Exploring the binding properties of agonists interacting with glucocorticoid receptor: an in silico approach

From High Resolution Tandem Mass Spectrometry to Pollutant Toxicity AI-Based Prediction: A Case Study of 7 Endocrine Disruptors Endpoints

Based on high-resolution mass spectrometry (HRMS), nontarget analysis (NTA) can rapidly identify and characterize numerous hazardous substances in complex environmental samples. However, the intricate identification process often results in the underutilization of many mass spectrometry features. Even when chemical structures are identified, their toxicological effects and health outcomes may remain unknown. To address these challenges, this study introduces MSFragTox, a novel approach that leverages the rich fragmentation spectra inherent in high resolution tandem mass spectrometry (MS/MS) to directly predict toxicity. This method integrates MS/MS data with high-throughput screening (HTS) assays, focusing on seven endocrine disruption-related endpoints from Tox21, and uses MS-derived fingerprints: substructure fragmentation probability vectors to construct toxicity predictions using machine learning algorithms. The best model demonstrated robust performance with an average area under the receiver operating characteristic curve (AUROC) of 0.845 on the test set, outperforming models based on traditional molecular fingerprints and descriptors. Additionally, a web client (http://ms.envwind.site:8500) is provided for users to screen toxicity based on chemical MS/MS data. Furthermore, in-depth analyses of commonalities and differences in substructures reveal the mechanisms underlying across toxicity endpoints. Using MSFragTox, we validated the potential endocrine-disrupting effects of substances corresponding to MS/MS from real samples, highlighting the feasibility of directly studying toxicity through MS/MS and its potential applications in risk prediction and early warning for environmental samples.

Cracking a cancer code DNA methylation in epigenetic modification: an in-silico approach on efficacy assessment of Sri Lanka-oriented nutraceuticals

DNA methyltransferase (DNMTs) are essential epigenetic modifiers that play a critical role in gene regulation. These enzymes add a methyl group to cytosine's 5'-carbon, specifically within CpG dinucleotides, using S-adenosyl-L-methionine. Abnormal overexpression of DNMTs can alter the gene expression patterns and contribute to cancer development in the human body. Therefore, the inhibition of DNMT is a promising therapeutic approach to cancer treatment. This study was aimed to identify potential nutraceutical inhibitors from the Sri Lanka Flora database using computational methods, which provided an atomic-level description of the drug binding site and examined the interactions between nutraceuticals and amino acids of the DNMT enzyme. A series of nutraceuticals from Sri Lanka-oriented plants were selected and evaluated to assess their inhibitory effects on DNMT using absorption, distribution, metabolism, excretion and toxicity analysis, virtual screening, molecular docking, molecular dynamics simulation and trajectory analysis. Azacitidine, a DNMT inhibitor approved by the US Food and Drug Administration, was selected as a reference inhibitor. The complexes with more negative binding energies were selected and further assessed for their potency. Seven molecules were identified from 200 nutraceuticals, demonstrating significantly negative binding energies against the DNMT enzyme. Various trajectory analyses were conducted to investigate the stability of the DNMT enzyme. The results indicated that petchicine (NP#0003), ouregidione (NP#0011) and azacitidine increased the stability of the DNMT enzyme. Consequently, these two nutraceuticals showed inhibitory efficacies similar to azacitidine, making them potential candidates for therapeutic interventions targeting DNMT enzyme-related cancers. Additional bioassay testing is recommended to confirm the efficacies of these nutraceuticals and explore their applicability in clinical treatments.

