Conditional Graphical Models for Protein Structural Motif Recognition
Name
Liu-2009-Conditional Graphica.pdf
Size
573.75 KB
Format
Adobe PDF
Checksum (MD5)
7e489631666429145ba1551a1157717f
Author(s)
Liu, Yan
Carbonell, Jaime
Gopalakrishnan, Vanathi
Weigele, Peter
Date Issued
May 2009
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1089/cmb.2008.0176
