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Related Experiment Videos

A hidden Markov model for predicting transmembrane helices in protein sequences

E L Sonnhammer1, G von Heijne, A Krogh

  • 1National Center for Biotechnology Information, NLM/NIH, Bethesda, Maryland 20894, USA. esr@ncbi.nlm.nih.gov

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|October 23, 1998
PubMed
Summary

A new hidden Markov model (HMM) accurately predicts alpha helix locations in membrane proteins. This transmembrane HMM (TMHMM) improves understanding of membrane protein topology and mechanisms.

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Area of Science:

  • Computational biology
  • Structural biology
  • Bioinformatics

Background:

  • Membrane-spanning proteins contain alpha helices crucial for their function.
  • Predicting the location and orientation of these helices is essential for understanding protein structure and function.
  • Existing methods face challenges in accurately modeling complex membrane protein topologies.

Purpose of the Study:

  • To develop a novel hidden Markov model (HMM) for predicting alpha helix location and orientation in membrane proteins.
  • To create a computational model that closely mirrors the biological system of membrane proteins.
  • To enhance understanding of the mechanisms and constraints governing membrane protein topology.

Main Methods:

  • Development of a cyclic hidden Markov model (HMM) with biologically relevant states (helix core, caps, loops, globular domains).

Related Experiment Videos

  • Modeling distinct loop lengths on the non-cytoplasmic side to represent different membrane insertion mechanisms.
  • Estimation of model parameters using maximum likelihood and discriminative methods.
  • Development of a method for reassignment of membrane helix boundaries.

    • Main Results:

    • The transmembrane HMM (TMHMM) achieved 77% accuracy in predicting the complete topology of membrane proteins on a standard dataset.
    • Similar accuracy was maintained on a larger dataset of 160 proteins.
    • The model's architecture provided insights into important features for encoding membrane topology.

    Conclusions:

    • The developed transmembrane HMM (TMHMM) offers a significant improvement in predicting membrane protein topology.
    • The model's biological relevance facilitates a deeper understanding of membrane protein structure and function.
    • This approach provides a valuable tool for bioinformatics and structural biology research.