Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents
Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents
复制标题
血清素转运蛋白：计算机配体预测方法和结构生物学在结构指导治疗剂计算机设计方面的最新进展
DOI：
10.1016/j.jphs.2022.01.004
复制
发表时间：
2022
影响因子：
3.5
通讯作者：
Nagayasu Kazuki
中科院分区：
文献类型：
--
作者：
Kawashita E.;Ozaki T.;Ishihara K.;Kashiwada C.;AkibaS.;Nagayasu Kazuki
5 -羟色胺转运蛋白（SERT）是一种膜转运蛋白，它通过5 -羟色胺的再摄取终止其神经传递。这种转运体及其底物作为包括精神障碍在内的多种疾病的关键介质和药物靶点长期受到关注。因此，通过x射线晶体学研究了其结构基础，以深入了解对密切相关的转运体具有高亲和力和高特异性的配体设计。近年来，包括单粒子冷冻电镜在内的结构生物学的进展也在确定人类SERT复合物及其配体的结构方面取得了重大进展。此外，深度学习等机器学习的快速发展加速了计算机辅助药物设计。在这里，我们想总结一下我们对使用这两种快速发展的技术的SERT的理解的最新进展，局限性和未来的展望。
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.