Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists
| Autor: | Xing Wang, Hongmin Zhang, Jie Xia, Wenjie Xue, Yuxi Wang, Zhufang Shen, Song Wu, Yi Huan, Zhenyi Wang, Zhenming Liu, Xiang Simon Wang, Liangren Zhang, Jui-Hua Hsieh |
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| Rok vydání: | 2020 |
| Předmět: |
General Chemical Engineering
Receptors Cytoplasmic and Nuclear Computational biology Library and Information Sciences Ligands 01 natural sciences Chemical library Machine Learning chemistry.chemical_compound Mice Docking (dog) 0103 physical sciences Animals Virtual screening 010304 chemical physics Drug discovery Obeticholic acid General Chemistry Ligand (biochemistry) 0104 chemical sciences Computer Science Applications Mice Inbred C57BL 010404 medicinal & biomolecular chemistry chemistry Liver Farnesoid X receptor Lead compound |
| Zdroj: | Journal of chemical information and modeling. 60(3) |
| ISSN: | 1549-960X |
| Popis: | Farnesoid X receptor (FXR) agonists can reverse dysregulated bile acid metabolism, and thus, they are potential therapeutics to prevent and treat nonalcoholic fatty liver disease. The low success rate of FXR agonists' R&D and the side effects of clinical candidates such as obeticholic acid make it urgent to discover new chemotypes. Unfortunately, structure-based virtual screening (SBVS) that can speed up drug discovery has rarely been reported with success for FXR, which was likely hindered by the failure in addressing protein flexibility. To address this issue, we devised human FXR (hFXR)-specific ensemble learning models based on pose filters from 24 agonist-bound hFXR crystal structures and coupled them to traditional SBVS approaches of the FRED docking plus Chemgauss4 scoring function. It turned out that the hFXR-specific pose filter ensemble (PFE) was able to improve ligand enrichment significantly, which rendered 3RUT-based SBVS with its PFE the ideal approach for FXR agonist discovery. By screening of the Specs chemical library and in vitro FXR transactivation bioassay, we identified a new class of FXR agonists with compound XJ034 as the representative, which would have been missed if the PFE was not coupled. Following that, we performed in-depth biological studies which demonstrated that XJ034 resulted in a downtrend of intracellular triglyceride in vitro, significantly decreased the serum/liver TG in high fat diet-induced C57BL/6J obese mice, and more importantly, showed metabolic stabilities in both plasma and liver microsomes. To provide insight into further structure-based lead optimization, we solved the crystal structure of hFXR complexed with compound XJ034, uncovering a unique hydrogen bond between compound XJ034 and residue Y375. The current work highlights the power of our pose filter-based ensemble learning approach in terms of scaffold hopping and provides a promising lead compound for further development. |
| Databáze: | OpenAIRE |
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