Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus. (28th May 2015)

• Record Type: Journal Article
• Authors: Grunert, Steffen; Labudde, Dirk
• Other Names: Taranto Maria D. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2015(2015)
• Publication Date: 2015-05-28
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2015/641393
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.