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Syntactic Approach to Predict Membrane Spanning Regions of Transmembrane Proteins

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Applications of Evolutionary Computing (EvoWorkshops 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3449))

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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.

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Author information

Authors and Affiliations

  1. Sri Lanka Institute of Information Technology, Sri Lanka

    Koliya Pulasinghe

  2. BioInformatics Research Centre, Nanyang Technological University, Singapore

    Jagath C. Rajapakse

Authors
  1. Koliya Pulasinghe
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  2. Jagath C. Rajapakse
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  8. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  9. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  10. School of Computing Sciences, University of East Anglia, UEA Norwich, NR4 7TJ, Norwich, UK

    George D. Smith

  11. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2005 Springer-Verlag Berlin Heidelberg

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Pulasinghe, K., Rajapakse, J.C. (2005). Syntactic Approach to Predict Membrane Spanning Regions of Transmembrane Proteins. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_10

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  • DOI: https://doi.org/10.1007/978-3-540-32003-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25396-9

  • Online ISBN: 978-3-540-32003-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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