题名 | Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists |
作者 | |
通讯作者 | Wu,Song; Shen,Zhufang; Zhang,Hongmin; Wang,Xiang Simon |
共同第一作者 | Xia,Jie; Wang,Zhenyi; Huan,Yi |
发表日期 | 2020
|
DOI | |
发表期刊 | |
ISSN | 1549-9596
|
EISSN | 1520-5142
|
卷号 | 60期号:3页码:1202-1214 |
摘要 | 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. |
相关链接 | [Scopus记录] |
收录类别 | |
语种 | 英语
|
学校署名 | 通讯
|
资助项目 | Shenzhen Science and Technology Innovation Committee[JCYJ20160608140912962]
; Shenzhen Science and Technology Innovation Committee[ZDSYS20140509142721429]
|
WOS研究方向 | Pharmacology & Pharmacy
; Chemistry
; Computer Science
|
WOS类目 | Chemistry, Medicinal
; Chemistry, Multidisciplinary
; Computer Science, Information Systems
; Computer Science, Interdisciplinary Applications
|
WOS记录号 | WOS:000526390800016
|
出版者 | |
EI入藏号 | 20201008262773
|
EI主题词 | Diagnosis
; Mammals
; Lead compounds
; Diseases
; Crystal structure
; Hydrogen bonds
|
EI分类号 | Medicine and Pharmacology:461.6
; Physical Chemistry:801.4
; Crystal Lattice:933.1.1
|
Scopus记录号 | 2-s2.0-85080871895
|
来源库 | Scopus
|
引用统计 |
被引频次[WOS]:7
|
成果类型 | 期刊论文 |
条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/106446 |
专题 | 生命科学学院_生物系 生命科学学院 南方科技大学医学院 |
作者单位 | 1.State Key Laboratory of Bioactive Substance and Function of Natural Medicines,Department of New Drug Research and Development,Institute of Materia Medica,Chinese Academy of Medical Sciences,Peking Union Medical College,Beijing,100050,China 2.Hefei National Laboratory for Physical Sciences at the Microscale,School of Life Sciences,University of Science and Technology of China,Hefei, Anhui,230026,China 3.Department of Biology,Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research,Shenzhen Key Laboratory of Cell Microenvironment,SUSTech-HKU Joint Laboratories for Matrix Biology,Southern University of Science and Technology,Shenzhen,518055,China 4.State Key Laboratory of Bioactive Substance and Function of Natural Medicines,Department of Pharmacology,Institute of Materia Medica,Chinese Academy of Medical Sciences,Peking Union Medical College,Beijing,100050,China 5.State Key Laboratory of Natural and Biomimetic Drugs,School of Pharmaceutical Sciences,Peking University,Beijing,100191,China 6.Kelly Government Solutions,Research Triangle Park,27709,United States 7.Artificial Intelligence and Drug Discovery Core Laboratory,District of Columbia Center for AIDS Research (DC CFAR),Department of Pharmaceutical Sciences,College of Pharmacy,Howard University,Washington,20059,United States |
通讯作者单位 | 生物系; 南方科技大学医学院; 生命科学学院 |
推荐引用方式 GB/T 7714 |
Xia,Jie,Wang,Zhenyi,Huan,Yi,et al. Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists[J]. Journal of Chemical Information and Modeling,2020,60(3):1202-1214.
|
APA |
Xia,Jie.,Wang,Zhenyi.,Huan,Yi.,Xue,Wenjie.,Wang,Xing.,...&Wang,Xiang Simon.(2020).Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists.Journal of Chemical Information and Modeling,60(3),1202-1214.
|
MLA |
Xia,Jie,et al."Pose Filter-Based Ensemble Learning Enables Discovery of Orally Active, Nonsteroidal Farnesoid X Receptor Agonists".Journal of Chemical Information and Modeling 60.3(2020):1202-1214.
|
条目包含的文件 | 条目无相关文件。 |
|
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论