Xiaowen Tang

Prof. Dr. Xiaowen Tang

orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 August 2019 in ACS Catalysis
Verified
Journal Article
Closed access
SMShan-Shui Meng
Shan-Shui Meng
XTXiaowen Tang
Xiaowen Tang
XLXiang Luo
Xiang Luo
RWRuibo Wu
Ruibo Wu
JZJun-Ling Zhao
Jun-Ling Zhao
ACAlbert Sun-chi Chan
Albert Sun-chi Chan

12 August 2019 in Journal of Chemical Information and Modeling
Verified
Journal Article
Closed access
TZTao Zeng
Tao Zeng
ZLZhihong Liu
Zhihong Liu
HLHuawei Liu
Huawei Liu
WHWengan He
Wengan He
XTXiaowen Tang
Xiaowen Tang
RWRuibo Wu
Ruibo Wu

12 June 2023 in Nature Chemistry
Verified
Journal Article
Closed access
PDF file
ZZZhiwei Zhang
Zhiwei Zhang
GLGiang N. T. Le
Giang N. T. Le
YGYang Ge
Yang Ge
XTXiaowen Tang
Xiaowen Tang
XCXin Chen
Xin Chen
GWG. Andrew Woolley
G. Andrew Woolley

31 October 2023 in Drug Discoveries & Therapeutics
Verified
Journal Article
Open access
PDF file
YLYuze Li
Yuze Li
LLLi Li
Li Li
SWShuang Wang
Shuang Wang
XTXiaowen Tang
Xiaowen Tang
Structure-based virtual screening plays a critical role in drug discovery. However, numerous docking programs, such as AutoDock Vina and Glide, are time-consuming due to the necessity of generating numerous molecular conformations and executing steps like scoring, ranking, and refinement for the ligand-receptor complexes. Consequently, achieving rapid and reliable virtual screening remains a noteworthy challenge. Recently, a team of researchers from Massachusetts Institute of Technology, led by Stärk et al., developed an SE(3)-equivariant geometric deep learning based protein-ligand binding prediction approach, EQUIBIND. In comparison to conventional docking methods, EQUIBIND has the capacity to predict the binding modes of small molecules with target proteins rapidly and precisely. It presents an innovative resolution for high-throughput screening of drug-like compounds.

9 February 2022 in Journal of Biological Chemistry
Verified
Journal Article
Open access
CNCanyang Niu
Canyang Niu
XSXiaoying Sun
Xiaoying Sun
FHFang Hu
Fang Hu
XTXiaowen Tang
Xiaowen Tang
KWKewei Wang
Kewei Wang
Transient receptor potential vanilloid 3 (TRPV3), robustly expressed in the skin, is a nonselective calcium-permeable cation channel activated by warm temperature, voltage, and certain chemicals. Natural monoterpenoid carvacrol from plant oregano is a known skin sensitizer or allergen that specifically activates TRPV3 channel. However, how carvacrol activates TRPV3 mechanistically remains to be understood. Here, we describe the molecular determinants for chemical activation of TRPV3 by the agonist carvacrol. Patch clamp recordings reveal that carvacrol activates TRPV3 in a concentration-dependent manner, with an EC50 of 0.2 mM, by increasing the probability of single-channel open conformation. Molecular docking of carvacrol into cryo-EM structure of TRPV3 combined with site-directed mutagenesis further identified a unique binding pocket formed by the channel S2-S3 linker important for mediating this interaction. Within the binding pocket consisting of four residues (Ile505, Leu508, Arg509, and Asp512), we report that Leu508 is the most critical residue for the activation of TRPV3 by carvacrol, but not 2-APB, a widely used nonspecific agonist and TRP channel modulator. Our findings demonstrate a direct binding of carvacrol to TRPV3 by targeting the channel S2-S3 linker that serves as a critical domain for chemical-mediated activation of TRPV3. We also propose that carvacrol can function as a molecular tool in the design of novel specific TRPV3 modulators for the further understanding of TRPV3 channel pharmacology.

1 August 2017 in Bioorganic Chemistry
Verified
Journal Article
Closed access
XTXiaowen Tang
Xiaowen Tang
RZRuiming Zhang
Ruiming Zhang
YLYanwei Li
Yanwei Li
QZQingzhu Zhang
Qingzhu Zhang
WWWenxing Wang
Wenxing Wang