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The arms race between bacteria and their phage foes 期刊论文
NATURE, 2020, 577 (7790) : 327-336
作者:  Hirschey, Matthew
收藏  |  浏览/下载:20/0  |  提交时间:2020/07/03

Bacteria are under immense evolutionary pressure from their viral invaders-bacteriophages. Bacteria have evolved numerous immune mechanisms, both innate and adaptive, to cope with this pressure. The discovery and exploitation of CRISPR-Cas systems have stimulated a resurgence in the identification and characterization of anti-phage mechanisms. Bacteriophages use an extensive battery of counter-defence strategies to co-exist in the presence of these diverse phage defence mechanisms. Understanding the dynamics of the interactions between these microorganisms has implications for phage-based therapies, microbial ecology and evolution, and the development of new biotechnological tools. Here we review the spectrum of anti-phage systems and highlight their evasion by bacteriophages.


  
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).