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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  KAKEN_id  orcid https://orcid.org/0000-0002-7438-732X (unconfirmed)
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

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