Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

Protein Sci. 2004 Jul;13(7):1908-17. doi: 10.1110/ps.04625404.


Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Humans
  • Markov Chains*
  • Membrane Proteins / chemistry*
  • Predictive Value of Tests
  • Protein Structure, Secondary
  • Protein Structure, Tertiary
  • Sequence Alignment / methods*


  • Membrane Proteins