DBETH: a Database of Bacterial Exotoxins for Human.
DBETH: a Database of Bacterial Exotoxins for Human.
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DOI：
10.1093/nar/gkr942
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发表时间：
2012-01
影响因子：
14.9
通讯作者：
Chakrabarti S
中科院分区：
文献类型：
--
作者：
Chakraborty A;Ghosh S;Chowdhary G;Maulik U;Chakrabarti S
致病细菌产生蛋白质毒素，以便在宿主防御系统和免疫反应所定义的恶劣环境中生存。高通量基因组测序和结构测定技术的最新进展有助于更好地了解细菌毒素在细胞和分子水平上导致致病性的作用机制。可以合理地假设，随着时间的推移，越来越多的未知毒素将会出现，不仅是由于新物种的发现，而且是由于现有细菌基因组的遗传重排。因此，对已知细菌毒素的固有特征进行系统汇编和后续分析至关重要。我们开发了细菌外毒素数据库（DBETH，http://www.hpppi.iicb.res.in/btox/），其中包含来自 26 个细菌属的 24 种机械和活性类型的 229 种毒素的序列、结构、相互作用网络和分析结果。该数据库的主要目标是提供人类致病细菌毒素的综合知识库，其中提供了各种重要的序列、结构和理化性质的分析。此外，我们开发了一个附加到该数据库的预测服务器，其目的是通过与已知毒素序列/域建立同源性或通过使用基于支持向量的机器学习技术对细菌毒素特定特征进行分类来识别细菌毒素样序列。
Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the host's defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of action of the bacterial toxins at the cellular and molecular levels leading to pathogenicity. It is fair to assume that with time more and more unknown toxins will emerge not only by the discovery of newer species but also due to the genetic rearrangement of existing bacterial genomes. Hence, it is crucial to organize a systematic compilation and subsequent analyses of the inherent features of known bacterial toxins. We developed a Database for Bacterial ExoToxins (DBETH, http://www.hpppi.iicb.res.in/btox/), which contains sequence, structure, interaction network and analytical results for 229 toxins categorized within 24 mechanistic and activity types from 26 bacterial genuses. The main objective of this database is to provide a comprehensive knowledgebase for human pathogenic bacterial toxins where various important sequence, structure and physico-chemical property based analyses are provided. Further, we have developed a prediction server attached to this database which aims to identify bacterial toxin like sequences either by establishing homology with known toxin sequences/domains or by classifying bacterial toxin specific features using a support vector based machine learning techniques.
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影响因子：
5.6
作者：
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D
影响因子：
14.9
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Letunic I;Doerks T;Bork P
通讯作者：
Bork P
影响因子：
14.9
作者：
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影响因子：
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Zmasek CM
影响因子：
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作者：
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通讯作者：
VAPNIK, V