中文版 | English

在结果中检索

GSTDTAP

浏览/检索结果: 共5条，第1-5条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
Bacteria are important dimethylsulfoniopropionate producers in marine aphotic and high-pressure environments 期刊论文
Nature Communications, 2020
作者:  Yanfen Zheng;  Jinyan Wang;  Shun Zhou;  Yunhui Zhang;  Ji Liu;  Chun-Xu Xue;  Beth T. Williams;  Xiuxiu Zhao;  Li Zhao;  Xiao-Yu Zhu;  Chuang Sun;  Hong-Hai Zhang;  Tian Xiao;  Gui-Peng Yang;  Jonathan D. Todd;  Xiao-Hua Zhang
收藏  |  浏览/下载：9/0  |  提交时间：2020/09/22
Mapping the drivers of formaldehyde (HCHO) variability from 2015-2019 over eastern China: insights from FTIR observation and GEOS-Chem model simulation 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Youwen Sun, Hao Yin, Cheng Liu, Lin Zhang, Yuan Cheng, Mathias Palm, Justus Notholt, Xiao Lu, Corinne Vigouroux, Bo Zheng, Wei Wang, Nicholas Jones, Changong Shan, Yuan Tian, Qihou Hu, and Jianguo Liu
收藏  |  浏览/下载：43/0  |  提交时间：2020/08/09
Surrogate‐Based Joint Estimation of Subsurface Geological and Relative Permeability Parameters for High‐Dimensional Inverse Problem by Use of Smooth Local Parameterization 期刊论文
Water Resources Research, 2020
作者:  Cong Xiao;  Leng Tian
收藏  |  浏览/下载：4/0  |  提交时间：2020/05/20
Plasmapause surface wave oscillates the magnetosphere and diffuse aurora 期刊论文
Nature, 2020
作者:  Fei He;  Rui-Long Guo;  William R. Dunn;  Zhong-Hua Yao;  Hua-Sen Zhang;  Yi-Xin Hao;  Quan-Qi Shi;  Zhao-Jin Rong;  Jiang Liu;  An-Min Tian;  Xiao-Xin Zhang;  Yong Wei;  Yong-Liang Zhang;  Qiu-Gang Zong;  Zu-Yin Pu;  Wei-Xing Wan
收藏  |  浏览/下载：15/0  |  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：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).