Gene network analysis combined with preclinical studies to identify and elucidate the mechanism of action of novel irreversible Keap1 inhibitor for Parkinson's disease.

The cysteine residues of Keap1 such as C151, C273, and C288 are critical for its repressor activity on Nrf2. However, to date, no molecules have been identified to covalently modify all three cysteine residues for Nrf2 activation. Hence, in this study, our goal is to discover new Keap1 covalent inhibitors that can undergo a Michael addition with all three cysteine residues. The Keap1's intervening region was modeled using Modeller v10.4. Covalent docking and binding free energy were calculated using CovDock. Molecular dynamics (MD) was performed using Desmond. Various in-vitro assays were carried out to confirm the neuroprotective effects of the hit molecule in 6-OHDA-treated SH-SY5Y cells. Further, the best hit was evaluated in vivo for its ability to improve rotenone-induced postural instability and cognitive impairment in male rats. Finally, network pharmacology was used to summarize the complete molecular mechanism of the hit molecule. Chalcone and plumbagin were found to form the necessary covalent bonds with all three cysteine residues. However, MD analysis indicated that the binding of plumbagin is more stable than chalcone. Plumbagin displayed neuroprotective effects in 6-OHDA-treated SH-SY5Y cells at concentrations 0.01 and 0.1 µM. Plumbagin at 0.1 µM had positive effects on reactive oxygen species formation and glutathione levels. Plumbagin also improved postural instability and cognitive impairment in rotenone-treated male rats. Our network analysis indicated that plumbagin could also improve dopamine signaling. Additionally, plumbagin could exhibit anti-oxidant and anti-inflammatory activity through the activation of Nrf2. Cumulatively, our study suggests that plumbagin is a novel Keap1 covalent inhibitor for Nrf2-mediated neuroprotection in PD.

Amyloid transformations of phenol soluble modulin α1 in Staphylococcus aureus and their modulation deploying a prenylated chalcone.

Phenol soluble modulins (PSMs) are small amphipathic peptides involved in a series of biological functions governing staphylococcal pathogenesis, primarily by facilitating the formation of an extracellular fibril structure with amyloid-like properties. This fibrillar architecture stabilizes the staphylococcal biofilm making it resilient to antibiotic treatment. Our study aims to abrogate the amyloid fibrillation of PSM α1 with novel insights on the amyloid modulatory potential of a prenylated chalcone, Isobavachalcone (IBC). A combination of biophysical and computational assays to address the amyloid modulatory effect of IBC has been undertaken to arrive at a model for the inhibition of PSM α1 fibrillation. ThT kinetics studies indicated that IBC must be stably interacting with the amyloidogenic core of PSM α1 monomers or it may be inhibiting the pre-fibrillar aggregates populated at the early stages of amyloid transformation kinetics. This heteromolecular association further inhibits the amyloid transformation corroborated by a ∼ 94% and ∼ 91% reduction in the ThT maxima, even at sub-stoichiometric concentrations. Transmission electron microscopy (TEM) of end-stage aggregates (∼ 55 h) depict mature, inter-twined, laterally stacked amyloid fibrils in untreated PSM α1 samples while this fibrillar load is remarkably reduced in the presence of IBC. The inhibitory effect of IBC on the ß-sheet transitions of PSM α1 were also validated using far-UV CD spectra. Molecular dynamics simulation studies with PSM aggregates (PSM-A) have also suggested that IBC disrupts the hydrogen bonding interactions and corroborates the inhibition of alpha to beta transitions of PSM-A. Collectively, our data proposes a novel structural motif for the rational discovery of non-toxic therapeutic agents targeting the functional amyloids which have slowly emerged as potent factors, consolidating the antibiotic resistant staphylococcal biofilm assembly.

Molecular insights into the potential effects of selective estrogen receptor ß agonists in Alzheimer's and Parkinson's diseases.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders. Pathologically, AD and PD are characterized by the accumulation of misfolded proteins. Hence, they are also called as proteinopathy diseases. Gender is considered as one of the risk factors in both diseases. Estrogens are widely accepted to be neuroprotective in several neurodegenerative disorders. Estrogens can be produced in the central nervous system, where they are called as neurosteroids. Estrogens mediate their neuroprotective action mainly through their actions on estrogen receptor alpha (ERα) and estrogen receptor beta (ERß). However, ERα is mainly involved in the growth and development of the primary and secondary sexual organs in females. Hence, the activation of ERα is associated with undesired side effects such as gynecomastia and increase in the risk of breast cancer, thromboembolism, and feminization. Therefore, selective activation of ERß is often considered to be safer. In this review, we explore the role of ERß in regulating the expression and functions of AD- and PD-associated genes. Additionally, we discuss the association of these genes with the amyloid-beta peptide (Aß) and α-synuclein mediated toxicity. Ultimately, we established a correlation between the importance of ERß activation and the process underlying ERß's neuroprotective mechanisms in AD and PD.

Network-based drug repositioning of linagliptin as a potential agent for uterine fibroids targeting transforming growth factor-beta mediated fibrosis.

Uterine fibroid is the most common non-cancerous tumor with no satisfactory options for long-term pharmacological treatment. Fibroblast activation protein-α (FAP) is one of the critical enzymes that enhances the fibrosis in uterine fibroids. Through STITCH database mining, we found that dipeptidyl peptidase-4 inhibitors (DPP4i) have the potential to inhibit the activity of FAP. Both DPP4 and FAP belong to the dipeptidyl peptidase family and share a similar catalytic domain. Hence, ligands which have a binding affinity with DPP4 could also bind with FAP. Among the DPP4i, linagliptin exhibited the highest binding affinity (Dock score = -8.562 kcal/mol) with FAP. Our study uncovered that the differences in the S2 extensive-subsite residues between DPP4 and FAP could serve as a basis for designing selective inhibitors specifically targeting FAP. Furthermore, in a dynamic environment, linagliptin was able to destabilize the dimerization interface of FAP, resulting in potential inhibition of its biological activity. True to the in-silico results, linagliptin reduced the fibrotic process in estrogen and progesterone-induced fibrosis in rat uterus. Furthermore, linagliptin reduced the gene expression of transforming growth factor-ß (TGF-ß), a critical factor in collagen secretion and fibrotic process. Masson trichrome staining confirmed that the anti-fibrotic effects of linagliptin were due to its ability to reduce collagen deposition in rat uterus. Altogether, our research proposes that linagliptin has the potential to be repurposed for the treatment of uterine fibroids.

The identification of cianidanol as a selective estrogen receptor beta agonist and evaluation of its neuroprotective effects on Parkinson's disease models.

AIM: The present study aims to identify selective estrogen receptor beta (ERß) agonists and to evaluate the neuroprotective mechanism in Parkinson's disease (PD) models. MAIN METHODS: In-silico studies were carried out using Maestro and GROMACS. Neuroprotective activity and apoptosis were evaluated using cytotoxicity assay and flow cytometry respectively. Gene expression studies were carried out by reverse transcription polymerase chain reaction. Motor and cognitive functions were assessed by actophotometer, rotarod, catalepsy, and elevated plus maze. The neuronal population in the substantia nigra and striatum of rats was assessed by hematoxylin and eosin staining. KEY FINDINGS: Cianidanol was identified as a selective ERß agonist through virtual screening. The cianidanol-ERß complex is stable during the 200 ns simulation and was able to retain the interactions with key amino acid residues. Cianidanol (25 µM) prevents neuronal toxicity and apoptosis induced by rotenone in differentiated SH-SY5Y cells. Additionally, cianidanol (25 µM) increases the expression of ERß, cathepsin D, and Nrf2 transcripts. The neuroprotective effects of cianidanol (25 µM) were reversed in the presence of a selective ERß antagonist. In this study, we found that selective activation of ERß could decrease the transcription of α-synuclein gene. Additionally, cianidanol (10, 20, 30 mg/kg, oral) improves the motor and cognitive deficit in rats induced by rotenone. SIGNIFICANCE: Cianidanol shows neuroprotective action in PD models and has the potential to serve as a novel therapeutic agent for the treatment of PD.

