Molecular docking and dynamics approach to in silico drug repurposing for inflammatory bowels disease by targeting TNF alpha

Novel peptide inhibitor of human tumor necrosis factor-α has antiarthritic activity

The inhibition of tumor necrosis factor (TNF)-α trimer formation renders it inactive for binding to its receptors, thus mitigating the vicious cycle of inflammation. We designed a peptide (PIYLGGVFQ) that simulates a sequence strand of human TNFα monomer using a series of in silico methods, such as active site finding (Acsite), protein-protein interaction (PPI), docking studies (GOLD and Flex-X) followed by molecular dynamics (MD) simulation studies. The MD studies confirmed the intermolecular interaction of the peptide with the TNFα. Fluorescence-activated cell sorting and fluorescence microscopy revealed that the peptide effectively inhibited the binding of TNF to the cell surface receptors. The cell culture assays showed that the peptide significantly inhibited the TNFα-mediated cell death. In addition, the nuclear translocation of the nuclear factor kappa B (NFκB) was significantly suppressed in the peptide-treated A549 cells, as observed in immunofluorescence and gel mobility-shift assays. Furthermore, the peptide protected against joint damage in the collagen-induced arthritis (CIA) mouse model, as revealed in the micro focal-CT scans. In conclusion, this TNFα antagonist would be helpful for the prevention and repair of inflammatory bone destruction and subsequent loss in the mouse model of CIA as well as human rheumatoid arthritis (RA) patients. This calls upon further clinical investigation to utilize its potential effect as an antiarthritic drug.

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches

Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Preclinical Targeting of the PGRMC1-CK2 Axis with Silmitasertib: A Potential Strategy for Lung Adenocarcinoma Therapy

Progesterone receptor membrane component 1 (PGRMC1) is a pleiotropic protein over-expressed in lung adenocarcinoma (LUAD). The precise molecular mechanisms underlying the signature motif of Casein kinase (CK2) presence in PGRMC1 and their role in LUAD remain unclear. X-ray crystallographic structure for CK2 and PGRMC1 from the PubChem database was obtained and subjected to protein-protein interaction (PPI) analysis to identify their interactions. In addition, the CK2 inhibitor - Silmitasertib was also utilised to understand the interaction between PGRMC1-CK2. The PPI complex (PGRMC1-CK2) and the PPI-ligand interaction analysis and their Molecular Dynamics (MD) studies revealed the stability of their interactions and critical amino acid contacts within the 5Ǻ vicinity of the CK2 signature motif "T/S-x-x-E/D". Moreover, in-vitro colony formation assay, migration assay, and gene expression analysis using quantitative Real-time PCR revealed that Silmitasertib (IC50-2.5 μM) was highly influential in suppressing the PGRMC1-CK2 expression axis. In conclusion, our study infers that PGRMC1-CK-2 axis inhibition could be a potential therapeutic option to limit the promotion and progression of lung cancer.

Computational-guided approach for identification of PI3K alpha inhibitor in the treatment of hepatocellular carcinoma by virtual screening and water map analysis

Hepatocellular carcinoma (HCC) is one of the most deadly disorders, with a relative survival rate of 36% in the last 5 years. After an extensive literature survey and pathophysiology analysis, PI3Kα was found to be a promising biological target as PIK3CA gene upregulation was observed in HCC, resulting in the loss of apoptosis of cells, which leads to uncontrollable growth and proliferation. Due to superior selectivity and promising therapeutic activity, the PI3K-targeted molecule library was selected, and the ligand preparation was executed. The study mainly focused on e-pharmacophore development, virtual screening and receptor-ligand docking analysis. Then, MMGBSA and ADME prediction analysis was performed with the top 10 molecules; for further analysis of ligand-receptor binding affinity at the catalytic binding site, induced fit docking was performed with the top two molecules. The analysis of quantum chemical stability descriptors, i.e., frontier molecular orbital analysis, was performed followed by molecular dynamics simulation of 100 ns to better understand the ligand-receptor binding. In this study, water map analysis played a significant role in the hit optimization and analysis of the thermodynamic properties of the receptor-ligand complex. The two hit molecules K894-1435 and K894-1045 represented superior docking scores, enhanced stability, and inhibitory action targeting Valine 851 amino acid residue at the catalytic binding site. Hence, the study has significance for the quest for selective PI3Kα inhibitors through the process of hit-to-lead optimization.Communicated by Ramaswamy H. Sarma.

