The observation of evolutionary interaction pattern pairs in membrane proteins.

BACKGROUND: Over the last two decades, many approaches have been developed in bioinformatics that aim at one of the most promising, yet unsolved problems in modern life sciences--prediction of structural features of a protein. Such tasks addressed to transmembrane protein structures provide valuable knowledge about their three-dimensional structure. For this reason, the analysis of membrane proteins is essential in genomic and proteomic-wide investigations. Thus, many in-silico approaches have been utilized extensively to gain crucial advances in understanding membrane protein structures and functions. RESULTS: It turned out that amino acid covariation within interacting sequence parts, extracted from a evolutionary sequence record of α-helical membrane proteins, can be used for structure prediction. In a recent study we discussed the significance of short membrane sequence motifs widely present in nature that act as stabilizing 'building blocks' during protein folding and in retaining the three-dimensional fold. In this work, we used motif data to define evolutionary interaction pattern pairs. These were obtained from different pattern alignments and were used to evaluate which coupling mechanisms the evolution provides. It can be shown that short interaction patterns of homologous sequence records are membrane protein family-specific signatures. These signatures can provide valuable information for structure prediction and protein classification. The results indicate a good agreement with recent studies. CONCLUSIONS: Generally, it can be shown how the evolution contributes to realize covariation within discriminative interaction patterns to maintain structure and function. This points to their general importance for α-helical membrane protein structure formation and interaction mediation. In the process, no fundamentally energetic approaches of previous published works are considered. The low-cost rapid computational methods postulated in this work provides valuable information to classify unknown α-helical transmembrane proteins and to determine their structural similarity.

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus.

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.

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

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.