Side effects based network construction and drug repositioning of ropinirole as a potential molecule for Alzheimer's disease: an in-silico, in-vitro, and in-vivo study.

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Drug repositioning is a process of finding new therapeutic applications for existing drugs. One of the methods in drug repositioning is to use the side-effect profile of a drug to identify a new therapeutic indication. The drugs with similar side-effects may act on similar biological targets and could affect the same biochemical process. In this study, we explored the Food and Drug Administration-approved drugs using PROMISCUOUS database to find those that have adverse effects profile comparable with the ligands being studied or used to treat AD. Here, we found that the ropinirole, a dopamine receptor agonist, shared a maximum number of side-effects with the drugs proven beneficial for treating AD. Furthermore, molecular modelling demonstrated that ropinirole exhibited strong binding affinity (-9.313 kcal/mol) and best ligand efficiency (0.49) with sigma-1 receptor. Here, we observed that the quaternary amino group of ropinirole is essential for binding with sigma-1 receptor. Molecular dynamic simulation indicated that the movement of the carboxy-terminal helices (α4/α5) could play a major role in the receptor's physiological functions. The neurotoxicity induced by Aß25-35 in SH-SY5Y cells was reduced by ropinirole at concentrations 10, 30, and 50 µM. The effect on spatial learning and memory was examined in mice with Aß25-35 induced memory deficit using the radial arm maze. Ropinirole (10 and 20 mg/kg) significantly improved the short and long-term memories in the radial arm maze test. Our results suggest that ropinirole has the potential to be repositioned for AD treatment.Communicated by Ramaswamy H. Sarma.

A quinoline alkaloid potentially modulates the amyloidogenic structural transitions of the biofilm scaffolding small basic protein.

Bacterial biofilm formation by communities of opportunistic bacterial pathogens like Staphylococcus epidermidis is regarded as the primary virulence mechanism facilitating the spread of detrimental nosocomial and implant-associated infections. An 18-kDa small basic protein (Sbp) and its amyloid fibrils account for strengthening the biofilm architecture and scaffolding the S. epidermidis biofilm matrix. Our study reports systematic analysis of the amyloidogenic structural transitions of Sbp and predicts the amyloid core of the protein which may trigger misfolding and aggregation. Herein, we report the novel amyloid inhibitory potential of Camptothecin, a quinoline alkaloid which binds stably to Sbp monomers and redirects the formation of unstructured regions further destabilizing the protein. Molecular dynamics simulations reveal that Camptothecin averts ß-sheet transitions, interrupts with electrostatic interactions and disrupts the intermolecular hydrophobic associations between the exposed hydrophobic amyloidogenic regions of Sbp. Collectively, our study puts forward the first report detailing the heteromolecular associations and amyloid modulatory effects of Camptothecin which may serve as a structural scaffold for the tailored designing of novel drugs targeting the S. epidermidis biofilm matrix.Communicated by Ramaswamy H. Sarma.

Enterococcus faecalis thrives in dual-species biofilm models under iron-rich conditions.

Escherichia coli (E. coli) and Enterococcus faecalis (E. faecalis) are pathogenic strains that often coexist in intestinal flora of humans and are prone to cause biofilm-associated infections, such as gastrointestinal tract and urinary tract infections. Earlier studies have demonstrated that E. faecalis biofilm can metabolize ferrous ions in iron-rich environments and promote biofilm growth under in-vivo conditions. However, the influence of iron transporters on dual-species biofilm growth and the nature of molecular-level interactions between iron transporter proteins and Fe2+ remains unknown. Therefore, in this work, co-culture studies were performed and the study indicates that Fe2+ at concentrations of 50-150 µM promotes the colonization of E. coli, and Fe2+ concentrations of 50-200 µM promote the growth of E. faecalis and dual-species colonies. Atomic absorption spectroscopy results reveal that Fe2+ ion augmentation in bacterial cells was increased to 4 folds in the single-species model and 11 folds in the dual-species model under iron-supplemented conditions. Furthermore, Fe2+ augmentation increased the antibiotic resistance of E. faecalis in both single- and dual-species bacterial cultures. In addition, in-silico docking were performed to determine a three-dimensional (3D) structure of ferrous iron-transporter proteins FeoB of E. faecalis and its affinity to extracellular Fe2+. Our model suggests that the FeoB facilitates the Fe2+ uptake in E. faecalis cells in the absence of iron chelator, 2,2-bipyridyl.

