Syntactic approach to predict membrane spanning regions of transmembrane proteins

Authors:
Koliya Pulasinghe;Jagath C. Rajapakse
Affiliations:
Sri Lanka Institute of Information Technology, Sri Lanka;BioInformatics Research Centre, Nanyang Technological University, Singapore
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

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A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences

ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology

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Abstract

This paper exploits “biological grammar” of transmembrane proteins to predict their membrane spanning regions using hidden Markov models and elaborates a set of syntactic rules to model the distinct features of transmembrane proteins. This paves the way to identify the characteristics of membrane proteins analogous to the way that identifies language contents of speech utterances by using hidden Markov models. The proposed method correctly predicts 95.24% of the membrane spanning regions of the known transmembrane proteins and correctly predicts 79.87% of the membrane spanning regions of the unknown transmembrane proteins on a benchmark dataset.