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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).


  
Chemical and optical properties of carbonaceous aerosols in Nanjing, eastern China: regionally transported biomass burning contribution 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2019, 19 (17) : 11213-11233
作者:  Liu, Xiaoyan;  Zhang, Yan-Lin;  Peng, Yiran;  Xu, Lulu;  Zhu, Chunmao;  Cao, Fang;  Zhai, Xiaoyao;  Haque, M. Mozammel;  Yang, Chi;  Chang, Yunhua;  Huang, Tong;  Xu, Zufei;  Bao, Mengying;  Zhang, Wenqi;  Fan, Meiyi;  Lee, Xuhui
收藏  |  浏览/下载:6/0  |  提交时间:2019/11/27
Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou in southern China 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2019, 19 (13) : 8471-8490
作者:  Tao, Jun;  Zhang, Zhisheng;  Wu, Yunfei;  Zhang, Leiming;  Wu, Zhijun;  Cheng, Peng;  Li, Mei;  Chen, Laiguo;  Zhang, Renjian;  Cao, Junji
收藏  |  浏览/下载:7/0  |  提交时间:2019/11/27
Characterization of urban amine-containing particles in southwestern China: seasonal variation, source, and processing 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2019, 19 (5) : 3245-3255
作者:  Chen, Yang;  Tian, Mi;  Huang, Ru-Jin;  Shi, Guangming;  Wang, Huanbo;  Peng, Chao;  Cao, Junji;  Wang, Qiyuan;  Zhang, Shumin;  Guo, Dongmei;  Zhang, Leiming;  Yang, Fumo
收藏  |  浏览/下载:14/0  |  提交时间:2019/04/09
Evidence for Majorana bound states in an iron-based superconductor 期刊论文
SCIENCE, 2018, 362 (6412) : 333-335
作者:  Wang, Dongfei;  Kong, Lingyuan;  Fan, Peng;  Chen, Hui;  Zhu, Shiyu;  Liu, Wenyao;  Cao, Lu;  Sun, Yujie;  Du, Shixuan;  Schneeloch, John;  Zhong, Ruidan;  Gu, Genda;  Fu, Liang;  Ding, Hong;  Gao, Hong-Jun
收藏  |  浏览/下载:7/0  |  提交时间:2019/11/27
Particle acidity and sulfate production during severe haze events in China cannot be reliably inferred by assuming a mixture of inorganic salts 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2018, 18 (14) : 10123-10132
作者:  Wang, Gehui;  Zhang, Fang;  Peng, Jianfei;  Duan, Lian;  Ji, Yuemeng;  Marrero-Ortiz, Wilmarie;  Wang, Jiayuan;  Li, Jianjun;  Wu, Can;  Cao, Cong;  Wang, Yuan;  Zheng, Jun;  Secrest, Jeremiah;  Li, Yixin;  Wang, Yuying;  Li, Hong;  Li, Na;  Zhang, Renyi
收藏  |  浏览/下载:7/0  |  提交时间:2019/04/09
Structure of the maize photosystem I supercomplex with light-harvesting complexes I and II 期刊论文
SCIENCE, 2018, 360 (6393) : 1109-1112
作者:  Pan, Xiaowei;  Ma, Jun;  Su, Xiaodong;  Cao, Peng;  Chang, Wenrui;  Liu, Zhenfeng;  Zhang, Xinzheng;  Li, Mei
收藏  |  浏览/下载:5/0  |  提交时间:2019/11/27
Characteristics of fine particulate matter and its sources in an industrialized coastal city, Ningbo, Yangtze River Delta, China 期刊论文
ATMOSPHERIC RESEARCH, 2018, 203: 105-117
作者:  Wang, Weifeng;  Yua, Jie;  Cui, Yang;  He, Jun;  Xue, Peng;  Cao, Wan;  Ying, Hongmei;  Gao, Wenkang;  Yan, Yingchao;  Hu, Bo;  Xin, Jinyuan;  Wang, Lili;  Liu, Zirui;  Sun, Yang;  Ji, Dongsheng;  Wang, Yuesi
收藏  |  浏览/下载:10/0  |  提交时间:2019/04/09
PM2.5  Chemical species  Source apportionment  Yangtze River Delta  
Seasonal characteristics, formation mechanisms and source origins of PM2.5 in two megacities in Sichuan Basin, China 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2018, 18 (2) : 865-881
作者:  Wang, Huanbo;  Tian, Mi;  Chen, Yang;  Shi, Guangming;  Liu, Yuan;  Yang, Fumo;  Zhang, Leiming;  Deng, Liqun;  Yu, Jiayan;  Peng, Chao;  Cao, Xuyao
收藏  |  浏览/下载:9/0  |  提交时间:2019/04/09
PAF1 regulation of promoter-proximal pause release via enhancer activation 期刊论文
SCIENCE, 2017, 357 (6357) : 1294-1298
作者:  Chen, Fei Xavier;  Xie, Peng;  Collings, Clayton K.;  Cao, Kaixiang;  Aoi, Yuki;  Marshall, Stacy A.;  Rendleman, Emily J.;  Ugarenko, Michal;  Ozark, Patrick A.;  Zhang, Anda;  Shiekhattar, Ramin;  Smith, Edwin R.;  Zhang, Michael Q.;  Shilatifard, Ali
收藏  |  浏览/下载:8/0  |  提交时间:2019/11/27