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Related Experiment Videos

Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures.

Vadim Alexandrov1, Mark Gerstein

  • 1Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06511, USA. vadim.alexandrov@yale.edu

BMC Bioinformatics
|January 13, 2004
PubMed
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This study introduces three-dimensional Hidden Markov Models (3D HMMs) for protein structure analysis, extending their use beyond 1D sequences. These 3D HMMs enable probabilistic alignment and classification of protein structures.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Bioinformatics

Background:

  • Hidden Markov Models (HMMs) are widely used in computational biology for sequence analysis.
  • Current HMMs are limited to representing one-dimensional sequences, not three-dimensional structures.

Purpose of the Study:

  • To extend Hidden Markov Models (HMMs) to explicitly incorporate three-dimensional (3D) structural information.
  • To develop methods for aligning and scoring protein structures using 3D HMMs.
  • To apply 3D HMMs for protein structure classification.

Main Methods:

  • Developed a 3D HMM formalism using 3D Gaussian distributions for match states based on alpha carbon coordinates.
  • Modeled transition probabilities based on the spread of neighboring states and gaps in structural alignments.

Related Experiment Videos

  • Extended Viterbi and forward algorithms for probabilistic alignment and scoring of query structures against 3D HMMs.

    • Main Results:

    • Successfully applied 3D HMMs to protein structure classification.
    • Demonstrated effective separation of scores for different protein fold families.
    • Indicated the utility of 3D HMMs for detailed protein structure analysis.

    Conclusions:

    • Created a robust 3D HMM representation for protein structures.
    • Implemented C and Perl routines for building 3D HMMs.
    • Provided freely available code and a prototype server for accessing the 3D HMM approach.