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Exceptional nexus with a hybrid topological invariant 期刊论文
Science, 2020
作者:  Weiyuan Tang;  Xue Jiang;  Kun Ding;  Yi-Xin Xiao;  Zhao-Qing Zhang;  C. T. Chan;  Guancong Ma
收藏  |  浏览/下载：7/0  |  提交时间：2020/11/30
Horizontal gene transfer of Fhb7 from fungus underlies Fusarium head blight resistance in wheat 期刊论文
Science, 2020
作者:  Hongwei Wang;  Silong Sun;  Wenyang Ge;  Lanfei Zhao;  Bingqian Hou;  Kai Wang;  Zhongfan Lyu;  Liyang Chen;  Shoushen Xu;  Jun Guo;  Min Li;  Peisen Su;  Xuefeng Li;  Guiping Wang;  Cunyao Bo;  Xiaojian Fang;  Wenwen Zhuang;  Xinxin Cheng;  Jianwen Wu;  Luhao Dong;  Wuying Chen;  Wen Li;  Guilian Xiao;  Jinxiao Zhao;  Yongchao Hao;  Ying Xu;  Yu Gao;  Wenjing Liu;  Yanhe Liu;  Huayan Yin;  Jiazhu Li;  Xiang Li;  Yan Zhao;  Xiaoqian Wang;  Fei Ni;  Xin Ma;  Anfei Li;  Steven S. Xu;  Guihua Bai;  Eviatar Nevo;  Caixia Gao;  Herbert Ohm;  Lingrang Kong
收藏  |  浏览/下载：17/0  |  提交时间：2020/05/25
Quantum interference in H + HD → H2 + D between direct abstraction and roaming insertion pathways 期刊论文
Science, 2020
作者:  Yurun Xie;  Hailin Zhao;  Yufeng Wang;  Yin Huang;  Tao Wang;  Xin Xu;  Chunlei Xiao;  Zhigang Sun;  Dong H. Zhang;  Xueming Yang
收藏  |  浏览/下载：8/0  |  提交时间：2020/05/20
Nanoplasma-enabled picosecond switches for ultrafast electronics (vol 579, pg 534, 2020) 期刊论文
NATURE, 2020, 580 (7803) : E8-E8
作者:  Li, Jing;  Xu, Chuanliang;  Lee, Hyung Joo;  Ren, Shancheng;  Zi, Xiaoyuan;  Zhang, Zhiming;  Wang, Haifeng;  Yu, Yongwei;  Yang, Chenghua;  Gao, Xiaofeng;  Hou, Jianguo;  Wang, Linhui;  Yang, Bo;  Yang, Qing;  Ye, Huamao;  Zhou, Tie;  Lu, Xin;  Wang, Yan;  Qu, Min;  Yang, Qingsong;  Zhang, Wenhui;  Shah, Nakul M.;  Pehrsson, Erica C.;  Wang, Shuo;  Wang, Zengjun;  Jiang, Jun;  Zhu, Yan;  Chen, Rui;  Chen, Huan;  Zhu, Feng;  Lian, Bijun;  Li, Xiaoyun;  Zhang, Yun;  Wang, Chao;  Wang, Yue;  Xiao, Guangan;  Jiang, Junfeng;  Yang, Yue;  Liang, Chaozhao;  Hou, Jianquan;  Han, Conghui;  Chen, Ming;  Jiang, Ning;  Zhang, Dahong;  Wu, Song;  Yang, Jinjian;  Wang, Tao;  Chen, Yongliang;  Cai, Jiantong;  Yang, Wenzeng;  Xu, Jun;  Wang, Shaogang;  Gao, Xu;  Wang, Ting;  Sun, Yinghao
收藏  |  浏览/下载：25/0  |  提交时间：2020/07/03
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).