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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

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

Author(s): Liang Wang*, Jianye Yang, Yaping Xu, Xue Piao and Jichang Lv

Volume 20, Issue 2, 2019

Page: [115 - 123] Pages: 9

DOI: 10.2174/1389202920666190320134438

Price: $65

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.

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