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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：156/0  |  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).


  
Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on shannon entropy 期刊论文
ENERGY POLICY, 2019, 132: 429-439
作者:  Wang, Zheng-Xin;  He, Ling-Yang;  Li, Dan-Dan
收藏  |  浏览/下载：20/0  |  提交时间：2019/11/27
Electricity regulation  Organisational structure  Degree of order  Shannon entropy  Genetic algorithm  
The Rupture Process of the 2018 M-w 6.9 Hawai'i Earthquake as Imaged by a Genetic Algorithm-Based Back-Projection Technique 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2019, 46 (5) : 2467-2474
作者:  Kehoe, H. L.;  Kiser, E. D.;  Okubo, P. G.
收藏  |  浏览/下载：15/0  |  提交时间：2019/11/26
seismology  back projection  genetic algorithm  Hawaii  Kilauea  rupture  
Design government incentive schemes for promoting electric taxis in China 期刊论文
ENERGY POLICY, 2018, 115: 1-11
作者:  Yang, Jie;  Dong, Jing;  Hu, Liang
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/09
Electric taxis  Charging behavior  GPS trajectory data  Subsidy  Genetic algorithm  
Energy supply security for the Aegean islands: A routing model with risk and environmental considerations 期刊论文
ENERGY POLICY, 2018, 113: 608-620
作者:  Iliopoulou, Christina;  Kepaptsoglou, Konstantinos;  Schinas, Orestis
收藏  |  浏览/下载：8/0  |  提交时间：2019/04/09
Energy supply security  Oil maritime transportation  Oil spill risk  Alternating objective genetic algorithm  
Evaporation modelling using different machine learning techniques 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2017, 37
作者:  Wang, Lunche;  Kisi, Ozgur;  Hu, Bo;  Bilal, Muhammad;  Zounemat-Kermani, Mohammad;  Li, Hui
收藏  |  浏览/下载：16/0  |  提交时间：2019/04/09
pan evaporation  fuzzy genetic algorithm  ANFIS-GP  M5 model tree  cross-station application  
Interactive genetic algorithm for user-centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior 期刊论文
WATER RESOURCES RESEARCH, 2017, 53 (5)
作者:  Piemonti, Adriana Debora;  Babbar-Sebens, Meghna;  Mukhopadhyay, Snehasis;  Kleinberg, Austin
收藏  |  浏览/下载：7/0  |  提交时间：2019/04/09
interactive genetic algorithm  optimization  watershed  human-computer interaction  conservation planning