DBETH: A Database of Bacterial Exotoxins for Human

被引:48
|
作者
Chakraborty, Abhijit [1 ,2 ]
Ghosh, Sudeshna [1 ,2 ]
Chowdhary, Garisha [3 ]
Maulik, Ujjwal [3 ]
Chakrabarti, Saikat [1 ,2 ]
机构
[1] CSIR, Indian Inst Chem Biol, Dept Biol Struct, Kolkata 700032, WB, India
[2] CSIR, Indian Inst Chem Biol, Bioinformat Div, Kolkata 700032, WB, India
[3] Jadavpur Univ, Dept Comp Sci & Engn, Kolkata 700032, WB, India
关键词
SIGNAL PEPTIDES; PREDICTION; CLASSIFICATION; PROTEINS; SEQUENCE; BIOLOGY; SERVER; SITE;
D O I
10.1093/nar/gkr942
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:D615 / D620
页数:6
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