Exploring FAM13A-N-Myc interactions to uncover potential targets in MYCN-amplified neuroblastoma: a study of protein interactions and molecular dynamics simulations.

Abstract

Neuroblastoma (NB), a common infantile neuroendocrine tumor, presents a substantial therapeutic challenge when MYCN is amplified. Given that the protein structure of N-Myc is disordered, we utilized Alphafold for prediction and GROMACS for optimization of the N-Myc structure, thereby improving the reliability of the predicted structure. The publicly available datasets GSE49710 and GSE73517 were adopted, which contain the transcriptome data of clinical samples from 598 NB patients. Through various machine learning algorithms, FAM13A was identified as a characteristic gene of MYCN. Cell functional experiments, including those on cell proliferation, apoptosis, and cell cycle, also indicate that FAM13A is a potential risk factor. Additionally, Alphafold and GROMACS were employed to predict and optimize the structure of FAM13A. Protein-protein docking and molecular dynamic modeling techniques were then used to validate the enhanced protein stability resulting from the interaction between N-Myc and FAM13A. Consequently, targeting FAM13A holds the potential to reduce the stability of N-Myc, hinder the proliferation of NB cells, and increase the infiltration of immune cells. This multi-faceted approach effectively combats tumor cells, making FAM13A a prospective therapeutic target for MYCN-amplified NB.