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Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody 期刊论文
Science, 2020
作者:  Zhe Lv;  Yong-Qiang Deng;  Qing Ye;  Lei Cao;  Chun-Yun Sun;  Changfa Fan;  Weijin Huang;  Shihui Sun;  Yao Sun;  Ling Zhu;  Qi Chen;  Nan Wang;  Jianhui Nie;  Zhen Cui;  Dandan Zhu;  Neil Shaw;  Xiao-Feng Li;  Qianqian Li;  Liangzhi Xie;  Youchun Wang;  Zihe Rao;  Cheng-Feng Qin;  Xiangxi Wang
收藏  |  浏览/下载：16/0  |  提交时间：2020/09/22
Measurement report: Evaluation of sources and mixing state of black carbon aerosol under the background of emission reduction in the North China Plain: implications for radiative effect 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Qiyuan Wang, Li Li, Jiamao Zhou, Jianhuai Ye, Wenting Dai, Huikun Liu, Yong Zhang, Renjian Zhang, Jie Tian, Yang Chen, Yunfei Wu, Weikang Ran, and Junji Cao
收藏  |  浏览/下载：19/0  |  提交时间：2020/07/21
Contributions of aerosol composition and sources to particulate optical properties in a southern coastal city of China 期刊论文
ATMOSPHERIC RESEARCH, 2020, 235
作者:  Tian, Jie;  Wang, Qiyuan;  Han, Yongming;  Ye, Jianhuai;  Wang, Ping;  Pongpiachan, Siwatt;  Ni, Haiyan;  Zhou, Yaqing;  Wang, Meng;  Zhao, Youzhi;  Cao, Junji
收藏  |  浏览/下载：11/0  |  提交时间：2020/07/02
PM2.5  Light extinction  Chemical composition  Source apportionment  
Reply to Comment by Jie Qin and Teng Wu on "A Modified Particle Filter-Based Data Assimilation Method for a High-Precision 2-D Hydrodynamic Model Considering Spatial-Temporal Variability of Roughness: Simulation of Dam-Break Flood Inundation" 期刊论文
WATER RESOURCES RESEARCH, 2020, 56 (3)
作者:  Cao, Yin;  Ye, Yuntao;  Liang, Lili;  Zhao, Hongli;  Jiang, Yunzhong;  Wang, Hao;  Yi, Zhenyan;  Shang, Yizi;  Yan, Dengming
收藏  |  浏览/下载：7/0  |  提交时间：2020/07/02
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).