Improved laccase production from Pleurotus floridanus using deoiled microalgal biomass: statistical and hybrid swarm-based neural networks modeling approach.

Fungal laccases are versatile biocatalyst and occupy a prominent place in various industrial applications due to its broad substrate specificity. The simplest method to enhance the laccase production is by usage of cheap substrates in the fermentation processes incorporating modeling approaches for optimization. Integrated biorefinery concept is receiving wide popularity by making use of various products from microalgal biomass. The research aimed to identify the potential of deoiled microalgal biomass (DMB), a waste product from algal biorefinery as a nutrient supplement to enhance laccase production in Pleurotus floridanus by submerged fermentation. The maximum production was obtained in the presence of DMB as an additional nutrient supplement and copper sulfate as an inducer. The predictive capabilities of the two methodologies Response Surface Methodology (RSM) and hybrid Particle swarm optimization (PSO)-based Artificial Neural Network (ANN) were compared and validated. The results showed that ANN coupled with PSO predicted with more accuracy with an R 2 value of 0.99 than the RSM model with an R 2 value of 0.97. The optimized condition as predicted by superior model hybrid PSO-based ANN was glucose (3.51%), DMB (0.545%), pH (4.9), temperature (24.68 â„ƒ) and CuSO4 (1.35 mM). The experimental laccase activity was 80.45 ± 0.132 U/mL which was 1.3 fold higher than unoptimized condition. This study promotes the usage of DMB as a novel supplement for the improved production of Pleurotus floridanus laccase. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03404-y.

Search for RNA aptamers against non-structural protein of SARS-CoV-2: Design using molecular dynamics approach.

BACKGROUND: Recent outbreak of deadly Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) urges the scientist to identify the potential vaccine or drug to control the disease. SARS-CoV-2 with its single stranded RNA genome (length ~ 30 kb) is enveloped with active spike proteins. The genome is non-segmental with 5'-cap and 3'-poly tail and acts as a mRNA for the synthesis of replicase polyproteins. The replicase gene lying downstream to 5'-end encodes for non-structural protein, which in turn pose multiple functions ranging from envelope to nucleocapsid development. This study aims to identify the highly stable, effective and less toxic single strand RNA-based aptamers against non-structural protein 10 (NSP10). NSP10 is the significant activator of methyltransferase enzymes (NSP14 and NSP16) in SARS-CoV-2. Inhibiting the activation of methyltransferase leads to partial viral RNA capping or lack of capping, which makes the virus particles susceptible to host defence system. RESULTS: In this study, we focused on designing RNA aptamers through computational approach, docking of protein-aptamer followed by molecular dynamics simulation to perceive the binding stability of complex. Docking study reveals the high binding affinity of three aptamers namely RNA-053, 001, 010 to NSP10 with the HADDOCK score of - 88.5 ± 7.0, - 87.7 ± 11.5, - 86.1 ± 12 respectively. Molecular Dynamics suggests high conformational stability between the aptamer and the protein. Among the screened aptamers two aptamers maintained at least 3-4 intermolecular H-bonds throughout the simulation period. CONCLUSIONS: The study identifies the potential aptamer candidate against less investigated but significant antiviral target i.e., NSP10/NSP16 interface complex. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43088-021-00152-5.

Virtual screening of phytochemical compounds as potential inhibitors against SARS-CoV-2 infection.

