Truong Khanh Linh Dang1, Thach Nguyen2, Michael Habeck2,3,4, Mehmet Gültas5,6, Stephan Waack7. 1. Institute of Computer Science, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany. linh.dang@informatik.uni-goettingen.de. 2. Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany. 3. Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany. 4. Microscopic Image Analysis Group, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany. 5. Breeding Informatics Group, Department of Animal Sciences, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany. 6. Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany. 7. Institute of Computer Science, University of Göttingen, Goldschmidtstr 7, 37077, Göttingen, Germany.
Abstract
BACKGROUND: 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. 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. RESULTS: We 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. CONCLUSIONS: The 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/ .
BACKGROUND: 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. 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. RESULTS: We 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. CONCLUSIONS: The 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/ .
Entities:
Keywords:
Generalized Viterbi algorithm; Graph algorithms; Protein structural transition
Authors: Kap Lim; Randy J Read; Celia C H Chen; Aleksandra Tempczyk; Min Wei; Dongmei Ye; Chun Wu; Debra Dunaway-Mariano; Osnat Herzberg Journal: Biochemistry Date: 2007-12-04 Impact factor: 3.162