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Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based

Jiang Zhu1, Li Xie, Barry Honig

  • 1Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, 1130 St. Nicholas Avenue, Room 815, New York, New York 10032, USA.

Proteins
|August 24, 2006
PubMed
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We developed an iterative, modular optimization (IMO) protocol for refining protein structures, outperforming traditional methods. This new approach enhances protein structure prediction accuracy for segments with secondary structure elements (SSEs).

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Accurate protein structure refinement is crucial for understanding biological function.
  • Existing methods for local structure refinement of protein segments, especially those with secondary structure elements (SSEs), have limitations.

Purpose of the Study:

  • To present a novel iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing SSEs.
  • To evaluate the effectiveness of different algorithmic modules and potentials within the IMO protocol.
  • To compare the IMO protocol against traditional refinement methods like energy minimization and molecular dynamics.

Main Methods:

  • The IMO protocol integrates a torsion-space local sampling algorithm, a knowledge-based potential (DFIRE), and a conformational clustering algorithm.

Related Experiment Videos

  • Initial conformations were generated by perturbing dihedral angles of protein segments.
  • Alternative methods for each module were tested, and results were compared with molecular mechanics-based refinement procedures.

    • Main Results:

    • The IMO protocol demonstrated superior performance in local structure refinement compared to energy minimization and molecular dynamics.
    • The DFIRE knowledge-based potential was identified as highly effective.
    • Clustering algorithms biased by DFIRE energies significantly improved results.
    • A hybrid strategy combining IMO with an energy minimization step further enhanced accuracy.

    Conclusions:

    • The iterative, modular optimization (IMO) protocol offers an effective approach for local protein structure refinement.
    • Knowledge-based potentials, particularly DFIRE, play a key role in improving refinement accuracy.
    • Hybrid strategies combining knowledge-based and physical energy functions show promise for future protein structure prediction applications.