Rearrangement of a Unique Kv1.3 Selectivity Filter Conformation Upon Binding of a Drug
Proceedings of the National Academy of Sciences of the United States of America(2022)SCI 1区
Nanyang Technol Univ | Max Planck Inst Biophys | Natl Univ Singapore | LKCMedicine-ICESing Ion Channel Platform | Univ Sci & Technol China | Univ Calif Davis
Abstract
Significance Voltage-gated potassium channels (Kv) open with membrane depolarization and allow the flow of K + ions. Ion flow is tightly governed by time-dependent entry into nonconducting inactivated states. Here, we focus on Kv1.3, a channel of physiological importance in immune cells. We used cryogenic electron microscopy to determine structures of human Kv1.3 alone and bound to dalazatide, a peptide inhibitor in human trials. In the unbound state, Kv1.3’s outer pore is rearranged compared to all other K + channels analyzed. Interaction of dalazatide with Kv1.3’s outer pore causes a dynamic rearrangement of the selectivity filter as Kv1.3 enters a drug-blocked state.
MoreTranslated text
Key words
ion channels,potassium channels,selectivity filter,ShK,dalazatide
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2009
被引用134 | 浏览
2007
被引用1599 | 浏览
2005
被引用17 | 浏览
1996
被引用1428 | 浏览
2012
被引用20533 | 浏览
1996
被引用123 | 浏览
1998
被引用8627 | 浏览
2009
被引用1048 | 浏览
2013
被引用208 | 浏览
2003
被引用293 | 浏览
2017
被引用5798 | 浏览
2017
被引用3877 | 浏览
2018
被引用4544 | 浏览
2016
被引用298 | 浏览
2017
被引用109 | 浏览
2017
被引用131 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper