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A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations.

ABSTRACT: Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within a protein rather than residues or secondary structure and find that even very simple analyses using this representation form a powerful event detection system with significant advantages over residue-based graph representations. We additionally compare graph edges based on probability of contact to graph edges based on contact strength and analyses of the entire graph structure to an alternative and more computationally tractable node-based analysis. We develop the simplest useful technique for analyzing protein simulations based on these comparisons and use it to shed light on the speed with which static protein structures adjust to a solvated environment at room temperature in simulation.

SUBMITTER: Benson NC

PROVIDER: S-EPMC3731134 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations.

Benson Noah C NC Daggett Valerie V

Journal of bioinformatics and computational biology 20120622 4

Graphs are rapidly becoming a powerful and ubiquitous tool for the analysis of protein structure and for event detection in dynamical protein systems. Despite their rise in popularity, however, the graph representations employed to date have shared certain features and parameters that have not been thoroughly investigated. Here, we examine and compare variations on the construction of graph nodes and graph edges. We propose a graph representation based on chemical groups of similar atoms within ...[more]

PMID: 22809421

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Project description: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.

Dataset Information

A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations.

Publications

A chemical group graph representation for efficient high-throughput analysis of atomistic protein simulations.

Similar Datasets

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