BACKGROUND: The present pandemic situation due to coronavirus has led to the search for newer prevention, diagnostic, and treatment methods. The onset of the corona infection in a human results in acute respiratory illness followed by death if not diagnosed and treated with suitable antiretroviral drugs. With the unavailability of the targeted drug treatment, several repurposed drugs are being used for treatment. However, the side-effects of the drugs urges us to move to a search for newer synthetic- or phytochemical-based drugs. The present study investigates the use of various phytochemicals virtually screened from various plant sources in Western Ghats, India, and subsequently molecular docking studies were performed to identify the efficacy of the drug in retroviral infection particularly coronavirus infection. RESULTS: Out of 57 phytochemicals screened initially based on the structural and physicochemical properties, 39 were effectively used for the docking analysis. Finally, 5 lead compounds with highest hydrophobic interaction and number of H-bonds were screened. Results from the interaction analysis suggest Piperolactam A to be pocketed well with good hydrophobic interaction with the residues in the binding region R1. ADME and toxicity profiling also reveals Piperolactam A with higher LogS values indicating higher permeation and hydrophilicity. Toxicity profiling suggests that the 5 screened compounds to be relatively safe. CONCLUSION: The in silico methods used in this study suggests that the compound Piperolactam A to be the most effective inhibitor of S-protein from binding to the GRP78 receptor. By blocking the binding of the S-protein to the CS-GRP78 cell surface receptor, they can inhibit the binding of the virus to the host. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43088-021-00095-x.

Modelling, docking and simulation analysis of Bisphenol A interaction with laccase from Trichoderma.

Fungal laccases are widely known for the degradation of recalcitrant xenobiotic compounds. Hence, it is of interest to study the interaction between laccase from Trichoderma laccase and Endocrine-Disrupting Chemical (EDC) named Bisphenol A. The molecular docking analysis of laccase from Trichoderma laccase with 23 xenobiotics and bisphenol A was completed. We show Bisphenol having optimal binding features (Glide score of -5.44 and the Glide energy -37.65 kcal/mol) with laccase from Trichoderma laccase.

Handling class imbalance problem in miRNA dataset associated with cancer.

MiRNAs are small (~22nt long) non-coding RNA sequences; binds to the complementarity target sites in 3' Untranslated Region (UTR) of mRNA sequences but not restricted to other mRNA regions viz., 5' UTR and Coding sequences (CDS). Complementarity binding of miRNA to mRNA target sites either results in complete degradation of the mRNA itself or it may regulate the mRNA as an oncogene or as a tumor suppressor gene. However, the exact mechanism involved in identifying a miRNA to be associated with cancer is still unclear. Further, with the outburst in the number of miRNAs sequences recorded every year in miRBase, the gap is still widening mainly due to the laborious and economically unfavorable experimental procedures associated with the functional annotation. Motivated by the fact, we constructed a two-step support vector machine-based predictive model - miRSEQ and miRINT. However, the major pitfall during the construction of the model is the class imbalance problem. Hence, in order to overcome class imbalance problem, in the present study we empirically compare the effectiveness of two different methods viz., Synthetic Minority Oversampling Technique (SMOTE) and cost-senstive learning method. Performance measures were evaluated in terms of Precision and Recall. Based on our result, it was observed that for miRNA dataset with high class imbalance utilized for predicting association of cancer, cost-sensitive method outperformed the oversampling method.

Identifying microRNAs involved in cancer pathway using support vector machines.

Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification - 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA-mRNA structure. The two step classifier model - miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew's correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools).

Search for signatures in miRNAs associated with cancer.

Since the first discovery in the early 1990's, the predicted and validated population of microRNAs (miRNAs or miRs) has grown significantly. These small (~22 nucleotides long) regulators of gene expression have been implicated and associated with several genes in the cancer pathway as well. Globally, the identification and verification of microRNAs as biomarkers for cancer cell types has been the area of thrust for most miRNA biologists. However, there has been a noticeable vacuum when it comes to identifying a common signature or trademark that could be used to demarcate a miR to be associated with the development or suppression of cancer. To answer these queries, we report an in silico study involving the identification of global signatures in experimentally validated microRNAs which have been associated with cancer. This study has thrown light on the presence of significant common signatures, viz., - sequential and hybridization, which may distinguish a miR to be associated with cancer. Based on our analysis, we suggest the utility of such signatures in the design and development of algorithms for prediction of miRs involved in the cancer pathway.