Novel quinazoline-chromene hybrids as anticancer agents: Synthesis, biological activity, molecular docking, dynamics and ADME studies

In this study, new quinazoline-chromene hybrid compounds were synthesized. The cytotoxic effects on cell viability of the hybrid compounds were tested against A549 human lung adenocarcinoma and BEAS-2B healthy bronchial epithelial cell lines in vitro. In addition, the ability of the active compounds to inhibit cell migration was tested. Molecular docking studies were performed to evaluate the ligand-protein interactions, and molecular dynamics simulations were performed to determine the interactions and stability of ligand-protein complexes. In silico absorption, distribution, metabolism, and excretion (ADME) studies were conducted to estimate the drug-likeness of the compounds. Compounds 4 (IC50  = 51.2 µM) and 5 (IC50  = 44.2 µM) were found to be the most active agents against A549 cells. They are found to be more selective against A549 cells than the reference drug doxorubicin. They also have the ability to significantly inhibit cell migration. They have the best docking scores against epidermal growth factor receptor (EGFR) (-11.300 and -11.226 kcal/mol) and vascular endothelial growth factor receptor 2 (VEGFR2) (-10.987 and -11.247 kcal/mol), respectively. In MD simulations, compounds 4 and 5 have strong hydrogen bond interactions above 80% of simulation times and showed a low ligand root mean square deviation (RMSD) around 2 Å. According to the ADME analysis, compounds 4 and 5 exhibit excellent drug-likeness and pharmacokinetic characteristics.

Molecular docking and dynamics guided approach to identify potential anti-inflammatory molecules as NRF2 activator to protect against drug-induced liver injury (DILI): a computational study

Inflammation and oxidative stress can contribute to the etiology of metabolic and chronic illnesses. The ability to prevent oxidative stress induced diseases such as cancer, cardiovascular disease, Alzheimer's disease, and others has been the subject of global research. Drug-induced liver injury (DILI) pathogenesis can be either due to oxidative stress or inflammatory response elicited by the drug, its metabolite, or herbal supplements. Our present research uses computational studies to identify a molecule with anti-inflammatory properties that can operate as an NRF2 activator. Acquiring and preparing the KEAP1-NRF2 Protein (PDB: 4L7D) with Schrodinger Suite was followed by developing a ligand library (Anti-inflammatory library downloaded from ChemDiv database). Molecular docking studies were performed in HTVS, SP, and XP modes, respectively. Based on the docking score, interaction, ADMET and binding free energy, the top ten compounds were selected and subjected to induced-fit docking (IFD) analysis for further study. The top three molecules were chosen for a molecular dynamics (MD) simulation study. Using the Desmond module of the Schrodinger Suite, the stability of the protein-ligand complex and protein-ligand contact throughout 100ns were evaluated during the MD simulation study. In our study, it was observed that three compounds exhibit exceptional stability and retain the essential interaction throughout the studies, and it is anticipated that these compounds may act as effective NRF2 activators. Further in vitro and in vivo assessments can be conducted to determine its potential to prevent DILI via acting as an NRF2 activator for future drug development.

Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100 ns molecular dynamics (MD) simulation. The compounds-S721-1955, CM4579-5085, S721-1963, and S721-1999-exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

In-silico validation of novel therapeutic activities of withaferin a using molecular docking and dynamics studies

Withaferin A is a bioactive molecule of W. somnifera. We access its efficacy against various target proteins associated with Cancer, Type-II Diabetes and hypercholesterolemia using molecular docking. Although it's efficacy against some of these targets have been reported earlier, we validate each mechanism in order to report the most appropriate mechanism of action. We explain the anti-cancer activity of Withaferin A by inhibition of Mortalin (mtHsp70) and Nrf2 protein with binding energies -8.85 kcal/mol and -12.59 kcal/mol respectively. Similarly, the anti-diabetic activity could be explained by inhibition of alpha and betα-glucosidase with binding energies -6.44 and -4.43 kcal/mol respectively and the cholesterol reduction could be explained by its ability to inhibition of NPC1 and SRB1 with binding energies -5.73 and -7.16 kcal/mol respectively. The molecular dynamics of the apoprotein and the protein-ligand complex simulated for the best targets of each activity namely Nrf2 protein for anti-cancer, α-glucosidase for anti-diabetic and SR-B1 for anti-hypercholesterolemia activity indicated the formation of stable complexes due to low RMSD deviations, low RMSF fluctuations and low RG values after the docking simulation. Finally, an ADME + T (Adsorption, distribution, metabolism, excretion and toxicity) prediction on Withaferin A showed that it obeyed all the Lipinsky's rules and qualified the drug-like criteria. All these results validate that Withaferin A possess potential anti-cancer, anti-diabetic and cholesterol reducing properties. This is the first report that indicates the possibility of Withaferin A binding and inhibiting SR-B1 as a mechanism of its anti-hypercholesterolemia activity.