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The Role of Natural Products in COVID-19
Published in Hanadi Talal Ahmedah, Muhammad Riaz, Sagheer Ahmed, Marius Alexandru Moga, The Covid-19 Pandemic, 2023
Iqra Akhtar, Sumera Javad, Tehreema Iftikhar, Amina Tariq, Hammad Majeed, Asma Ahmad, Muhammad Arfan, M. Zia-Ul-Haq
Molecular docking is a common and modern technique which is used now a day to discover new drug formulas. A number of software are in use for this purpose. This procedure can analyze small molecules for their interaction with the binding sites of target enzymes or proteins. The capacity of a molecule to bind with minimum energy is searched out. This binding of natural products and target proteins depend upon the shape of the molecule and electrostatic forces between them. Its’ such a successful method that it can be used for such target proteins whose structure is still undiscovered but can be done with some homologous structures as in the case of various proteins involved in SARS CoV and COVID-19 infections [61].
Integrating CADD and Herbal Informatics Approach to Explore Potential Drug Candidates Against HPV E6 Associated With Cervical Cancer
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Arushi Verma, Jyoti Bala, Navkiran Kaur, Anupama Avasthi
Results of docking analysis (highest scores) done using PatchDock tool is available in Table 8.2. Visualization and bond analysis were done using Chimera. For better visualization, they were examined in 3D (ribbon) and 2D atom formats, as shown in Figures 8.8A and 8.9A. Molecular docking analysis was done using the Protein Ligand Interaction Profiler (Figures 8.8B and 8.9B). The docking analysis results indicated hydrogen bond and hydrophobic interactions in MD between E6 and luteolin; and hydrogen bonds, hydrophobic interactions, and pie-stacking (perpendicular) interactions in MD between daphnoretin and E6. This analysis strengthens our resolve to continue with these ligands for further studies into a pipeline of ADME analysis.
Monoterpenes Modulating IL-10
Published in Parimelazhagan Thangaraj, Phytomedicine, 2020
Saravanan Shanmugam, Jullyana S. S. Quintans, Parimelazhagan Thangaraj, Luciana Scotti, Marcus T. Scotti, Adriano A. S. Araújo, Lucindo J. Quintans-Júnior
Molecular docking is one of the major bioinformatic tools used in the drug-discovery process. This is one of the virtual screening methods that mainly focuses on the structures of targets and ligands. It predicts the binding geometries as well as the binding energy of the ligand-target complex. The mechanism of action and molecular target of the IL-10 were studied in this present review. Molecular docking analysis can be conducted to study the interaction of monoterpene compounds with molecular targets of anti-inflammatory activity, especially IL-10. Further, this structure-activity relationship can be used to develop new derivative natural compounds with higher anti-inflammatory activity.
Radio-protective efficacy of Gymnema sylvestre on Pangasius sutchi against gamma (60Co) irradiation
Published in International Journal of Radiation Biology, 2022
Pamela Sinha, Kantha Devi Arunachalam, Santhosh Kumar Nagarajan, Thirumurthy Madhavan, Arumugam R. Jayakumar, Mohamed Saiyad Musthafa
Molecular docking is one of the most frequently used methods in structure-based drug design, due to its ability to predict the binding-conformation of small molecule ligands to the appropriate target binding site. Characterization of the binding behavior plays an important role in rational design of drugs, as well as to elucidate fundamental biochemical processes; and predicting and prioritizing large library of molecules for a particular action is highly desirable (Chong et al. 2006; Kim et al. 2009). Elucidation of structural similarity of drugs and their known side-effects are useful in the establishment and analysis of networks responsible for poly-pharmacology (Johnson and Maggiora 1990; Campillos et al. 2008). The importance of ‘Amifostine’ and G. sylvestre components and its possible implications in radiation protection has made us to carry out a systematic effort to find whether the known radio-protectants carry this function by following in silico approaches.
Artificial intelligence, machine learning, and drug repurposing in cancer
Published in Expert Opinion on Drug Discovery, 2021
Ziaurrehman Tanoli, Markus Vähä-Koskela, Tero Aittokallio
Molecular docking is a widely used in-silico method in structure-based drug design, due to its ability to predict the binding-conformation of small molecule ligands to the appropriate target-binding site [95–97]. The drawback of molecular docking is that the 3D structures of many target proteins have not yet been resolved, which is required for running the docking simulations. Furthermore, the accuracy of docking-based methods decreases in cases where the number of known ligands for a protein is not sufficient [98]. Regardless of these limitations, there are several examples of successful docking-based drug off-target activity predictions [99]. For instance, antipsychotic agent thioridazine was found among 1500 FDA-approved compounds to possess anti-inflammatory activity by binding and inhibiting IκB kinase, which is critical for the NF-ΚB pathway [100]. Similarly, virtual docking accurately predicted inhibitory activity of five compounds from a collection of more than 1400 FDA-approved drugs against Pseudomonas aeruginosa quorum-sensing (population-wide virulence) mechanisms, with antipsychotic agent pimozide displaying potent in vitro activity in inhibiting bacterial virulence gene expression [101]. Moreover, AI is also emerging as an increasingly accurate approach for predicting the 3D structures of proteins from their amino-acid sequences [102,103].
Lead optimization of 4-(thio)-chromenone 6-O-sulfamate analogs using QSAR, molecular docking and DFT – a combined approach as steroidal sulfatase inhibitors
Published in Journal of Receptors and Signal Transduction, 2021
Molecular docking was carried out using Autodock 4.2.3 package. The docking calculations were performed with 60*60*60 points as grid map spacing and centering on the c-β atom with x = 71.01, y= −1.51, z = 28.27 dimensions. 100 independent docking runs were performed with 500,000 energy evaluations for each run. The other docking parameters were set to default and docking calculations were calculated based on energy-scoring functions. The output of all the derivatives were clustered based on the binding energy values and the best pose of docked ligands with the lowest energy conformations were saved. For all the docking analyses, only the best scoring poses were taken into account. Further to validate the docking methodology, Irosustat was used as reference ligand.