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Molecular docking-aided identification of small molecule inhibitors targeting β-catenin-TCF4 interaction.


ABSTRACT: Here we report a molecular docking-based approach to identify small molecules that can target the β-catenin (β-cat)-TCF4 protein-protein interaction (PPI), a key effector complex for nuclear Wnt signaling activity. Specifically, we developed and optimized a computational model of β-cat using publicly available β-cat protein crystal structures, and existing β-cat-TCF4 interaction inhibitors as the training set. Using our computational model to an in silico screen predicted 27 compounds as good binders to β-cat, of which 3 were identified to be effective against a Wnt-responsive luciferase reporter. In vitro functional validation experiments revealed GB1874 as an inhibitor of the Wnt pathway that targets the β-cat-TCF4 PPI. GB1874 also affected the proliferation and stemness of Wnt-addicted colorectal cancer (CRC) cells in vitro. Encouragingly, GB1874 inhibited the growth of CRC tumor xenografts in vivo, thus demonstrating its potential for further development into therapeutics against Wnt-associated cancer indications.

SUBMITTER: Low JL 

PROVIDER: S-EPMC8184503 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Molecular docking-aided identification of small molecule inhibitors targeting β-catenin-TCF4 interaction.

Low Joo-Leng JL   Du Weina W   Gocha Tenzin T   Oguz Gokce G   Zhang Xiaoqian X   Chen Ming Wei MW   Masirevic Srdan S   Yim Daniel Guo Rong DGR   Tan Iain Bee Huat IBH   Ramasamy Adaikalavan A   Fan Hao H   DasGupta Ramanuj R  

iScience 20210515 6


Here we report a molecular docking-based approach to identify small molecules that can target the β-catenin (β-cat)-TCF4 protein-protein interaction (PPI), a key effector complex for nuclear Wnt signaling activity. Specifically, we developed and optimized a computational model of β-cat using publicly available β-cat protein crystal structures, and existing β-cat-TCF4 interaction inhibitors as the training set. Using our computational model to an <i>in silico</i> screen predicted 27 compounds as  ...[more]

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