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DOI 10.1021/acs.jcim.6c00160

Медицинская библиотека · Journal of chemical information and modeling · 2026

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Chiariello Maria Gabriella, Biswas Akash Deep, Gratteri Carmen, Prandi Ingrid Guarnetti, Marrink Siewert-Jan, Beccari Andrea, Talarico Carmine

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Аннотация

The receptor tyrosine kinase KIT is essential for cell proliferation and differentiation, and its aberrant activation contributes to cancers such as acute myeloid leukemia and gastrointestinal stromal tumors. KIT activation normally requires stem cell factor (SCF)-induced dimerization, whereas oncogenic mutations can drive ligand-independent signaling. Here, we employ multiscale molecular dynamics simulations based on the Martini 3 coarse-grained (CG) force field to investigate the dimerization mechanisms of full-length KIT in both wild-type (WT) and oncogenic mutant forms within a realistic membrane environment. For the WT receptor, simulations capture SCF-driven dimerization through a cooperative "zipper-like" mechanism involving sequential domain-domain interactions. In contrast, the T417I, Δ418-419 mutant forms stable dimers spontaneously in the absence of SCF, stabilized by enhanced D5-D5 hydrophobic contacts. Back-mapping and atomistic refinement yielded the first all-atom (AA) structural model of the full-length KIT (T417I, Δ418-419) mutant, consistent with the V-shaped extracellular conformation observed experimentally. This study provides mechanistic insight into KIT activation and establishes a Martini 3-based framework for simulating receptor assembly and guiding the design of antibodies or small molecules that disrupt aberrant dimerization.

Для цитирования: Chiariello Maria Gabriella, Biswas Akash Deep, Gratteri Carmen, Prandi Ingrid Guarnetti, Marrink Siewert-Jan, Beccari Andrea, Talarico Carmine. Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.. Journal of chemical information and modeling. 2026. DOI: 10.1021/acs.jcim.6c00160.

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