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

已选(0)清除 条数/页：   排序方式：
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).


  
Forecasting severe convective storms with WRF-based RTFDDA radar data assimilation in Guangdong, China 期刊论文
ATMOSPHERIC RESEARCH, 2018, 209: 131-143
作者:  Huang, Yongjie;  Liu, Yubao;  Xu, Mei;  Liu, Yuewei;  Pan, Linlin;  Wang, Haoliang;  Cheng, Will Y. Y.;  Jiang, Ying;  Lan, Hongping;  Yang, Honglong;  Wei, Xiaolin;  Zong, Rong;  Cao, Chunyan
收藏  |  浏览/下载：12/0  |  提交时间：2019/04/09
Severe convective storms  RTFDDA  Radar data assimilation  Latent heating  Nowcasting  
Weakly perturbative imaging of interfacial water with submolecular resolution by atomic force microscopy 期刊论文
NATURE COMMUNICATIONS, 2018, 9
作者:  Peng, Jinbo;  Guo, Jing;  Hapala, Prokop;  Cao, Duanyun;  Ma, Runze;  Cheng, Bowei;  Xu, Limei;  Ondracek, Martin;  Jelinek, Pavel;  Wang, Enge;  Jiang, Ying
收藏  |  浏览/下载：4/0  |  提交时间：2019/11/27
食细菌线虫与溶磷菌相互作用促进冬小麦生长的土壤磷素活化机制 项目
项目编号：41401274； 经费：260000(CNY)； 起止日期：2015 / dc_date_end
项目负责人:  姜瑛
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/11
内核平动振荡的探测及其对地球深部结构的约束 项目
项目编号：41404064； 经费：260000(CNY)； 起止日期：2015 / dc_date_end
项目负责人:  江颖
收藏  |  浏览/下载：1/0  |  提交时间：2019/04/11