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Predicting protein structure using hidden Markov models

K Karplus1, K Sjölander, C Barrett

  • 1Department, Jack Baskin School of Engineering, University of California, Santa Cruz 95064, USA. karplus@cse.ucsc.edu

Proteins
|January 1, 1997
PubMed
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Hidden Markov models (HMMs) showed strong performance in protein fold recognition during the CASP2 experiment. This method accurately identified homologous protein structures, particularly for moderately difficult targets.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Bioinformatics

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • The Critical Assessment of protein Structure Prediction (CASP) experiment evaluates prediction methods.
  • Hidden Markov models (HMMs) are probabilistic models used in sequence analysis.

Purpose of the Study:

  • To evaluate the performance of HMM-based methods in the CASP2 fold recognition category.
  • To assess the effectiveness of HMMs for identifying homologous protein structures.

Main Methods:

  • Generated HMMs from a dataset of over 1,000 Protein Data Bank (PDB) structures.
  • Scored CASP2 target sequences against the HMM library.
  • Built HMMs for target sequences and scored against the PDB database.

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Main Results:

  • HMM methods demonstrated good performance in the CASP2 fold recognition task.
  • The approach was effective for targets with at least 15% residue identity to known structures.
  • Successful identification of homologous structures was achieved through a dual scoring approach.

Conclusions:

  • HMM-based approaches are effective for protein fold recognition, especially for moderately challenging targets.
  • The study validates the utility of HMMs in structural bioinformatics and protein structure prediction.
  • This work contributes to advancing computational methods for protein structure analysis.