Abstract
The (S)-adenosyl-L-methionine (SAM)-dependent methyltransferases play essential roles in post-translational modifications (PTMs) and other miscellaneous biological processes, and are implicated in the pathogenesis of various genetic disorders and cancers. Increasing efforts have been committed toward discovering novel PTM inhibitors targeting the (S)-Adenosyl-L-methionine (SAM)-binding site and the substrate-binding site of methyltransferases, among which virtual screening (VS) and structure-based drug design (SBDD) are the most frequently used strategies. Here, we report the development of a target-specific scoring model for compound VS, which predict the likelihood of the compound being a potential inhibitor for the SAM-binding pocket of a given methyltransferase. Protein-ligand interaction characterized by Fingerprinting Triplets of Interaction Pseudoatoms was used as the input feature, and a binary classifier based on deep neural networks is trained to build the scoring model. This model enhances the efficiency of the existing strategies used for discovering novel chemical modulators of methyltransferase, which is crucial for understanding and exploring the complexity of epigenetic target space.
Highlights
Methyltransferases (MTases) are a class of enzymes that transfer methyl groups to the substrates including DNA, proteins and small molecules (Zhang and Zheng, 2016)
Data Sources Based on the previous work of our workgroup, the data used to build model include the same set of 12 SAM-dependent methyltransferases, which are DNA-methyltransferase 1 (DNMT1), coactivatorassociated arginine methyltransferase 1 (CARM1), protein arginine N-methyltransferase 1 (PRMT1), protein arginine N-methyltransferase 3 (PRMT3), protein arginine N-methyltransferase 5 (PRMT5), protein arginine Nmethyl-transferase 6 (PRMT6), euchromatic histone-lysine N-methyl-transferase 1 (EHMT1), euchromatic histone-lysine N-methyltransferase 2 (EHMT2), SET domain containing lysine methyltransferase 7 (SETD7), SET domain containing lysine methyltransferase 8 (SETD8), suppressor of variegation 3-9 homolog 2 (SUV39H2) and disruptor of telomeric silencing 1-like histone H3K79 methyltransferase (DOT1L)
The structures and activities data of small molecule ligands for the 12 targets were collected from the ChEMBL database, and the IC50, EC50, and Ki values less than or equal to 10 micromole were used as positive data, and that more than 50 micromole as negative data
Summary
Methyltransferases (MTases) are a class of enzymes that transfer methyl groups to the substrates including DNA, proteins and small molecules (Zhang and Zheng, 2016). Most methyltransferases use S-adenosyl-L-methionine (SAM) as a donor for methyl groups, where all have a SAM-binding pocket and a substrate-binding pocket (Martin and McMillan, 2002). These SAM-dependent MTases participate in numerous essential biological processes, including the epigenetic control of cell fate, cell signaling and degration of metabolites (Hu et al, 2015; Schapira, 2016). Pyridone-based EZH2 inhibitors CPI-1205, EPZ-6438 and GSK-126 have been in phase I clinical trials. Finding of MTases inhibitors with novel scaffolds is still a challenging research area
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