Toll-like receptor 7 agonists: chemical feature based pharmacophore identification and molecular docking studies

• Published in: PloS one Vol. 8; no. 3; p. e56514
• Main Authors: Yu, Hui, Jin, Hongwei, Sun, Lidan, Zhang, Liangren, Sun, Gang, Wang, Zhanli, Yu, Yongchun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.03.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino Acid Sequence; Analysis; Antiviral activity; Binding sites; Biological activity; Biology; Chemistry; Chinese medicine; Computer Simulation; Databases, Chemical; Drug Design; Drugs, Chinese Herbal - chemistry; Drugs, Chinese Herbal - pharmacology; Feature based; Hepatitis; Homology; Hospitals; Humans; Hydrogen; Hydrogen Bonding; Hydrogen bonds; Hydrogen ion concentration; Hydrophobic and Hydrophilic Interactions; Hydrophobicity; Hypotheses; Laboratories; Lead compounds; Ligands; Models, Molecular; Molecular docking; Molecular Docking Simulation; Molecular modelling; Molecular Sequence Data; Pharmacology; Physics; Proteins; Receptors; Software; Structural Homology, Protein; Structure-Activity Relationship; TLR7 protein; Toll-Like Receptor 7 - agonists; Toll-Like Receptor 7 - chemistry; Toll-Like Receptor 7 - genetics; Toll-like receptors; Traditional Chinese medicine; Viruses; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0056514

Chemical feature based pharmacophore models were generated for Toll-like receptors 7 (TLR7) agonists using HypoGen algorithm, which is implemented in the Discovery Studio software. Several methods tools used in validation of pharmacophore model were presented. The first hypothesis Hypo1 was considered to be the best pharmacophore model, which consists of four features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features. In addition, homology modeling and molecular docking studies were employed to probe the intermolecular interactions between TLR7 and its agonists. The results further confirmed the reliability of the pharmacophore model. The obtained pharmacophore model (Hypo1) was then employed as a query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit was identified as a potent TLR7 agonist, which has antiviral activity against hepatitis virus in vitro. Therefore, our current work provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 agonists with desired biological activity.