Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family
文献类型：期刊论文
| 作者 | Li, Fei4,5; Wan, Xiaozhe5,6; Xing, Jing1,5; Tan, Xiaoqin5,6; Li, Xutong5,6; Wang, Yulan5; Zhao, Jihui5,6; Wu, Xiaolong5,7; Liu, Xiaohong2,5; Li, Zhaojun3 |
| 刊名 | FRONTIERS IN CHEMISTRY
 |
| 出版日期 | 2019-05-10 |
| 卷号 | 7页码:17 |
| 关键词 | deep neural network virtual screening methyltransferase epigenetic drug design |
| ISSN号 | 2296-2646 |
| DOI | 10.3389/fchem.2019.00324 |
| 通讯作者 | Luo, Xiaomin(xmluo@simm.ac.cn) ; Lu, Wencong(wclu@shu.edu.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | PROTEIN ; DISCOVERY ; POTENT ; EZH2 |
| 资助项目 | National Natural Science Foundation of China[81773634] ; National Natural Science Foundation of China[81430084] ; National Science & Technology Major Project Key New Drug Creation and Manufacturing Program, China[2018ZX09711002] ; 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000467689400001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/289898]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Luo, Xiaomin; Lu, Wencong; Zheng, Mingyue |
| 作者单位 | 1.Michigan State Univ, Dept Pediat & Human Dev, E Lansing, MI 48824 USA 2.Shanghai Tech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China 3.Dezhou Univ, Sch Informat Management, Dezhou, Peoples R China 4.Shanghai Univ, Coll Sci, Dept Chem, Shanghai, Peoples R China 5.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China 6.Univ Chinese Acad Sci, Sch Pharm, Beijing, Peoples R China 7.East China Univ Sci & Technol, Sch Pharm, Shanghai, 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,2019,7:17. |
| 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,7,17. |
| 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):17. |
入库方式： OAI收割
来源：上海药物研究所
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