improved cognitive performance; may be via increasing hippocampal levels of estrogen-dependent biogenic amines. At the same time, Gb could strictly control BPA-induced oxidative stress by improving SOD activity and adiponectin level with decrease in NO and MDA levels. Lastly, Gb alleviated the histopathological injuries induced by BPA and inhibited NF-κB and caspase-3 activation. In conclusion, our results suggested that Gb has potential to ameliorate BPA-induced hippocampal neuronal damage and subsequent cognitive deficits through mechanisms involving its ability to enhance the release of biogenic amines as well as its antioxidant and adiponectin pro-secretory effects.

Serological detection and epidemiology of Neospora caninum and Cryptosporidium parvum antibodies in cattle in southern Egypt.

Neospora caninum and Cryptosporidium parvum are intracellular protozoan parasites that are distributed worldwide and of major economical concern in cattle industry. N. caninum can cause abortion storms and high culling rates, whereas C. parvum has zoonotic implications and can cause diarrhea in calves. There are currently no data on the prevalence of neosporosis and cryptosporidiosis in humans or animals in southern Egypt. Prevalence of these two infections was determined in a sample of cattle from two different areas in southern Egypt, Sohag and Qena, using enzyme-linked immunosorbent assay. A total 301 cattle were sampled, of which 18.9% were positive for N. caninum, 35.9% were positive for C. parvum and 10.0% were positive for both. Geographical location and breeding system were considered as potential risk factors for C. parvum infection. A higher prevalence of infection was identified on small scale farms, compared with larger, intensive systems, with a prevalence of 50.2% compared with 37.8%, respectively. Animals in Sohag had a significantly higher prevalence compared with Qena, with a seroprevalence of 46.1% compared with 31.6%, respectively. In brief, marked seroprevalence recorded in this study indicates a high incidence of N. caninum and C. parvum infections in cattle, and this necessitates the application of more effective strategies for combating these types of infections on farms in Egypt.