In silico drug design of inhibitor of nuclear factor kappa B kinase subunit beta inhibitors from 2-acylamino-3-aminothienopyridines based on quantitative structure–activity relationships and molecular docking. (February 2019)

• Record Type: Journal Article
• Authors: Wang, Jiao-Long; Li, Liang; Hu, Mei-Bian; Wu, Bo; Fan, Wen-Xiang; Peng, Wei; Wei, Da-Neng; Wu, Chun-Jie
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 297
• Page End: 305
• Publication Date: 2019-02
• Subjects: IKK-β -- QSAR -- Docking -- Fingerprint -- Clustering; 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.2018.12.021
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Firstly conducted an in silico screening study for potential IKK-β inhibitors based on aminothienopyridines template. An in-house library which has more than one hundred molecules was designed based on QSAR study results. Combined use of 2D, 3D QSAR and molecular docking method for molecular screening. Two potentially active compounds were designed, predicted and conjoined verified. Abstract: Inhibitor of nuclear factor kappa B kinase subunit beta (IKK-β), a specific regulator of nuclear factor-κB (NF-κB), is considered a valid target to design novel candidate drugs to treat rheumatoid arthritis and various cancers. In the present study, quantitative structure–activity relationships (QSAR) and molecular docking techniques were used to screen for new IKK-β inhibitors from a series of 2-acylamino-3-aminothienopyridine analogs. During the two-dimensional QSAR phase, the statistical model partial least square was selected from among two alternatives (r 2 = 0.868, q 2 (cross-validation) = 0.630). Descriptors with positive or negative contributions were derived from the created model. To build of three-dimensional QSAR models, we used three different fingerprints as analysis precepts for molecular clustering and the subsequent division of training sets and test sets. The best model, which used fingerprint model definition language public keys, was selected for further prediction of the compounds' activities. Favorable physicochemical, structural, electrostatic, and steric properties were derived from the created QSAR models and then used for drug design with an in-house library. Amongst the designed compounds, compounds B01 and B02 showed good predicted activities. Furthermore, after a selecting the protein structure and docking method, docking studies were carried out to reveal the detailed interactions between the ligands and the target protein. Binding affinity was measured and sorted using the value of "-CDOCKER_ENERGY". The high -CDOCKER_ENERGY values of compounds B01 (41.6134 kcal/mol) and B02 (40.1366 kcal/mol) indicated their prominent docking affinities.