Predicting the &bgr;-helix fold from protein sequence data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A graphical model for protein secondary structure prediction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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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.