Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family

doi:10.3389/fchem.2019.00324

	Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family
	Li, Fei 1,2; Wan, Xiaozhe 2,3; Xing, Jing 2,4; Tan, Xiaoqin 2,3; Li, Xutong 2,3; Wang, Yulan 2; Zhao, Jihui 2,3; Wu, Xiaolong 2,5; Liu, Xiaohong2,6 ; Li, Zhaojun 7; Luo, Xiaomin 2; Lu, Wencong 1; Zheng, Mingyue 2
	2019-05-10
发表期刊	FRONTIERS IN CHEMISTRY
ISSN	2296-2646
卷号	7
发表状态	已发表
DOI	10.3389/fchem.2019.00324
摘要	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.
关键词	deep neural network virtual screening methyltransferase epigenetic drug design
收录类别	SCI ; SCIE
语种	英语
资助项目	Personalized Medicines-Molecular Signature-based Drug Discovery and Development, Strategic Priority Research Pro-gram of the Chinese Academy of Sciences[XDA12050201]
WOS研究方向	Chemistry
WOS类目	Chemistry, Multidisciplinary
WOS记录号	WOS:000467689400001
出版者	FRONTIERS MEDIA SA
WOS关键词	PROTEIN ; DISCOVERY ; POTENT ; EZH2
原始文献类型	Article
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/40892
专题	生命科学与技术学院_博士生
通讯作者	Luo, Xiaomin; Lu, Wencong; Zheng, Mingyue
作者单位	1.Shanghai Univ, Coll Sci, Dept Chem, Shanghai, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China 3.Univ Chinese Acad Sci, Sch Pharm, Beijing, Peoples R China 4.Michigan State Univ, Dept Pediat & Human Dev, E Lansing, MI 48824 USA 5.East China Univ Sci & Technol, Sch Pharm, Shanghai, Peoples R China 6.Shanghai Tech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China 7.Dezhou Univ, Sch Informat Management, Dezhou, Peoples R China
推荐引用方式 GB/T 7714	Li, Fei,Wan, Xiaozhe,Xing, Jing,et al. Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family[J]. FRONTIERS IN CHEMISTRY,2019,7.
APA	Li, Fei.,Wan, Xiaozhe.,Xing, Jing.,Tan, Xiaoqin.,Li, Xutong.,...&Zheng, Mingyue.(2019).Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family.FRONTIERS IN CHEMISTRY,7.
MLA	Li, Fei,et al."Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family".FRONTIERS IN CHEMISTRY 7(2019).