Predicting protein folds with structural repeats using a chain graph model

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 longrange 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 -helices and leucine-rich repeats, and found it significantly outp...

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Yan Liu, Eric P. Xing, Jaime G. Carbonell
• Keywords: Captures Long-range Interactions, Chain Graph Model, ICML 2005, Machine Learning, Structural Repeats