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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：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).


  
Sea Surface Height Measurement Using a GNSS Wave Glider 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (11) : 5609-5616
作者:  Penna, Nigel T.;  Maqueda, Miguel A. Morales;  Martin, Ian;  Guo, Jing;  Foden, Peter R.
收藏  |  浏览/下载：3/0  |  提交时间：2019/04/09
Wave Glider  GNSS  sea surface height  dynamic ocean topography  geoid  significant wave height  
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
含碳质岩石的电性干扰及其找矿意义研究--以扎西康铅锌锑多金属矿为例 项目
项目编号：41604118； 经费：200000(CNY)； 起止日期：2017 / dc_date_end
项目负责人:  郭镜
收藏  |  浏览/下载：2/0  |  提交时间：2019/04/11
疏勒河源区高山冻土-植被景观对水文过程影响的试验研究 项目
项目编号：41671187； 经费：670000(CNY)； 起止日期：2017 / dc_date_end
项目负责人:  杨国靖
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/11
考虑冠层水碳耦合的双源遥感蒸散发模型的构建和验证 项目
项目编号：41601033； 经费：200000(CNY)； 起止日期：2017 / dc_date_end
项目负责人:  甘国靖
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/11
利用非重力卫星观测值补偿时变重力场缺失 项目
项目编号：41504009； 经费：210000(CNY)； 起止日期：2016 / dc_date_end
项目负责人:  郭靖
收藏  |  浏览/下载：0/0  |  提交时间：2019/04/11
朗缪尔环流诱导南海上混合层湍流混合过程及其非线性动力机制 项目
项目编号：41506001； 经费：220000(CNY)； 起止日期：2016 / dc_date_end
项目负责人:  李国敬
收藏  |  浏览/下载：1/0  |  提交时间：2019/04/11
基于气候变化及人类活动的未来流域水资源演变趋势预估研究 项目
项目编号：41401018； 经费：260000(CNY)； 起止日期：2015 / dc_date_end
项目负责人:  郭靖
收藏  |  浏览/下载：3/0  |  提交时间：2019/04/11