Large-scale computational screening of Indian medicinal plants reveals Cassia angustifolia to be a potentially anti-diabetic.

Researchers are investigating the medicinal properties of herbal plants throughout the world, which often leads to the discovery of novel plants and their chemicals for prophylactic needs of humans. Natural phytochemicals continue to be sought as alternative treatments for various diseases because of their non-toxic and therapeutic properties. In recent years, computational phytochemistry has enabled large-scale screening of phytochemicals, enabling researchers to pursue a wide range of therapeutic research alternatives to traditional ethnopharmacology. We propose to identify an anti-diabetic plant by computational screening on Indian herbal plants in conjunction with experimental characterization and biological validation. The methodology involves the creation of an in-house Indian herbal plant database. Molecular docking is used to screen against alpha amylase for anti-diabetic prophylaxis. Cassia angustifolia was chosen because its phytochemicals are able to bind to alpha amylase. Plants were experimentally extracted, botanically studied and their biological activity was evaluated. Further, the use of molecular dynamics was then applied to pinpoint the phytochemicals responsible for the affinity of alpha amylase. Results in the phytochemical analysis of the extracts revealed strong presence of alkaloids, flavonoids and cardiac glycosides. Moreover, alpha amylase biological activity with C. angustifolia extracts of chloroform, hexane and ethyl acetate demonstrated activity of 3.26, 8.01 and 30.33 µg/ml validating computational predictions. In conclusion, this study developed, validated computational predictions of identifying potential anti-diabetic plants 'Cassia angustifolia' from house herbal databases. Hope this study shall inspire explore plant therapeutic repurposing using computational methods of drug discovery.Communicated by Ramaswamy H. Sarma.

In-house database phytochemicals preparation using Indian medicinal plants for repurposing plant therapeutics screening.Virtual screening of in-house database against alpha amylase for anti-diabetic therapeutics.The highest affinity plants Cassia angustifolia were identified, collected, processed four solvent extracts, along with qualitative and quantitative estimations.All plant extracts are subjected to botanical and biological experimental perspective.Advanced molecular dynamics simulations are used to understand the non-bonding interactions of phytochemicals with alpha amylase.

Assessment of factors related to poly cystic ovarian syndrome - A comparative and correlational study.

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder that primarily affects women of reproductive age. It is particularly prevalent among adolescent females who receive an insufficient diagnosis despite having potentially adverse consequences. The use of PCOS screening questionnaires has the potential to aid in the early detection of symptoms. The goal of this study is to observe if a self-administered questionnaire may be useful for a clear cognizance of the associated conditions like mental stress and menstrual characteristics correlated to polycystic ovary syndrome. In this study, we selected women within an age group of 17-40 with and without PCOS based on the modified Rotterdam criteria to fill out a self-administrated questionnaire based on the signs and symptoms of PCOS majorly focusing on mental stress and menstrual characteristics. SPSS software, univariate analyses were employed to elucidate the associations among the components of PCOS, demographic factors, and lifestyle characteristics, hence providing insights into the interrelationships among those variables. 64 women with PCOS and 141 women without PCOS participated in the present study. The present study revealed PCOS is greatly influenced by age at menarche (p-value= .043), typical cycle length (p-value = .000) mental health problems during menstruation (p-value = .032), and body mass index (p-value = .001). Multivariate hierarchical logistic regression analysis showed only 2 variables BMI (a-OR 1.156,95% CI (1.067-1.242), p-value = .000), and typical cycle length (a-OR 2.278, 95% CI (1.079-4.809), p-value = .003) were significant. The present study showed that BMI and menstrual cycle length were most closely associated with the incidence of PCOS, which is important in diagnosing and treating the condition. Considering the high incidence of PCOS among women of reproductive age and its potential for significant health implications, it would be prudent to incorporate inquiries regarding mental health concerns and menstrual patterns into routine medical assessments for this demographic analysis. This approach aims to ascertain whether additional diagnostic evaluations and screenings for PCOS are warranted.

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation.

Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design.Communicated by Ramaswamy H. Sarma.

Investigating anti-inflammatory and apoptotic actions of fucoidan concentrating on computational and therapeutic applications.

Fucoidan is linked to a variety of biological processes. Differences in algae species, extraction, seasons, and locations generate structural variability in fucoidan, affecting its bioactivities. Nothing is known about fucoidan from the brown alga Dictyota bartayresiana, its anti-inflammatory properties, or its inherent mechanism. This study aimed to investigate the anti-inflammatory properties of fucoidan isolated from D. bartayresiana against LPS-induced RAW 264.7 macrophages and to explore potential molecular pathways associated with this anti-inflammatory effects. Fucoidan was first isolated and purified from D. bartayresiana, and then, MTT assay was used to determine the effect of fucoidan on cell viability. Its effects on reactive oxygen species (ROS) formation and apoptosis were also studied using the ROS assay and acridine orange/ethidium bromide fluorescence labelling, respectively. Molecular docking and molecular dynamics simulation studies were performed on target proteins NF-κB and TNF-α to identify the route implicated in these inflammatory events. It was observed that fucoidan reduced LPS-induced inflammation in RAW 264.7 cells. Fucoidan also decreased the LPS-stimulated ROS surge and was found to induce apoptosis in the cells. Molecular docking and molecular dynamics simulation studies revealed that fucoidan's potent anti-inflammatory action was achieved by obstructing the NF-κB signalling pathway. These findings were particularly noteworthy and novel because fucoidan isolated from D. bartayresiana had not previously been shown to have anti-inflammatory properties in RAW 264.7 cells or to exert its activity by obstructing the NF-κB signalling pathway. Conclusively, these findings proposed fucoidan as a potential pharmaceutical drug for inflammation-related diseases.

