In silico identification of novel IL-1β inhibitors to target protein–protein interfaces. (October 2015)

• Record Type: Journal Article
• Authors: Halim, Sobia Ahsan; Jawad, Muhammad; Ilyas, Muhammad; Mir, Zulfiqar; Mirza, Atif Anwar; Husnain, Tayyab
• Is Part Of: Computational biology and chemistry. Volume 58(2015)
• Page Start: 158
• Page End: 166
• Publication Date: 2015-10
• Subjects: CADD computer aided drug design -- FRED fast rigid exhaustive docking -- IL-1β interleukin-1 beta -- IL-1R interleukin-1 receptor -- MOE molecular operating environment -- PDB protein data bank -- PLIF protein ligand interaction fingerprints -- QSAR quantitative structure activity relationship -- VS virtual screening -- ZINC zinc is not commercial; IL-1β -- Virtual screening -- QSAR -- Pharmacophore modeling -- Molecular docking; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.06.004
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The study encompasses QSAR modeling, pharmacophore modeling and docking simulation. QSAR and pharmacophore modeling was performed for IL-1β inhibitors. The activity of 7 million compounds from ZINC database was predicted by QSAR model. The best predicted compounds were subjected to molecular docking by MOE and FRED. Docking results showed 7 compounds as potential IL-1β inhibitors. Abstract: Interleukin-1β is a drug target in rheumatoid arthritis and several auto-immune disorders. In this study, a set of 48 compounds with the determined IC50 values were used for QSAR analysis by MOE. The QSAR model was developed by using training set of 41 compounds, based on 12 unique descriptors. Model was validated by predicting the IC50 values for a test set of 7 compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 values with predicted ones. Subsequently, model was used for the screening of a large data set of 7, 397, 957 compounds obtained from "Drugs Now" category of ZINC database. The activities of those compounds were predicted by developed model. 708, 960 compounds that showed best predicted activities were chosen for further studies. Additionally this set of 708, 960 compounds was screened by pharmacophore modeling that led to the retrieval of 1809 molecules. Finally docking of 1809 molecules was conducted at the IL-1β receptor binding site using MOE and FRED docking program. Several new compounds were predicted as IL-1β inhibitors in silico . This study provides valuable insight for designing more potent and selective inhibitors for the treatment of inflammatory diseases.