Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome proliferator-activated receptor gamma.

ABSTRACT: Peroxisome proliferator-activated receptor gamma (PPAR_γ) is an attractive target for chemoprevention of lung carcinoma, however its highly dynamic nature has plagued drug development for decades, with difficulties in receptor modeling for structure-based design. In this work, an integrated receptor-based virtual screening (VS) strategy was applied to identify PPAR_γ agonists as chemoprophylactic agents by using extensive docking and conformational sampling methods. Our results showed that the conformational plasticity of PPAR_γ, especially the H2 & S245 loop, H2' & Ω loop and AF-2 surface, is markedly affected by binding of full/partial agonists. To fully take the dynamic behavior of PPAR_γ into account, the VS approach effectively sorts out five co

SUBMITTER: Yang Z 

PROVIDER: S-EPMC8690233 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature