Comparative modelling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma

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:

DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors


Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR’s) present in the nasal cavity. Different odorants stimulate different OR’s due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier’s performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83% and 99.92 % for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at
Date of Publication: 12 June 2020 

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

Understanding Drug Resistance Mechanism of NS3/4A Protease of HCV Using Comparative Molecular Dynamics Simulation

Hepatitis C virus (HCV) infects approximately 325 million individuals globally causing hepatitis C, a fatal disease leading to liver cirrhosis. Imperative sincere efforts are needed to develop inhibitors targeting the essential NS3/4A protease. NS3/4A protease is an exceptionally significant target. Resistance against the most promising protease inhibitors, Telaprevir, Boceprevir and Faldaprevir has emerged in clinical trials. The emergence of resistance is attributed to the error-prone viral RNA-dependent RNA polymerase, thereby reducing the effectiveness of these inhibitors. Among the drug-resistant variants, single amino acid residues (V35M, Q80K, R155K, A156V, and D168A) are noteworthy for their presence in clinical isolates and also their efficacy against these inhibitors in clinical development. Thus, it is essential to unravel the mechanistic insights of these drug-resistant variants while designing potent novel inhibitors. In this current work, we have performed molecular docking and comparative MD simulation to analyze and unravel molecular mechanism of conformational fluctuations among inhibitor binding between wild type and its V35M, Q80K, R155K, A156V, and D168A variants. Protein-ligand contacts, Root mean square deviation (RMSD), Root mean square fluctuation(RMSF) and post-simulation plot analysis has been used to identify the stability and conformation of the key residues that regulate inhibitor binding and their impact in developing drug resistance. Unraveling and understanding of the binding mechanism of inhibitor within substrate would be a significant approach to design inhibitor that fits within the substrate with less susceptibility towards drug resistance as mutations upsetting inhibitor binding would concurrently impede the recognition of viral substrates.

Targeting INSM1 in Order to Prevent the Growth of Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a very aggressive form of cancer because of its poor survival and high rate of tumour progression. There are very few commercial drugs that can specifically target SCLC tumours and inhibit their progression. Thus, there is an urgent and unmet need to identify novel and effective drugs that can specifically target the SCLC tumour progression. In this study, we have selected Insulinoma associated protein 1 (INSM1), a transcription factor responsible for neuro-endocrinal differentiation. It has also been studied as a marker for neuro-endocrinal tumours like SCLC. It also plays a role in activation of several key pathways that are responsible for SCLC progression. N-myc which is an intermediate of the Sonic Hedgehog (Shh) pathway, helps in the overexpression of INSM1 which in turn activates PI3K/AKT and MEK/ERK1/2 pathways. The activation of these pathways results in further stabilization of N-myc and this cycle repeats. Thus, INSM1 acts as a key intermediate molecule between the Shh, PI3K/AKT and MEK/ERK1/2 pathways which have been reported to play an important role in tumour progression. It has already been reported that the knockdown of INSM1 can significantly reduce the SCLC tumour progression. Thus, we have screened the target INSM1 against our library of plant based anti-cancerous compounds to obtain a suitable drug. The top compounds having better docking score were studied for their ADME properties. The best compound obtained is then simulated for 50 ns in order to study the stability of the protein-ligand interaction. From this study, we try to identify novel plant-based therapeutics to inhibit SCLC tumour progression by inhibiting INSM1.

Understanding the Mechanism of Cell Death in Gemcitabine Resistant Pancreatic Ductal Adenocarcinoma: A Systems Biology Approach

Background: Gemcitabine is the standard chemotherapeutic drug administered in advanced Pancreatic Ductal Adenocarcinoma (PDAC). However, due to drug resistance in PDAC patients, this treatment has become less effective. Over the years, clinical trials for the quest of finding novel compounds that can be used in combination with gemcitabine have met very little success.

Objective: To predict the driving factors behind pancreatic ductal adenocarcinoma, and to understand the effect of these components in the progression of the disease and their contribution to cell growth and proliferation.

Methods: With the help of systems biology approaches and using gene expression data, which is generally found in abundance, dysregulated elements in key signalling pathways were predicted. Prominent dysregulated elements were integrated into a model to simulate and study the effect of gemcitabine- induced hypoxia.

Results: In this study, several transcription factors in the form of key drivers of cancer-related genes were predicted with the help of CARNIVAL, and the effect of gemcitabine-induced hypoxia on the apoptosis pathway was shown to have an effect on the downstream elements of two primary pathway models; EGF/VEGF and TNF signalling pathway.

Conclusion: It was observed that EGF/VEGF signalling pathway played a major role in inducing drug resistance through cell growth, proliferation, and avoiding cell death. Targeting the major upstream components of this pathway could potentially lead to successful treatment.

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

Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent

Tara Chand Yadav, Naresh Kumar, Utkarsh Raj, Nidhi Goel, Pritish Kumar Vardawaj, Ramasare Prasad & Vikas Pruthi (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


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)



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.