Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Methodology article

Prediction of the burial status of transmembrane residues of helical membrane proteins

Yungki Park, Sikander Hayat and Volkhard Helms*

Author Affiliations

Center for Bioinformatics, Saarland University, 66041 Saarbruecken, Germany

For all author emails, please log on.

BMC Bioinformatics 2007, 8:302  doi:10.1186/1471-2105-8-302

Published: 20 August 2007

Abstract

Background

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.

Results

We have developed TMX (TransMembrane eXposure), a novel method for predicting the burial status (i.e. buried in the protein structure vs. exposed to the membrane) of transmembrane (TM) residues of HMPs. TMX derives positional scores of TM residues based on their profiles and conservation indices. Then, a support vector classifier is used for predicting their burial status. Its prediction accuracy is 78.71% on a benchmark data set, representing considerable improvements over 68.67% and 71.06% of previously proposed methods. Importantly, unlike the previous methods, TMX automatically yields confidence scores for the predictions made. In addition, a feature selection incorporated in TMX reveals interesting insights into the structural organization of HMPs.

Conclusion

A novel computational method, TMX, has been developed for predicting the burial status of TM residues of HMPs. Its prediction accuracy is much higher than that of previously proposed methods. It will be useful in elucidating structural characteristics of HMPs as an inexpensive, auxiliary tool. A web server for TMX is established at http://service.bioinformatik.uni-saarland.de/tmx and freely available to academic users, along with the data set used.