Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 16, Issue 5, Pages 639-657Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2008.0176
Keywords
conditional random fields; graphical models; protein structure prediction
Categories
Funding
- National Science Foundation [0225656]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0225656] Funding Source: National Science Foundation
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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.
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