Next Article in Journal
Controlled Synthesis of Atomically Layered Hexagonal Boron Nitride via Chemical Vapor Deposition
Next Article in Special Issue
Intrinsic Dynamics Analysis of a DNA Octahedron by Elastic Network Model
Previous Article in Journal
Water Will Be the Coal of the Future—The Untamed Dream of Jules Verne for a Solar Fuel
Open AccessArticle

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking

1
College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China
2
College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China
3
College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Roberta Galeazzi
Molecules 2016, 21(12), 1639; https://doi.org/10.3390/molecules21121639
Received: 31 October 2016 / Revised: 21 November 2016 / Accepted: 26 November 2016 / Published: 29 November 2016
(This article belongs to the Special Issue Biomolecular Simulations)
In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators. View Full-Text
Keywords: ABCA1; QSAR; molecular modeling; similarity analysis; molecular docking; LXRβ ABCA1; QSAR; molecular modeling; similarity analysis; molecular docking; LXRβ
Show Figures

Graphical abstract

MDPI and ACS Style

Chen, M.; Yang, F.; Kang, J.; Yang, X.; Lai, X.; Gao, Y. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking. Molecules 2016, 21, 1639.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop