Abstract
BackgroundConformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains.ResultsWe develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively.ConclusionsThe results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/.
Highlights
Conformational transitions are implicated in the biological function of many proteins
The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains
One could use the software to study molecular dynamics trajectories at the level of rigid domains to gain an understanding of large-scale movements, or identify important active sites located at the interface between rigid domains
Summary
Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Protein function is often driven by large-scale structural transitions [1]. A powerful approach to understand structural transitions in proteins is to decompose structures of different states into rigid domains and classify protein movements by hinge and shear motions of these structural domains [2]. Given the large number of available protein structures, we need computational methods that identify structurally conserved domains in a set of alternative structures in an automated fashion with minimal user intervention. One could use the software to study molecular dynamics trajectories at the level of rigid domains to gain an understanding of large-scale movements, or identify important active sites located at the interface between rigid domains
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