Molecular docking and dynamic simulation approach to decipher steroidal sapogenins (genus Trillium) derived agonists for glucocorticoid receptor

Steroidal sapogenins (SS) are structural analogues of steroidal drugs, which are frequently used for the treatment of several diseases including reproductive, malignancies, neurological, and inflammation-related diseases. The glucocorticoid receptor (GR) is a nuclear receptor that regulates development, metabolism, and inflammation, in response to steroidal ligands. Therefore, GR is considered as a potential therapeutic target for steroidal agents to the treatment of inflammation-related diseases. We hypothesized that SS may act as an agonist for GR due to structural similarity with corticosteroids. In this study, we carried out in silico screening of various SS from the genus Trillium to check their potential as an agonist for GR. Our data suggest that out of 42 SS, only 7 molecules have interacted with GR. However, molecular mechanics with generalized Born and surface area (MM-GBSA) analysis revealed that only two SS (SS 38 and SS 39) molecules bind favorably to GR. Among these, SS 38 (docking score: -9.722 Kcal/mol and MM-GBSA ΔG_bind: -50.192 Kcal/mol) and SS 39 (docking score: -11.20 Kcal/mol and MM-GBSA ΔG_bind: -58.937 Kcal/mol) have best docking and MM-GBSA scores. Molecular dynamics (MD) simulation studies of SS 38, SS 39, and dexamethasone-GR complex revealed that both SS shows hydrogen bonding and hydrophobic interaction with GR over the 120 ns simulation with mild fluctuations. The current study suggests that SS 38 and SS 39 may be further explored as a potential agonist to treat several disease conditions mediated by GR.

Papaya Leaf Extracts as Potential Dengue Treatment: An In-Silico Study

Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) cause serious public health problems, with nearly 390 million people affected and 20,000 deaths per year in tropical and subtropical countries. Despite numerous attempts, no antiviral drug or vaccine is currently available to combat the manifestation. The challenge of discovering an efficient vaccine is enhanced by the surplus presence of efficient vectors and drug resistance from the virus. For centuries, papaya (Carica papaya) extracts have been traditionally used to treat DF, DHF, and DSS. In the present study, we systematically investigated seven compounds isolated from papaya leaf extract with regard to their potential as inhibitors for non-structural (NS) proteins, NS3 and NS5, which play a crucial role in viral RNA replication. The computational tools applied stretched across classical molecular docking, molecular dynamics (MD) simulations and SwissADME used to calculate binding affinities; binding free energies; Absorption, Distribution, Metabolism, and Excretion (ADME); and drug-likeness properties, thus, identifying Kaempferol, Chlorogenic acid, and Quercetin as potential candidates, with Kaempferol and Quercetin scoring best. Therefore, for the Kaempferol and Quercetin complexes, hybrid quantum mechanical/molecular mechanical (QM/MM) geometry and frequency calculations were performed, followed by the local mode analysis developed in our group to quantify Kaempferol-NS and Quercetin-NS hydrogen bonding. Given the non-toxic nature and the wide availability of the Kaempferol and Quercetin papaya extract in almost all of the susceptible regions, and our results showing high NS3 and NS5 binding affinities and energies, strong hydrogen bonding with both NS3 and NS5, and excellent ADME properties, we suggest Kaempferol and Quercetin as a strong NS3 and NS5 inhibitor to be further investigated in vitro.

In silico approach to probe the binding affinity between OMVs harboring the ZEGFR affibody and the EGF receptor

There is a growing interest in designing a nanocarrier containing an EGFR targeting affibody to direct toward cancer cells. Here, cytolysin A was cloned at the N-terminus of Z_EGFR:1907 affibody to guarantee its surface presentation on the OMVs while targeting the epidermal growth factor receptors (EGFRs). A separate construct including a fusogenic peptide (GALA) was also designed for the endosomal escape of the nanocarrier. Binding of the two constructs ClyA-affi_EGFR and ClyA-affi_EGFR-GALA to domain III of EGFR was investigated using molecular docking and molecular dynamic simulations. The higher stability of the ClyA-affi_EGFR-GALA/EGFR as compared to the ClyA-affi_EGFR/EGFR complex was evident. The ClyA-affi_EGFR-GALA structure showed a higher RMSD during the first half of the simulation time implying a much less stable behavior. Plateau state of the radius of gyration plot of ClyA-affi_EGFR-GALA confirmed a well-folded structure in the presence of the GALA sequence. Solvent accessible surface area for both proteins was in the same range. The data obtained from hydrogen bond analysis revealed a more equilibrated and stable form of the ClyA-affi_EGFR-GALA structure upon interaction with EGFR. The data provided here was a requisite for our biological evaluation of the synthesized constructs as a component of a novel drug delivery system.

