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Identification of Integrator-PP2A complex (INTAC), an RNA polymerase II phosphatase 期刊论文
Science, 2020
作者:  Hai Zheng;  Yilun Qi;  Shibin Hu;  Xuan Cao;  Congling Xu;  Zhinang Yin;  Xizi Chen;  Yan Li;  Weida Liu;  Jie Li;  Jiawei Wang;  Gang Wei;  Kaiwei Liang;  Fei Xavier Chen;  Yanhui Xu
收藏  |  浏览/下载：11/0  |  提交时间：2020/11/30
Increase of High Molecular Weight Organosulfate With Intensifying Urban Air Pollution in the Megacity Beijing 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (10)
作者:  Xie, Qiaorong;  Li, Ying;  Yue, Siyao;  Su, Sihui;  Cao, Dong;  Xu, Yisheng;  Chen, Jing;  Tong, Haijie;  Su, Hang;  Cheng, Yafang;  Zhao, Wanyu;  Hu, Wei;  Wang, Zhe;  Yang, Ting;  Pan, Xiaole;  Sun, Yele;  Wang, Zifa;  Liu, Cong-Qiang;  Kawamura, Kimitaka;  Jiang, Guibin;  Shiraiwa, Manabu;  Fu, Pingqing
收藏  |  浏览/下载：18/0  |  提交时间：2020/07/02
Organic aerosol  Organosulfates  FT-ICR MS  Secondary organic aerosol  Volatile organic compounds  
Structure of the RNA-dependent RNA polymerase from COVID-19 virus 期刊论文
Science, 2020
作者:  Yan Gao;  Liming Yan;  Yucen Huang;  Fengjiang Liu;  Yao Zhao;  Lin Cao;  Tao Wang;  Qianqian Sun;  Zhenhua Ming;  Lianqi Zhang;  Ji Ge;  Litao Zheng;  Ying Zhang;  Haofeng Wang;  Yan Zhu;  Chen Zhu;  Tianyu Hu;  Tian Hua;  Bing Zhang;  Xiuna Yang;  Jun Li;  Haitao Yang;  Zhijie Liu;  Wenqing Xu;  Luke W. Guddat;  Quan Wang;  Zhiyong Lou;  Zihe Rao
收藏  |  浏览/下载：16/0  |  提交时间：2020/05/20
Molecular absorption and evolution mechanisms of PM2.5 brown carbon revealed by electrospray ionization ‐Fourier‐transform ion cyclotron resonance mass spectrometry during a severe winter pollution episode in Xi’an, China 期刊论文
Geophysical Research Letters, 2020
作者:  Yaling Zeng;  Zhenxing Shen;  Satoshi Takahama;  Leiming Zhang;  Tian Zhang;  Yali Lei;  Qian Zhang;  Hongmei Xu;  Yanli Ning;  Yu Huang;  Junji Cao;  Hö;  hn Rudolf
收藏  |  浏览/下载：8/0  |  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：143/0  |  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).