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A multi-objective evolutionary approach to the protein structure prediction problem.

Vincenzo Cutello1, Giuseppe Narzisi, Giuseppe Nicosia

  • 1University of Catania, Department of Mathematics and Computer Science, V. le A. Doria 6, 95125 Catania, Italy.

Journal of the Royal Society, Interface
|July 20, 2006
PubMed
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Protein structure prediction faces challenges due to conflicting local and non-local atomic interactions. This study introduces a multi-objective approach using evolutionary algorithms to explore protein conformations effectively.

Area of Science:

  • Computational Biology
  • Biophysics
  • Structural Biology

Background:

  • Protein structure prediction (PSP) aims to determine a protein's native tertiary structure from its amino acid sequence.
  • This remains a computationally challenging problem despite extensive research.
  • Current methods often rely on potential energy functions evaluating local and non-local atomic interactions.

Purpose of the Study:

  • To investigate the conflict between local and non-local atomic interactions in protein structure prediction.
  • To introduce and evaluate a multi-objective formulation for the PSP problem.
  • To explore the conformational space of proteins using advanced computational methods.

Main Methods:

  • Experimental validation of conflicting interaction energies using the "Chemistry at HARvard Macromolecular Mechanics" (CHARMM) potential energy function.

Related Experiment Videos

  • Formulation of the protein structure prediction problem as a multi-objective optimization problem.
  • Application of a multi-objective evolutionary algorithm to search the protein conformational space.

    • Main Results:

    • Demonstrated experimentally that local and non-local atomic interactions are in conflict.
    • Successfully applied a multi-objective formulation to the PSP problem.
    • Utilized a multi-objective evolutionary algorithm for efficient conformational space exploration.

    Conclusions:

    • The conflict between local and non-local interactions presents a significant challenge in protein structure prediction.
    • A multi-objective approach offers a promising framework for addressing this challenge.
    • Multi-objective evolutionary algorithms are effective tools for navigating the complex protein conformational landscape.