Antioxidant activities and Allelopathic Potential of Chonemorpha splendens Chun et Tsiang Stem Methanol Extract

Finding agriculturally active compounds from nature or finding active lead compounds from natural products, artificial synthesis and structural modification are the main ways to create new agrochemical. In order to explore the agricultural activities of Chonemorpha splendens Chun et Tsiang (C. splendens), an important medicinal plant, the antioxidant activities and allelopathic potential were investigated. C. splendens was extracted with methanol, then, C. splendens methanol extract (CSME) were extracted with petroleum ether, chloroform, ethyl acetate and n-butanol. Reducing activity, lipid peroxidation, and the scavenging abilities for DPPH∙, O2-∙, HO∙, and H2O2 were also measured and allelopathic potentials were evaluated by bioassay method. GC-MS analysis revealed that esters were the main component (66.34%) of CSME, the total CSME flavonoid content was 313 mg g-1 (rutin equivalent). The chloroform phase of CSME was identified as stigmasterol by NMR for the first time. The DPPH• scavenging rate of CSME was 87%, with an IC50 value of 0.12 ± 0.02 mg mL-1, which was significantly difference from the positive control, trolox. Chloroform fraction showed the strongest inhibitory effect against Mimosa pudica (MP) seed germination at 1.0 mg mL-1 (100% inhibition), which was better than that of the chemical herbicide paraquat. In the seed growth experiment, systematic EC50 and the principal component analysis (PCA) were used to assess the allelopathic potential of extracts. The systematic EC50 values of Crotalaria pallida Ait. (CP), Bidens pilosa L. (BP) were significantly greater than MP. MP, Oryza sativa L. (OS) and Lactuca satiua L., (LS) inhibited all parameters. Our results would provide an idea for controlling weeds through allelopathy from C. splendens to reduce dependency on synthetic herbicides.

Computational insight of dexamethasone against potential targets of SARS-CoV-2

The health sector has been on the race to find a potent therapy for coronavirus disease (COVID)-19, a diseases caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2. Repurposed anti-viral drugs have played a huge role in combating the virus, and most recently, dexamethasone (Dex) have shown its therapeutic activity in severe cases of COVID-19 patients. The study sought to provide insights on the anti-COVID-19 mechanism of Dex at both atomic and molecular level against SARS-CoV-2 targets. Computational methods were employed to predict the binding affinity of Dex to SARS-CoV-2 using the Schrodinger suite (v2020-2). The target molecules and ligand (Dex) were retrieved from PDB and PubChem, respectively. The selected targets were SARS-CoV-2 main protease (Mpro), and host secreted molecules glucocorticoid receptor, and Interleukin-6 (IL-6). Critical analyses such as Protein and ligand preparation, molecular docking, molecular dynamic (MD) simulations, and absorption, distribution, metabolism, excretion (ADME), and toxicity analyses were performed using the targets and the ligand as inputs. Dex showed stronger affinity to its theoretical (glucocorticoid) receptor with a superior docking score of -14.7 and a good binding energy value of -147.48 kcal/mol; while short hydrogen bond distances were observed in both Mpro and IL-6 when compared to glucocorticoid receptor. Based on these findings, Dex-target complexes were used to perform MD simulations to analyze Dex stability at 50 ns. This study demonstrates that Dex could bind to both the viral and host receptors as a potential drug candidate for COVID-19. To ascertain the biological fitness of this study, other SARS-CoV-2 targets should be explored. Also, the in vitro studies of dexamethasone against several SARS-CoV-2 targets warrant further investigation.Communicated by Ramaswamy H. Sarma.

An In-Silico Approach to Evaluate the Inhibitory Potency of Selected Hydroxamic Acid Derivatives on Zinc-Dependent Histone Deacetylase Enzyme

Histone deacetylase (HDAC) enzymes modify the histone by removing the acetyl group from the lysine residues, known as histone deacetylation. HDACs have been involved in altering gene expressions, resulting in cancer cells in the body. This study focuses on HDAC inhibitors’ impact on histone deacetylase-like protein (HDLP) stability through computational techniques. Molecular dynamics (MD) analyses were used to examine the atomic-level description of drug binding sites and how the HDAC inhibitors change the HDLP enzyme environment. In this study, two hydroxamic acid-derived inhibitors, such as [Formula: see text]-Carboxycinnamic acid bis-hydroxamide (CBHA) and scriptaid (GCK1026), were selected to examine the inhibition ability in terms with suberanilohydroxamic acid (SAHA) as a reference drug. The crystal structure of the HDLP was downloaded from the Protein Data Bank. The structures of inhibitors were optimized using the G09W package. Docking studies were done by AutoDock-Vina, and the resultant complex was used to initiate MD studies. The trajectories obtained from MD simulation were used to perform the structural analysis. Root-mean-square deviation (RMSD), radius of gyration, hydrogen bond, binding free energy and interaction energy studies revealed that the stability of HDLP-SAHA and HDLP-CBHA is higher than the free HDLP enzyme. The HDLP-CBHA complex shows an increased number of hydrogen bonds (5), high MM-PBSA binding free energy ([Formula: see text][Formula: see text]kJ/mol), high interaction energy ([Formula: see text][Formula: see text]kJ/mol), and an increased number of alpha-helical amino acids (130) compared with HDLP-SAHA. It concluded that the CBHA has the relatively same potential as SAHA to inhibit the HDLP. Consequently, the use of CBHA in clinical application is recommended through this in-silico method.

Cracking a cancer code histone deacetylation in epigenetic: the implication from molecular dynamics simulations on efficacy assessment of histone deacetylase inhibitors

Epigenetic changes, histone acetylation and deacetylation in chromatin have been intensively studied due to their significance in regulating the gene expression. According to the type of tumor, the levels of histone deacetylases (HDAC) are varied. HDAC inhibitors are a new promising class of compounds that inhibit the proliferation of tumor cells. In this study, the inhibitory efficacy of some HDAC inhibitors such as vorinostat, panobinostat, abexinostat, belinostat, resminostat, dacinostat and pracinostat was studied using molecular dynamics simulation. The inhibitory efficacy was estimated by computing the enzyme's stability, positional stability of the individual amino acids and interaction energies of HDLP-inhibitor complexes. It is hoped that this investigation may improve our understanding of the atomic-level description of the inhibitor binding site and how the HDAC inhibitors change the environment of the enzyme's active site. The results obtained from the root-mean-square deviation, the radius of gyration, solvent-accessible surface area, root-mean-square fluctuation, stride server and Ramachandran plot have revealed that the stability of HDLP enzyme with vorinostat, panobinostat and abexinostat is higher than the other studied complexes. According to the calculated values for MM-PBSA, LIE, semi-LIE binding free energies and interaction energies, the stability of the HDLP enzyme varies as panobinostat > abexinostat > vorinostat where resminostat complex showed relatively low stability. The ligandability and drugability values also give the same trend as above. The findings revealed that the panobinostat and abexinostat are potential lead compounds as reference inhibitor vorinostat. Therefore, it is possible to use these drugs as HDAC inhibitors in clinical practices. Also, the outcomes of this study could be utilized to identify new inhibitors for clinical research.Communicated by Ramaswamy H. Sarma.