Graph representation of high-dimensional alpha-helical membrane protein data

BioData Mining

Steffen Grunert¹  Dirk Labudde¹ 

[1] Department of Mathematics, Sciences, Computer Science, University of Applied Sciences Mittweida, Technikumplatz 17, Mittweida 09648, Germany

关键词: Architecture;    Graph;    Motifs;    Membrane proteins;   

Others  :  797144
DOI  :  10.1186/1756-0381-6-21

 received in 2013-08-07, accepted in 2013-11-26,  发布年份 2013

【 摘 要 】

Background

In genomics and proteomics, membrane protein analysis have shown that such analyses are very important to support the understanding of complex biological processes. In Genome-wide investigations of membrane proteins a large number of short, distinct sequence motifs has been revealed. Such motifs found so far support the understanding of the folded membrane protein in the membrane environment. They provide important information about functional or stabilizing properties. Recently several integrative approaches have been proposed to extract meaningful information out of the membrane environment. However, many information based approaches deliver results having deficits of visualisation outputs. Outgoing from high-throughput protein data analysis, these outputs play an important role in the evaluation of high-dimensional protein data, to establish a biological relationship and ultimately to provide useful information for research.

Results

We have evaluated different resulting graphs generated from statistical analysis of consecutive motifs in helical structures of the membrane environment. Our results show that representative motifs with high occurrence in all investigated protein families are responsible for the general importance in alpha-helical membrane structure formation. Further, motifs which often occur with others in their function as so called “hubs” lead to the assumption, that these motifs constitute as important components in helical structures within the membrane. Otherwise, consecutive motifs and hubs which show a high occurrence in certain families only can be classified as important for family-specific functional characteristics. Summarized, we are able to bridge our graphical results from high-throughput analysis of membrane proteins over networking with databases to a biological context.

Conclusions

Our results and the corresponding graphical visualisation support the understanding and interpretation of structure forming and functional motifs of membrane proteins. Our results are useful to interpret and refine results of common developed approaches. At last we show a simple way to visualise high-dimensional protein data in context to biological relevant information.

【 授权许可】

   
2013 Grunert and Labudde; licensee BioMed Central Ltd.

【 图 表 】

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