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<p>RNA modifications are critical in regulating gene expression and cell functions by affecting RNA stability, splicing, translation, and degradation. The catalytic core of N<sup>6</sup>-adenosine-methyltransferase catalytic subunit METTL3 has emerged as a key enzyme in tumorigenesis by enhancing the translation efficiency of oncogenic transcripts, which is a promising therapeutic target for cancers, including acute myeloid leukemia. In this study, we presented a novel METTL3 inhibitory bioactivity (pIC<sub>50</sub>) prediction model (ML3-mix-DPLIFE) by combining machine learning, protein–ligand docking, and protein–ligand interaction analysis, through encoding the conventional physicochemical properties, chemical fingerprint, and the docking-based protein–ligand interaction features (DPLIFE) with leveraging auto-stacking 6 algorithms. A feature selection algorithm further optimized the model (ML3-mix-DPLIFE-FS) and obtained a promising mean squared error (<i>MSE</i>) of 0.261 and a Pearson’s correlation coefficient (<i>CC</i>) of 0.853 on an independent test dataset, and identified 8 residues critical for ligand interactions with METTL3. To further test the model, the pIC<sub>50</sub>s of recently reported inhibitors were predicted using the ML3-mix-DPLIFE-FS model, and a good <i>MSE</i> of 0.418 and <i>CC</i> of 0.727 were obtained. This innovative strategy seamlessly integrates machine learning prediction with structural biology insights and reveals a novel way to identify key protein–ligand interactions for further structural rational drug design.</p>

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Integrating machine learning and protein–ligand interaction profiling for the discovery of METTL3 inhibitors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Wei-Cheng Huang,
  • Hsing-Pang Hsieh,
  • Chun-Wei Tung

摘要

RNA modifications are critical in regulating gene expression and cell functions by affecting RNA stability, splicing, translation, and degradation. The catalytic core of N6-adenosine-methyltransferase catalytic subunit METTL3 has emerged as a key enzyme in tumorigenesis by enhancing the translation efficiency of oncogenic transcripts, which is a promising therapeutic target for cancers, including acute myeloid leukemia. In this study, we presented a novel METTL3 inhibitory bioactivity (pIC50) prediction model (ML3-mix-DPLIFE) by combining machine learning, protein–ligand docking, and protein–ligand interaction analysis, through encoding the conventional physicochemical properties, chemical fingerprint, and the docking-based protein–ligand interaction features (DPLIFE) with leveraging auto-stacking 6 algorithms. A feature selection algorithm further optimized the model (ML3-mix-DPLIFE-FS) and obtained a promising mean squared error (MSE) of 0.261 and a Pearson’s correlation coefficient (CC) of 0.853 on an independent test dataset, and identified 8 residues critical for ligand interactions with METTL3. To further test the model, the pIC50s of recently reported inhibitors were predicted using the ML3-mix-DPLIFE-FS model, and a good MSE of 0.418 and CC of 0.727 were obtained. This innovative strategy seamlessly integrates machine learning prediction with structural biology insights and reveals a novel way to identify key protein–ligand interactions for further structural rational drug design.