A graph-based algorithm for detecting rigid domains in protein structures

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 66 - 19
• Main Authors: Dang, Truong Khanh Linh, Nguyen, Thach, Habeck, Michael, Gültas, Mehmet, Waack, Stephan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.02.2021; Springer Nature B.V; BMC
• Subjects: Algorithms; Amino acids; Bioinformatics; Biomedical and Life Sciences; Chemistry Techniques, Analytical - methods; Cluster Analysis; Clustering; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Generalized Viterbi algorithm; Graph algorithms; Graphical representations; Kinases; Labeling; Life Sciences; Methodology; Methodology Article; Methods; Microarrays; Novel computational methods for analysis of biological systems; Parameter identification; Protein structural transition; Proteins; Proteins - chemistry; Segmentation; Viterbi algorithm detectors; Protein structural transition; Graph algorithms; Generalized Viterbi algorithm
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-03966-3

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/ .