Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modelled, and the response of key amino acids to perturbation were identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches. DOI: https://doi.org/10.1080/07391102.2020.1742793
Category: Structural Biology
DeEPn: A deep neural network based tool for enzyme functional annotation
Authors: Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Pritish Kumar Varadwaj
With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.
Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach
Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.
Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks.
Methodology: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.
Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network.
Conclusion: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.
Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent
(2019)Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent, Journal of Biomolecular Structure and Dynamics,
DOI: 10.1080/07391102.2019.1575283
Abstract: Drug discovery for a vigorous and feasible lead candidate is a challenging scientific mission as it requires expertise, experience, and huge investment. Natural products and their derivatives having structural diversity are renowned source of therapeutic agents since many years. Tyrosol (a natural phenylethanoid) has been extracted from olive oil, and its structure was confirmed by elemental analysis, FT-IR, FT-NMR, and single crystal X-ray crystallography. The conformational analysis for tyrosol geometry was performed by Gaussian 09 in terms of density functional theory. Validation of bond lengths and bond angles obtained experimentally as well as theoretically were performed with the help of curve fitting analysis, and values of correlation coefficient (R) obtained as 0.988 and 0.984, respectively. The charge transfer within the tyrosol molecule was confirmed by analysis of HOMO→LUMO molecular orbitals. In molecular docking with COX-2 (PDB ID: 5F1A), tyrosol was found to possess satisfactory binding affinity as compared to other NSAIDs (Aspirin, Ibuprofen, and Naproxen) and a COX-2 selective drug (Celecoxib). ADMET prediction, drug-likeness and bioactivity score altogether confirm the lead/drug like potential of tyrosol. Further investigation of simulation quality plot, RMSD and RMSF plots, ligands behavior plot as well as post simulation analysis manifest the consistency of 5F1A-tyrosol complex throughout the 20 ns molecular simulation process that signifies its compactness and stability within the receptor pocket.
PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site
PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site
Semwal R., Aier I., Varadwaj P.K., Antsiperov S. (2019) PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site. In: Rojas I., Valenzuela O., Rojas F., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science, vol 11465. Springer, Cham
DOI: https://doi.org/10.1007/978-3-030-17938-0_40
Abstract: To carry out functional annotation of proteins, the most crucial step is to identify the ligand binding site (LBS) information. Although several algorithms have been reported to identify the LBS, most have limited accuracy and efficiency while considering the number and type of geometrical and physio-chemical features used for such predictions. In this proposed work, a fast and accurate algorithm “PROcket” has been implemented and discussed. The algorithm uses grid-based approach to cluster the local residue neighbors that are present on the solvent accessible surface of proteins. Further with inclusion of selected physio-chemical properties and phylogenetically conserved residues, the algorithm enables accurate detection of the LBS. A comparative study with well-known tools; LIGSITE, LIGSITECS, PASS and CASTptool was performed to analyze the performance of our tool. A set of 48 ligand-bound protein structures from different families were used to compare the performance of the tools. The PROcket algorithm outperformed the existing methods in terms of quality and processing speed with 91% accuracy while considering top 3 rank pockets and 98% accuracy considering top 5 rank pockets.
Computational and In-Vitro Validation of Natural Molecules as Potential Acetylcholinesterase Inhibitors and Neuroprotective Agents
Computational and In-Vitro Validation of Natural Molecules as Potential Acetylcholinesterase Inhibitors and Neuroprotective Agents
Current Alzheimer Research, Volume 16, Number 2, 2019, pp. 116-127(12)
DOI: https://doi.org/10.2174/1567205016666181212155147
Abstract:
Background: Cholinesterase inhibitors are the first line of therapy for the management of Alzheimer’s disease (AD), however, it is now established that they provide only temporary and symptomatic relief, besides, having several inherited side-effects. Therefore, an alternative drug discovery method is used to identify new and safer ‘disease-modifying drugs’.
Methods: Herein, we screened 646 small molecules of natural origin having reported pharmacological and functional values through in-silico docking studies to predict safer neuromodulatory molecules with potential to modulate acetylcholine metabolism. Further, the potential of the predicted molecules to inhibit acetylcholinesterase (AChE) activity and their ability to protect neurons from degeneration was determined through in-vitro assays.
