中文版 | English
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
SCI ; EI
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.
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