GSTDTAP

浏览/检索结果: 共32条,第1-10条 帮助

已选(0)清除 条数/页:   排序方式:
Decarbonising the iron and steel sector for a 2鈥壜癈 target using inherent waste streams 期刊论文
Nature Communications, 2022
作者:  Sun, Yongqi;  Tian, Sicong;  Ciais, Philippe;  Zeng, Zhenzhong;  Meng, Jing;  Zhang, Zuotai
收藏  |  浏览/下载:23/0  |  提交时间:2022/01/29
Lead halide–templated crystallization of methylamine-free perovskite for efficient photovoltaic modules 期刊论文
Science, 2021
作者:  Tongle Bu;  Jing Li;  Hengyi Li;  Congcong Tian;  Jie Su;  Guoqing Tong;  Luis K. Ono;  Chao Wang;  Zhipeng Lin;  Nianyao Chai;  Xiao-Li Zhang;  Jingjing Chang;  Jianfeng Lu;  Jie Zhong;  Wenchao Huang;  Yabing Qi;  Yi-Bing Cheng;  Fuzhi Huang
收藏  |  浏览/下载:25/0  |  提交时间:2021/06/24
A small climate-amplifying effect of climate-carbon cycle feedback 期刊论文
Nature Communications, 2021
作者:  Xuanze Zhang;  Ying-Ping Wang;  Peter J. Rayner;  Philippe Ciais;  Kun Huang;  Yiqi Luo;  Shilong Piao;  Zhonglei Wang;  Jianyang Xia;  Wei Zhao;  Xiaogu Zheng;  Jing Tian;  Yongqiang Zhang
收藏  |  浏览/下载:14/0  |  提交时间:2021/06/07
Microbial metabolic response to winter warming stabilizes soil carbon 期刊论文
Global Change Biology, 2021
作者:  Jing Tian;  Ning Zong;  Iain P. Hartley;  Nianpeng He;  Jinjing Zhang;  David Powlson;  Jizhong Zhou;  Yakov Kuzyakov;  Fusuo Zhang;  Guirui Yu;  Jennifer A. J. Dungait
收藏  |  浏览/下载:6/0  |  提交时间:2021/02/17
Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique 期刊论文
Geophysical Research Letters, 2021
作者:  Peilin Song;  Yongqiang Zhang;  Jing Tian
收藏  |  浏览/下载:4/0  |  提交时间:2021/02/17
Structural analysis of full-length SARS-CoV-2 spike protein from an advanced vaccine candidate 期刊论文
Science, 2020
作者:  Sandhya Bangaru;  Gabriel Ozorowski;  Hannah L. Turner;  Aleksandar Antanasijevic;  Deli Huang;  Xiaoning Wang;  Jonathan L. Torres;  Jolene K. Diedrich;  Jing-Hui Tian;  Alyse D. Portnoff;  Nita Patel;  Michael J. Massare;  John R. Yates;  David Nemazee;  James C. Paulson;  Greg Glenn;  Gale Smith;  Andrew B. Ward
收藏  |  浏览/下载:14/0  |  提交时间:2020/11/30
Sustainable production of value-added carbon nanomaterials from biomass pyrolysis 期刊论文
NATURE SUSTAINABILITY, 2020
作者:  Zhang, Shun;  Jiang, Shun-Feng;  Huang, Bao-Cheng;  Shen, Xian-Cheng;  Chen, Wen-Jing;  Zhou, Tian-Pei;  Cheng, Hui-Yuan;  Cheng, Bin-Hai;  Wu, Chang-Zheng;  Li, Wen-Wei;  Jiang, Hong;  Yu, Han-Qing
收藏  |  浏览/下载:19/0  |  提交时间:2020/05/20
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:27/0  |  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.


  
Evolutionary selection of biofilm-mediated extended phenotypes in Yersinia pestis in response to a fluctuating environment 期刊论文
NATURE COMMUNICATIONS, 2020, 11 (1)
作者:  Cui, Yujun;  Schmid, Boris, V;  Cao, Hanli;  Dai, Xiang;  Du, Zongmin;  Easterday, W. Ryan;  Fang, Haihong;  Guo, Chenyi;  Huang, Shanqian;  Liu, Wanbing;  Qi, Zhizhen;  Song, Yajun;  Tian, Huaiyu;  Wang, Min;  Wu, Yarong;  Xu, Bing;  Yang, Chao;  Yang, Jing;  Yang, Xianwei;  Zhang, Qingwen;  Jakobsen, Kjetill S.;  Zhang, Yujiang;  Stenseth, Nils Chr;  Yang, Ruifu
收藏  |  浏览/下载:14/0  |  提交时间:2020/05/13
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).