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Domain-based Comparative Analysis of Bacterial Proteomes: Uniqueness, Interactions, and the Dark Matter

[ Vol. 20 , Issue. 2 ]

Author(s):

Liang Wang*, Jianye Yang, Yaping Xu, Xue Piao and Jichang Lv   Pages 115 - 123 ( 9 )

Abstract:


Background: Proteins may have none, single, double, or multiple domains, while a single domain may appear in multiple proteins. Their distribution patterns may have impacts on bacterial physiology and lifestyle.

Objective: This study aims to understand how domains are distributed and duplicated in bacterial proteomes, in order to better understand bacterial physiology and lifestyles.

Methods: In this study, we used 16712 Hidden Markov Models to screen 944 bacterial reference proteomes versus a threshold E-value<0.001. The number of non-redundant domains and duplication rates of redundant domains for each species were calculated. The unique domains, if any, were also identified for each species. In addition, the properties of no-domain proteins were investigated in terms of physicochemical properties.

Results: The increasing number of non-redundant domains for a bacterial proteome follows the trend of an asymptotic function. The domain duplication rate is positively correlated with proteome size and increases more rapidly. The high percentage of single-domain proteins is more associated with small proteome size. For each proteome, unique domains were also obtained. Moreover, no-domain proteins show differences with the other three groups for several physicochemical properties analysed in this study.

Conclusion: The study confirmed that a low domain duplication rate and a high percentage of singledomain proteins are more likely to be associated with bacterial host-dependent or restricted nicheadapted lifestyle. In addition, the unique lifestyle and physiology were revealed based on the analysis of species-specific domains and core domain interactions or co-occurrences.

Keywords:

Bacterial proteome, Hidden markov model, Pfam, Bacterial lifestyle, Domain interaction, Domain redundancy.

Affiliation:

Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu, 221000, Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu, 221000, Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu, 221000, Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu, 221000, Department of Bioinformatics, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu, 221000

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