Investigating anti-inflammatory actions of marine algal compound against lipoxygenase concentrating on therapeutic applications through computational approach.

Inflammation is the preliminary response given to any possible harmful stimuli including infections, injury or stress by immune system where neutrophils and macrophages gets activated and produces mediators, such as nitric oxide and cytokines that serves as biomarkers of inflammation. Lipoxygenases are enzymes that peroxidises lipids and are involved in the pathogenesis of several diseases including inflammatory diseases. These are oxidative enzymes comprising a non-heme iron atom in active site and are convoluted in inflammatory reactions. Fucoidan is sulphated polysaccharide that has numerous pharmacological implications. Implications of fucoidan on inflammatory diseases are still an objective of rigorous research. Therefore, this study focusses on investigating lipoxygenase inhibitory activities of fucoidan. The mechanism of lipoxygenase inhibitory activities of fucoidan was studied via molecular docking and molecular dynamics simulations. The docking score produced by the binding of the fucoidan to the lipoxygenase was - 6.69 kcal/mol whereas, the docking score in case of Aspirin and Zileuton were -5.8 kcal/mol and -7.0 kcal/mol and it was found that fucoidan makes hydrogen bonds with lipoxygenase protein through polar amino acid glutamine at GLN 514. The results obtained from molecular dynamics simulations proposed the development of a stable complex between fucoidan and lipoxygenase due to the establishment of favourable interactions with amino acid residues and indicated efficient results when compared with Aspirin and Zileuton. This study suggested that fucoidan had anti-inflammatory potentials and thus can be used as a promising drug candidate against inflammation.Communicated by Ramaswamy H. Sarma.

Determination and analysis of agonist and antagonist potential of naturally occurring flavonoids for estrogen receptor (ERα) by various parameters and molecular modelling approach.

Most estrogen receptor α (ERα) ligands target the ligand binding domain (LBD). Agonist 17ß-estradiol (E2) and tamoxifen (TM, known SERM), bind to the same site within the LBD. However, structures of ligand-bound complexes show that E2 and TM induce different conformations of helix 12 (H12). During the molecular modelling studies of some naturally occurring flavonoids such as quercetin, luteolin, myricetin, kaempferol, naringin, hesperidin, galangin, baicalein and epicatechin with human ERα (3ERT and 1GWR), we observed that most of the ligands bound to the active site pocket of both 3ERT and 1GWR. The docking scores, interaction analyses, and conformation of H12 provided the data to support for the estrogenic or antiestrogenic potential of these flavonoids to a limited degree. Explicit molecular dynamics for 50 ns was performed to identify the stability and compatibility pattern of protein-ligand complex and RMSD were obtained. Baicalein, epicatechin, and kaempferol with 1GWR complex showed similar RMSD trend with minor deviations in the protein backbone RMSD against 1GWR-E2 complex that provided clear indications that ligands were stable throughout the explicit molecular simulations in the protein and outcome of naringin-3ERT complex had an upward trend but stable throughout the simulations and all molecular dynamics showed stability with less than overall 1 Å deviation throughout the simulations. To examine their estrogenic or antiestrogenic potential, we studied the effect of the flavonoids on viability, progesterone receptor expression and 3xERE/3XERRE-driven reporter gene expression in ERα positive and estrogen responsive MCF-7 breast cancer cells. Epicatechin, myricetin, and kaempferol showed estrogenic potential at 5 µM concentration.

Induction of ROS-dependent mitochondria-mediated intrinsic apoptosis in MDA-MB-231 cells by glycoprotein from Codium decorticatum.

Marine macroalgae consist of a range of bioactive molecules exhibiting different biological activities, and many of these properties are attributed to sulfated polysaccharides, fucoxanthin, phycobiliproteins, and halogenated compounds. In this study, a glycoprotein (GLP) with a molecular mass of ∼48 kDa was extracted and purified from Codium decorticatum and investigated for its cytotoxic properties against human MDA-MB-231 breast cancer cells. The IC50 values of GLP against MDA-MB-231 and normal breast HBL-100 cells (control) were 75 ± 0.23 µg/mL (IC25), 55 ± 0.32 µg/mL (IC50), and 30 ± 0.43 µg/mL (IC75) and 90 ± 0.57 µg/mL (IC25), 80 ± 0.48 µg/mL (IC50), and 60 ± 0.26 µg/mL (IC75), respectively. Chromatin condensation and poly(ADP-ribose) polymerase (PARP) cleavage studies showed that the GLP inhibited cell viability by inducing apoptosis in MDA-MB-231 cells. Induction of mitochondria-mediated intrinsic apoptotic pathway by GLP was evidenced by the events of loss of mitochondrial membrane potential (ΔΨ(m)), bax/bcl-2 dysregulation, cytochrome c release, and activation of caspases 3 and 9. Apoptosis-associated factors such as reactive oxygen species (ROS) formation and loss of ΔΨ(m) were evaluated by DCFH-DA staining and flow cytometry, respectively. Cell cycle arrest of G2/M phase and expression of apoptosis associated proteins were determined using flow cytometry and Western blotting, respectively.