title: Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.
abstract: 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.
authors.affiliation: College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China. chenmeimei1984@163.com.|College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China. chenmeimei1984@163.com.
authors.forename: Meimei
authors.initials: M
authors.lastname: Chen
authors.affiliation: College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China. yangfafu@fjnu.edu.cn.
authors.forename: Fafu
authors.initials: F
authors.lastname: Yang
authors.affiliation: College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China. meinuohua2007@163.com.
authors.forename: Jie
authors.initials: J
authors.lastname: Kang
authors.affiliation: College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China. mei_tcm@163.com.
authors.forename: Xuemei
authors.initials: X
authors.lastname: Yang
authors.affiliation: College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China. keerer1990@163.com.
authors.forename: Xinmei
authors.initials: X
authors.lastname: Lai
authors.affiliation: College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China. gaoxingchem@xmu.edu.cn.
authors.forename: Yuxing
authors.initials: Y
authors.lastname: Gao
mesh_terms: D064286:ATP Binding Cassette Transporter 1 / Q000378:metabolism*; D050197:Atherosclerosis / Q000473:pathology; D001665:Binding Sites; D008076:Cholesterol, HDL / Q000097:blood; D003198:Computer Simulation; D062126:Databases, Chemical; D015195:Drug Design; D005419:Flavonoids / Q000737:chemistry*; D006801:Humans; D000071518:Liver X Receptors / Q000378:metabolism*; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: C577836:ABCA1 protein, human; D064286:ATP Binding Cassette Transporter 1; D008076:Cholesterol, HDL; D005419:Flavonoids; D000071518:Liver X Receptors
keywords: ABCA1; LXRβ; QSAR; molecular docking; molecular modeling; similarity analysis