In silico modeling of PAX8-PPARγ fusion protein in thyroid carcinoma: influence of structural perturbation by fusion on ligand-binding affinity.

Kaori Sakaguchi, Yoshio Okiyama, Shigenori Tanaka

Journal of computer-aided molecular design 2021 May

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Paired box 8 (PAX8)-peroxisome proliferator-activated receptor γ (PPARγ) rearrangement is believed to play an important role in tumorigenesis of PAX8-PPARγ fusion protein (PPFP) thyroid carcinomas, while without establishing any standard treatment, including drugs. Although PPFP is a potential promising target for therapeutic agents, the three-dimensional (3D) structure and functions have not yet been experimentally elucidated. In this study, we aimed to construct the 3D structure of PPFP and to aid in the development of therapies that can target PPFP for thyroid carcinomas. The 3D structure of PPFP was constructed by homology modeling based on crystallographic structure data. To validate the modeled structure, we analyzed the thermal fluctuations by molecular dynamics simulations and predicted the physical properties using bioinformatic analyses. We found that the modeled structure was stable under hydrated conditions and had features indicating the actual existence of the structure. Furthermore, the binding free energies of the ligand rosiglitazone with PPARγ and PPFP were evaluated by the molecular mechanics-Poisson-Boltzmann surface area method. We found that rosiglitazone has different binding affinities for the same binding pockets of PPARγ and PPFP, and the optimal compound for PPFP can differ from that of PPARγ. This suggests the need for the development of drugs targeting PPFP that allow for the fusion, rather than focusing on the PPARγ side of PPFP and searching for the best compounds for that pocket. Our findings are expected to lead to the development of new therapies for thyroid tumors.

Citation

Kaori Sakaguchi, Yoshio Okiyama, Shigenori Tanaka. In silico modeling of PAX8-PPARγ fusion protein in thyroid carcinoma: influence of structural perturbation by fusion on ligand-binding affinity. Journal of computer-aided molecular design. 2021 May;35(5):629-642