Results: Based on in-silico AChE interaction studies, we predicted quercetin, caffeine, ascorbic acid and gallic acid to be potential AChE inhibitors. We confirmed the AChE inhibitory potential of these molecules through in-vitro AChE inhibition assay and compared results with donepezil and begacestat. Herbal molecules significantly inhibited enzyme activity and inhibition for quercetin and caffeine did not show any significant difference from donepezil. Further, the tested molecules did not show any neurotoxicity against primary (E18) hippocampal neurons. We observed that quercetin and caffeine significantly improved neuronal survival and efficiently protected hippocampal neurons from HgCl2 induced neurodegeneration, which other molecules, including donepezil and begacestat, failed to do.
Conclusion: Quercetin and caffeine have the potential as “disease-modifying drugs” and may find application in the management of neurological disorders such as AD.
Structure-based drug designing and identification of Woodfordia fruticosa inhibitors targeted against heat shock protein (HSP70-1) as suppressor for Imiquimod-induced psoriasis like skin inflammation in mice model
Structure-based drug designing and identification of Woodfordia fruticosa inhibitors targeted against heat shock protein (HSP70-1) as suppressor for Imiquimod-induced psoriasis like skin inflammation in mice model
Materials Science and Engineering: CVolume 95, 1 February 2019, Pages 57-71
DOI: https://doi.org/10.1016/j.msec.2018.10.061
Abstract: Heat shock proteins (HSPs) emerged as a therapeutic target and it was observed that inhibition of HSP70-1 plays a pivotal role in the management of psoriasis. In-silico investigation involving techniques like molecular docking and molecular dynamics (MD) simulation analysis was performed against HSP70-1. Further, anti-psoriatic activity of bioactive immunomodulatory compounds present in ethanolic extract of Woodfordia fruticosa flowers (Wffe) using combination of bioinformatics together with ethnopharmacological approach has been explored in this study. Myricetin (−8.024), Quercetin (−7.368) and Ellagic acid (−7.311) were the top three compounds with minimum energy levels as well as high therapeutic value/ADMET as compared to currently available marketed anti-psoriatic drug Tretinoin (−7.195). ADMET prediction was used to screen ligands for drug-likeness and efficacy. Further, biogenically Woodfordia fruticosa gold nanoparticles (WfAuNPs) were synthesized and characterized by UV–Visible Spectroscopy (UV–vis), Dynamic Light Scattering (DLS), Zeta Potential, X-Ray Diffraction (XRD) and High Resolution Transmission Electron Microscopy (HRTEM) techniques. Synthesized WfAuNPs observed in the size range of 10–20 nm and were used to develop WfAuNPs-Carbopol®934 ointment gel. Subsequently, the therapeutic efficacy of WfAuNPs-Carbopol® 934 was checked against 5% Imiquimod-induced psoriasis like skin inflammation. WfAuNPs-Carbopol® 934 was found to be exerting better therapeutic effect in reducing the mean DAI score (0.63 ± 0.08), serum cytokines (TNF-α, IL-22 and IL-23) levels along with reduced epidermal thickness, parakeratosis and marked decrease in the hyperproliferation of keratinocytes. Results of the study revealed that the WfAuNPs-Carbopol® 934 could be an effective alternative treatment for psoriasis in near future.
Unveiling the transcriptome complexity of the High- and Low- Zinc & Iron accumulating Indian wheat (Triticum aestivum L.) cultivars
Unveiling the transcriptome complexity of the High- and Low- Zinc & Iron accumulating Indian wheat (Triticum aestivum L.) cultivars
A systematic assessment of statistics, risk factors, and underlying features involved in pancreatic cancer
A systematic assessment of statistics, risk factors, and underlying features involved in pancreatic cancer,
Cancer Epidemiology 58:104-110, February 2019
DOI: 10.1016/j.canep.2018.12.001.
Abstract
Pancreatic cancer remains the fourth leading cause of cancer-related death in the world, and will continue to become the number two cause of cancer-related death unless a remarkable breakthrough is achieved. With a slim chance of early diagnosis, surgery can only provide a median survival of 17-23 months. The presence of a dense stroma makes this cancer resilient to chemotherapy, with very few potent inhibitors like nab paclitaxelin available that can work in combination with chemotherapeutic agents. Survival rates, on the one hand, lie at 8.5%. Variation in types of pancreatic cancer, on the other hand, makes it notoriously difficult to come up with a practical solution for the treatment of this disease. A deeper understanding of the root cause would be beneficial for diagnosis. Advancement in the field of genomics has made the identification of novel biomarkers relatively easier. By coupling this factor with the production of suitable inhibitors, testing in large numbers can be made possible with the help of cell lines. With the combined efforts of biological knowledge and modern technology, the cure for pancreatic cancer could be at hand.
https://www.ncbi.nlm.nih.gov/pubmed/30537645