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Electromechanical coupling in the hyperpolarization-activated K+ channel KAT1 期刊论文
NATURE, 2020, 583 (7814) : 145-+
作者:  Jin, Zhenming;  Du, Xiaoyu;  Xu, Yechun;  Deng, Yongqiang;  Liu, Meiqin;  Zhao, Yao;  Zhang, Bing;  Li, Xiaofeng;  Zhang, Leike;  Peng, Chao;  Duan, Yinkai;  Yu, Jing;  Wang, Lin;  Yang, Kailin;  Liu, Fengjiang;  Jiang, Rendi;  Yang, Xinglou;  You, Tian;  Liu, Xiaoce
收藏  |  浏览/下载:28/0  |  提交时间:2020/07/03

Voltage-gated potassium (K-v) channels coordinate electrical signalling and control cell volume by gating in response to membrane depolarization or hyperpolarization. However, although voltage-sensing domains transduce transmembrane electric field changes by a common mechanism involving the outward or inward translocation of gating charges(1-3), the general determinants of channel gating polarity remain poorly understood(4). Here we suggest a molecular mechanism for electromechanical coupling and gating polarity in non-domain-swapped K-v channels on the basis of the cryo-electron microscopy structure of KAT1, the hyperpolarization-activated K-v channel from Arabidopsis thaliana. KAT1 displays a depolarized voltage sensor, which interacts with a closed pore domain directly via two interfaces and indirectly via an intercalated phospholipid. Functional evaluation of KAT1 structure-guided mutants at the sensor-pore interfaces suggests a mechanism in which direct interaction between the sensor and the C-linker hairpin in the adjacent pore subunit is the primary determinant of gating polarity. We suggest that an inward motion of the S4 sensor helix of approximately 5-7 angstrom can underlie a direct-coupling mechanism, driving a conformational reorientation of the C-linker and ultimately opening the activation gate formed by the S6 intracellular bundle. This direct-coupling mechanism contrasts with allosteric mechanisms proposed for hyperpolarization-activated cyclic nucleotide-gated channels(5), and may represent an unexpected link between depolarization- and hyperpolarization-activated channels.


The cryo-electron microscopy structure of the hyperpolarization-activated K+ channel KAT1 points to a direct-coupling mechanism between S4 movement and the reorientation of the C-linker.


  
Improved protein structure prediction using potentials from deep learning 期刊论文
NATURE, 2020, 577 (7792) : 706-+
作者:  Ma, Runze;  Cao, Duanyun;  Zhu, Chongqin;  Tian, Ye;  Peng, Jinbo;  Guo, Jing;  Chen, Ji;  Li, Xin-Zheng;  Francisco, Joseph S.;  Zeng, Xiao Cheng;  Xu, Li-Mei;  Wang, En-Ge;  Jiang, Ying
收藏  |  浏览/下载:142/0  |  提交时间:2020/07/03

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)  however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).


  
Rapid biotic rebound during the late Griesbachian indicates heterogeneous recovery patterns after the Permian-Triassic mass extinction 期刊论文
GEOLOGICAL SOCIETY OF AMERICA BULLETIN, 2018, 130: 2015-2030
作者:  Dai, Xu;  Song, Haijun;  Wignall, Paul B.;  Jia, Enhao;  Bai, Ruoyu;  Wang, Fengyu;  Chen, Jing;  Tian, Li
收藏  |  浏览/下载:14/0  |  提交时间:2019/04/09
A selfish genetic element confers non-Mendelian inheritance in rice 期刊论文
SCIENCE, 2018, 360 (6393) : 1130-1132
作者:  Yu, Xiaowen;  Zhao, Zhigang;  Zheng, Xiaoming;  Zhou, Jiawu;  Kong, Weiyi;  Wang, Peiran;  Bai, Wenting;  Zheng, Hai;  Zhang, Huan;  Li, Jing;  Liu, Jiafan;  Wang, Qiming;  Zhang, Long;  Liu, Kai;  Yu, Yang;  Guo, Xiuping;  Wang, Jiulin;  Lin, Qibing;  Wu, Fuqing;  Ren, Yulong;  Zhu, Shanshan;  Zhang, Xin;  Cheng, Zhijun;  Lei, Cailin;  Liu, Shijia;  Liu, Xi;  Tian, Yunlu;  Jiang, Ling;  Ge, Song;  Wu, Chuanyin;  Tao, Dayun;  Wang, Haiyang;  Wan, Jianmin
收藏  |  浏览/下载:20/0  |  提交时间:2019/11/27
Interannual cycles of Hantaan virus outbreaks at the human-animal interface in Central China are controlled by temperature and rainfall 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2017, 114 (30) : 8041-8046
作者:  Tian, Huaiyu;  Yu, Pengbo;  Cazelles, Bernard;  Xu, Lei;  Tan, Hua;  Yang, Jing;  Huang, Shanqian;  Xu, Bo;  Cai, Jun;  Ma, Chaofeng;  Wei, Jing;  Li, Shen;  Qu, Jianhui;  Laine, Marko;  Wang, Jingjun;  Tong, Shilu;  Stenseth, Nils Chr.;  Xu, Bing
收藏  |  浏览/下载:14/0  |  提交时间:2019/11/27
Hantaan virus  spillover to humans  wildlife reservoir  time-series data  climate change  
Emission factors and light absorption properties of brown carbon from household coal combustion in China 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2017, 17 (7)
作者:  Sun, Jianzhong;  Zhi, Guorui;  Hitzenberger, Regina;  Chen, Yingjun;  Tian, Chongguo;  Zhang, Yayun;  Feng, Yanli;  Cheng, Miaomiao;  Zhang, Yuzhe;  Cai, Jing;  Chen, Feng;  Qiu, Yiqin;  Jiang, Zhiming;  Li, Jun;  Zhang, Gan;  Mo, Yangzhi
收藏  |  浏览/下载:10/0  |  提交时间:2019/04/09