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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).


  
N-6-methyladenosine RNA modification-mediated cellular metabolism rewiring inhibits viral replication 期刊论文
SCIENCE, 2019, 365 (6458) : 1171-+
作者:  Liu, Yang;  You, Yuling;  Lu, Zhike;  Yang, Jiang;  Li, Panpan;  Liu, Lun;  Xu, Henan;  Niu, Yamei;  Cao, Xuetao
收藏  |  浏览/下载:5/0  |  提交时间:2019/11/27
Drivers of improved PM2.5 air quality in China from 2013 to 2017 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019
作者:  Qiang Zhang;  Yixuan Zheng;  Dan Tong;  Min Shao;  Shuxiao Wang;  Yuanhang Zhang;  Xiangde Xu;  Jinnan Wang;  Hong He;  Wenqing Liu;  Yihui Ding;  Yu Lei;  Junhua Li;  Zifa Wang;  Xiaoye Zhang;  Yuesi Wang;  Jing Cheng;  Yang Liu;  Qinren Shi;  Liu Yan;  Guannan Geng;  Chaopeng Hong;  Meng Li;  Fei Liu;  Bo Zheng;  Junji Cao;  Aijun Ding;  Jian Gao;  Qingyan Fu;  Juntao Huo;  Baoxian Liu;  Zirui Liu;  Fumo Yang;  Kebin He;  and Jiming Hao
收藏  |  浏览/下载:12/0  |  提交时间:2019/11/27
clean air actions  PM  emission abatements  air quality improvements  health benefits  
Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain 期刊论文
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019, 116 (16) : 7760-7765
作者:  Liu, Mingxu;  Huang, Xin;  Song, Yu;  Tang, Jie;  Cao, Junji;  Zhang, Xiaoye;  Zhang, Qiang;  Wang, Shuxiao;  Xu, Tingting;  Kang, Ling;  Cai, Xuhui;  Zhang, Hongsheng;  Yang, Fumo;  Wang, Huanbo;  Yu, Jian Zhen;  Lau, Alexis K. H.;  He, Lingyan;  Huang, Xiaofeng;  Duan, Lei;  Ding, Aijun;  Xue, Likun;  Gao, Jian;  Liu, Bin;  Zhu, Tong
收藏  |  浏览/下载:13/0  |  提交时间:2019/11/27
ammonia emission  China  PM2.5  acid rain  nitrogen deposition  
Observation of Dicke cooperativity in magnetic interactions 期刊论文
SCIENCE, 2018, 361 (6404) : 794-796
作者:  Li, Xinwei;  Bamba, Motoaki;  Yuan, Ning;  Zhang, Qi;  Zhao, Yage;  Xiang, Maolin;  Xu, Kai;  Jin, Zuanming;  Ren, Wei;  Ma, Guohong;  Cao, Shixun;  Turchinovich, Dmitry;  Kono, Junichiro
收藏  |  浏览/下载:7/0  |  提交时间:2019/11/27
Satellite-based entanglement distribution over 1200 kilometers 期刊论文
SCIENCE, 2017, 356 (6343) : 1180-1184
作者:  Yin, Juan;  Cao, Yuan;  Li, Yu-Huai;  Liao, Sheng-Kai;  Zhang, Liang;  Ren, Ji-Gang;  Cai, Wen-Qi;  Liu, Wei-Yue;  Li, Bo;  Dai, Hui;  Li, Guang-Bing;  Lu, Qi-Ming;  Gong, Yun-Hong;  Xu, Yu;  Li, Shuang-Lin;  Li, Feng-Zhi;  Yin, Ya-Yun;  Jiang, Zi-Qing;  Li, Ming;  Jia, Jian-Jun;  Ren, Ge;  He, Dong;  Zhou, Yi-Lin;  Zhang, Xiao-Xiang;  Wang, Na;  Chang, Xiang;  Zhu, Zhen-Cai;  Liu, Nai-Le;  Chen, Yu-Ao;  Lu, Chao-Yang;  Shu, Rong;  Peng, Cheng-Zhi;  Wang, Jian-Yu;  Pan, Jian-Wei
收藏  |  浏览/下载:17/0  |  提交时间:2019/11/27