このアイテムのアクセス数: 103
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
---|---|---|---|---|
j.jphs.2022.01.004.pdf | 536.31 kB | Adobe PDF | 見る/開く |
タイトル: | Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents |
著者: | Nagayasu, Kazuki ![]() ![]() |
著者名の別形: | 永安, 一樹 |
キーワード: | Serotonin transporter Machine learning Virtual screening Major depressive disorder |
発行日: | Mar-2022 |
出版者: | Elsevier BV |
誌名: | Journal of Pharmacological Sciences |
巻: | 148 |
号: | 3 |
開始ページ: | 295 |
終了ページ: | 299 |
抄録: | Serotonin transporter (SERT) is a membrane transporter which terminates neurotransmission of serotonin through its reuptake. This transporter as well as its substrate have long drawn attention as a key mediator and drug target in a variety of diseases including mental disorders. Accordingly, its structural basis has been studied by X-ray crystallography to gain insights into a design of ligand with high affinity and high specificity over closely related transporters. Recent progress in structural biology including single particle cryo-EM have made big strides also in determination of the structures of human SERT in complex with its ligands. Moreover, rapid progress in machine learning such as deep learning accelerates computer-assisted drug design. Here, we would like to summarize recent progresses in our understanding of SERT using these two rapidly growing technologies, limitations, and future perspectives. |
著作権等: | © 2022 The Authors. Production and hosting by Elsevier B.V. on behalf of Japanese Pharmacological Society. This is an open access article under the CC BY-NC-ND license. |
URI: | http://hdl.handle.net/2433/283248 |
DOI(出版社版): | 10.1016/j.jphs.2022.01.004 |
PubMed ID: | 35177208 |
出現コレクション: | 学術雑誌掲載論文等 |

このアイテムは次のライセンスが設定されています: クリエイティブ・コモンズ・ライセンス