Conditional Graphical Models for Protein Structural Motif Recognition
Author(s)
Liu, Yan; Carbonell, Jaime; Gopalakrishnan, Vanathi; Weigele, Peter
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Determining protein structures is crucial to understanding the mechanisms of infection and designing drugs. However, the elucidation of protein folds by crystallographic experiments can be a bottleneck in the development process. In this article, we present a probabilistic graphical model framework, conditional graphical models, for predicting protein structural motifs. It represents the structure characteristics of a structural motif using a graph, where the nodes denote the secondary structure elements, and the edges indicate the side-chain interactions between the components either within one protein chain or between chains. Then the model defines the optimal segmentation of a protein sequence against the graph by maximizing its "conditional" probability so that it can take advantages of the discriminative training approach. Efficient approximate inference algorithms using reversible jump Markov Chain Monte Carlo (MCMC) algorithm are developed to handle the resulting complex graphical models. We test our algorithm on four important structural motifs, and our method outperforms other state-of-art algorithms for motif recognition. We also hypothesize potential membership proteins of target folds from Swiss-Prot, which further supports the evolutionary hypothesis about viral folds.
Date issued
2009-05Department
Massachusetts Institute of Technology. Department of BiologyJournal
Journal of Computational Biology
Publisher
Mary Ann Liebert, Inc.
Citation
Liu, Yan et al. “Conditional Graphical Models for Protein Structural Motif Recognition.” Journal of Computational Biology 16.5 (2009) : 639-657. © 2009 Mary Ann Liebert, Inc.
Version: Final published version
ISSN
1066-5277
1557-8666