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Bioinformatics Advance Access originally published online on February 25, 2009
Bioinformatics 2009 25(8):996-1003; doi:10.1093/bioinformatics/btp114
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Predicting helix–helix interactions from residue contacts in membrane proteins

Allan Lo 1,2, Yi-Yuan Chiu 3, Einar Andreas Rødland 4,5, Ping-Chiang Lyu 2, Ting-Yi Sung 3,* and Wen-Lian Hsu 3,*

1Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 2Institute of Bioinformatics and Structural Biology, Department of Life Sciences, National Tsing Hua University, Hsinchu, 3Bioinformatics Laboratory, Institute of Information Science, Academia Sinica, Taipei, Taiwan, 4Centre for Cancer Biomedicine, University of Oslo, NO-0027 Oslo and 5SAMBA, Norwegian Computing Center, P.O. Box 114 Blindern, NO-0314 Oslo, Norway

*To whom correspondence should be addressed.


  Abstract

Motivation: Helix–helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction.

Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins.

Availability: http://bio-cluster.iis.sinica.edu.tw/TMhit/

Contact: tsung{at}iis.sinica.edu.tw

Supplementary information:Supplementary data are available at Bioinformatics online.

Associate Editor: Ivo Hofacker

Received on August 26, 2008; revised on February 20, 2009; accepted on February 23, 2009

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