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CorNet:通过自动文献挖掘为共同进化残基网络赋予功能。

CorNet: Assigning function to networks of co-evolving residues by automated literature mining.

作者信息

van den Bergh Tom, Tamo Giorgio, Nobili Alberto, Tao Yifeng, Tan Tianwei, Bornscheuer Uwe T, Kuipers Remko K P, Vroling Bas, de Jong René M, Subramanian Kalyanasundaram, Schaap Peter J, Desmet Tom, Nidetzky Bernd, Vriend Gert, Joosten Henk-Jan

机构信息

Bio-Prodict, Nijmegen, The Netherlands.

Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands.

出版信息

PLoS One. 2017 May 18;12(5):e0176427. doi: 10.1371/journal.pone.0176427. eCollection 2017.

Abstract

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

摘要

CorNet是一个基于网络的工具,用于分析蛋白质超家族序列比对中共同进化的残基位置。CorNet将外部信息(如从文献中提取的突变数据)投射到交互式显示的共同进化残基位置组上,以阐明与这些组及其内部残基相关的功能。我们使用CorNet分析了六个酶超家族,发现高度共同进化的残基组往往由参与相同功能(如活性、特异性、辅因子结合或对映选择性)的残基组成。这一发现使得能够为文献中尚无数据的残基赋予功能。设计了一个突变体文库,对预测参与对映选择性但尚无文献数据的一组共同进化残基中观察到的残基进行突变。所得的突变集确实显示出许多对映选择性增加的实例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bf1/5436653/16999c44b8a4/pone.0176427.g001.jpg

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