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Predicting Protein Folds with Structural Repeats Using a Chain Graph Model
Institute for Software Research
  • Yan Liu, Carnegie Mellon University
  • Eric P. Xing, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
Conference Proceeding
Abstract or Description
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeats are prevalent in proteins involved in a wide spectrum of biological functions. However, extant approaches often perform inadequately due to their inability to capture long-range interactions between structural units and to handle low sequence similarities across proteins (under 25% identity). In this paper, we propose a chain graph model built on a causally connected series of segmentation conditional random fields (SCRFs) to address these issues. Specifically, the SCRF model captures long-range interactions within recurring structural units and the Bayesian network backbone decomposes cross-repeat interactions into locally computable modules consisting of repeat-specific SCRFs and a model for sequence motifs. We applied this model to predict β -helices and leucine-rich repeats, and found it significantly outperforms extant methods in predictive accuracy and/or computational efficiency.
Citation Information
Yan Liu, Eric P. Xing and Jaime G. Carbonell. "Predicting Protein Folds with Structural Repeats Using a Chain Graph Model" (2005)
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