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Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets f...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275593/ https://www.ncbi.nlm.nih.gov/pubmed/35837274 http://dx.doi.org/10.3389/fphar.2022.939555 |
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author | Zhu, Zhengdan Deng, Zhenfeng Wang, Qinrui Wang, Yuhang Zhang, Duo Xu, Ruihan Guo, Lvjun Wen, Han |
author_facet | Zhu, Zhengdan Deng, Zhenfeng Wang, Qinrui Wang, Yuhang Zhang, Duo Xu, Ruihan Guo, Lvjun Wen, Han |
author_sort | Zhu, Zhengdan |
collection | PubMed |
description | Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field. |
format | Online Article Text |
id | pubmed-9275593 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92755932022-07-13 Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design Zhu, Zhengdan Deng, Zhenfeng Wang, Qinrui Wang, Yuhang Zhang, Duo Xu, Ruihan Guo, Lvjun Wen, Han Front Pharmacol Pharmacology Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field. Frontiers Media S.A. 2022-06-28 /pmc/articles/PMC9275593/ /pubmed/35837274 http://dx.doi.org/10.3389/fphar.2022.939555 Text en Copyright © 2022 Zhu, Deng, Wang, Wang, Zhang, Xu, Guo and Wen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Zhu, Zhengdan Deng, Zhenfeng Wang, Qinrui Wang, Yuhang Zhang, Duo Xu, Ruihan Guo, Lvjun Wen, Han Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title | Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title_full | Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title_fullStr | Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title_full_unstemmed | Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title_short | Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design |
title_sort | simulation and machine learning methods for ion-channel structure determination, mechanistic studies and drug design |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275593/ https://www.ncbi.nlm.nih.gov/pubmed/35837274 http://dx.doi.org/10.3389/fphar.2022.939555 |
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