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ClubSub-P:基于聚类的革兰氏阴性细菌和古菌亚细胞定位预测。

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea.

机构信息

Department I Protein Evolution, Max Planck Institute for Developmental Biology Tübingen, Germany.

出版信息

Front Microbiol. 2011 Nov 8;2:218. doi: 10.3389/fmicb.2011.00218. eCollection 2011.

Abstract

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/

摘要

蛋白质的亚细胞定位 (SCL) 为其在细胞中的功能提供了重要线索。在我们努力预测针对革兰氏阴性菌的有用疫苗靶点时,我们注意到错误注释的起始密码子经常导致错误分配的 SCL。通过将多个预测工具应用于同源蛋白组,可以避免 SCL 预测中的此类问题和其他问题,例如某些工具的假阳性和假阴性率相对较高。在这里,我们介绍了 ClubSub-P,这是一个在线数据库,它将现有的 SCL 预测工具组合成一个共识管道,来自完全测序的微生物的 600 多个蛋白质组。除了在单个序列级别进行共识预测外,该工具还使用来自革兰氏阴性菌和古菌的同源蛋白簇来消除假阳性和假阴性预测。ClubSub-P 可以高精度地分配来自革兰氏阴性菌和古菌的蛋白质的 SCL。该数据库可搜索,并可轻松使用新的细菌基因组或新的预测工具进行扩展,因为它们随时可用。这将进一步提高 SCL 预测的性能,以及检测错误注释的起始密码子和其他注释错误的性能。ClubSub-P 可在线访问,网址为 http://toolkit.tuebingen.mpg.de/clubsubp/

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76a7/3210502/cd410a11504a/fmicb-02-00218-g001.jpg

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