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Comparative protein structure modeling by iterative alignment, model building and model assessment.

Bino John1, Andrej Sali

  • 1Laboratory of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, New York, NY 10021, USA.

Nucleic Acids Research
|July 11, 2003
PubMed
Summary
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This study introduces an automated genetic algorithm to improve protein structure modeling by optimizing sequence alignments and models. The method enhances alignment accuracy and resulting model quality for challenging targets.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Bioinformatics

Background:

  • Protein structure modeling is crucial for understanding protein function.
  • Accurate sequence alignment is a major limitation in homology modeling.
  • Existing methods struggle with low sequence identity targets.

Purpose of the Study:

  • To develop an automated method for optimizing both sequence alignment and protein structure models.
  • To improve the accuracy of comparative protein structure modeling, especially for low sequence identity targets.

Main Methods:

  • A genetic algorithm protocol was employed for iterative optimization.
  • The process involved alignment generation, comparative model building using MODELLER, and model assessment.
  • Spatial restraints and an atomic statistical potential were utilized in model evaluation.

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Main Results:

  • The automated method improved average alignment accuracy from 37% to 45% for difficult targets.
  • Average model accuracy increased from 43% to 54% for the tested set.
  • The new method demonstrated superior performance compared to PSI-BLAST and SAM.

Conclusions:

  • The developed genetic algorithm approach effectively enhances protein structure modeling accuracy.
  • This method offers a significant improvement for homology modeling, particularly when sequence identity is low.
  • Further improvements in model ranking could yield even greater accuracy gains.