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AI-driven virtual screening platform identifies novel NSUN2 inhibitor candidates for targeted cancer therapy: a
Shuangqi Yu1,2,3, Qiao Peng1, Wei Wei2,3
1Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract:
The RNA cytosine-5 methyltransferase NSUN2 is an emerging therapeutic target in precision oncology, with aberrant overexpression driving tumor progression, metastasis, and therapy resistance across multiple malignancies. Despite its critical role in cancer biology, selective small-molecule inhibitors remain limited. We employed an AI-accelerated workflow to screen approximately 101 million compounds from the ZINC database using structure-based virtual screening. The AlphaFold2-predicted human NSUN2 structure was aligned with the experimentally determined M. jannaschii TRM4 homolog (PDB: 3A4T, 34.2% sequence identity, 1.82 Å RMSD). A CatBoost ensemble classifier trained on Morgan fingerprint descriptors with AutoDock Vina-derived labels achieved robust performance (training: recall 0.87, ROC-AUC 0.89; test: recall 0.71, ROC-AUC 0.85), with low test precision reflecting extreme class imbalance inherent to virtual screening. Multi-stage filtering identified 12,000 high-scoring compounds with binding affinities of -9.933 to -8.375 kcal/mol. ADMET profiling yielded 34 drug-like candidates with favorable pharmacokinetic and toxicological profiles. Molecular dynamics simulations over 50 nanoseconds validated binding stability of lead compounds ZINC-1000507789 and ZINC-1000507824. These structurally diverse non-covalent reversible inhibitors targeting the SAM cofactor binding pocket warrant experimental validation through biochemical assays and cellular studies to overcome therapeutic resistance in NSUN2-driven malignancies.
Insights
Researchers used AI to screen millions of compounds, identifying potential small-molecule inhibitors for the NSUN2 enzyme. These novel inhibitors could target NSUN2 overexpression in cancers, offering new therapeutic strategies for drug resistance.
Area of Science:
- Oncology
- Biochemistry
- Computational Chemistry
Background:
- The RNA cytosine-5 methyltransferase NSUN2 is overexpressed in various cancers, driving tumor progression and therapy resistance.
- Limited availability of selective small-molecule inhibitors hinders NSUN2-targeted precision oncology.
- NSUN2's role in cancer necessitates the development of novel therapeutic agents.
Purpose of the Study:
- To identify novel small-molecule inhibitors targeting the NSUN2 enzyme using an AI-accelerated virtual screening approach.
- To discover drug-like candidates with favorable ADMET profiles for potential therapeutic development.
- To validate the binding stability of lead compounds through molecular dynamics simulations.
Main Methods:
- Structure-based virtual screening of ~101 million compounds from the ZINC database using an AlphaFold2-predicted NSUN2 structure.
- Utilized a CatBoost ensemble classifier for compound scoring and filtering.
- Performed ADMET profiling and 50-nanosecond molecular dynamics simulations for lead compound validation.
Main Results:
- Identified 12,000 high-scoring compounds with predicted binding affinities ranging from -9.933 to -8.375 kcal/mol.
- 34 drug-like candidates with favorable pharmacokinetic and toxicological profiles were selected.
- Molecular dynamics simulations confirmed the binding stability of lead compounds ZINC-1000507789 and ZINC-1000507824.
Conclusions:
- AI-driven virtual screening successfully identified novel non-covalent reversible NSUN2 inhibitors targeting the SAM cofactor binding pocket.
- Lead compounds ZINC-1000507789 and ZINC-1000507824 warrant further experimental validation for treating NSUN2-driven malignancies.
- These findings offer promising avenues for overcoming therapeutic resistance in precision oncology.
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