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Integrating machine learning and protein–ligand interaction profiling for the discovery of METTL3 inhibitors
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Journal Article
Integrating machine learning and protein–ligand interaction profiling for the discovery of METTL3 inhibitors
2025
Overview
RNA modifications are critical in regulating gene expression and cell functions by affecting RNA stability, splicing, translation, and degradation. The catalytic core of N
6
-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
50
) 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 pIC
50
s 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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 631/154
/ Datasets
/ Enzyme Inhibitors - chemistry
/ Enzyme Inhibitors - pharmacology
/ Human N6-adenosine-methyltransferase catalytic subunit METTL3
/ Humanities and Social Sciences
/ Humans
/ Leukemia
/ Ligands
/ Methyltransferases - antagonists & inhibitors
/ Methyltransferases - chemistry
/ Methyltransferases - metabolism
/ Molecular Docking Simulation
/ Principal components analysis
/ Proteins
/ Science
/ The docking-based protein–ligand interaction features (DPLIFE)
