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Introduction of a distance cut-off into structural alignment by the double dynamic programming algorithm

H Toh1

  • 1Biomolecular Engineering Research Institute, Osaka, Japan. toh@beri.co.jp

Computer Applications in the Biosciences : CABIOS
|August 1, 1997
PubMed
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Structural alignment computational time was reduced using distance cut-off and delta N cut-off approximations in dynamic programming. A two-step algorithm improved accuracy by refining initial alignments within an epsilon-suboptimal region.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural bioinformatics

Background:

  • Protein structural alignment is crucial for understanding biological function and evolution.
  • Traditional dynamic programming algorithms for structural alignment are computationally intensive.
  • Approximations are needed to accelerate these algorithms for large-scale analyses.

Purpose of the Study:

  • To reduce the computational time of the double dynamic programming algorithm for structural alignment.
  • To maintain or improve the accuracy of structural alignments despite computational speed-ups.

Main Methods:

  • Introduced two approximations: distance cut-off and delta N cut-off.
  • Distance cut-off approximates local residue environments using a spherical radius.

Related Experiment Videos

  • Delta N cut-off skips similarity evaluation if residue counts in local environments differ significantly.
  • Developed a two-step alignment strategy: initial rough alignment followed by refinement in an epsilon-suboptimal region using the full algorithm.

    • Main Results:

    • The approximations significantly reduced computational time for structural alignment.
    • However, these approximations led to a decrease in alignment accuracy.
    • The two-step algorithm successfully improved the accuracy of the alignments obtained with approximations.

    Conclusions:

    • The distance cut-off and delta N cut-off are effective in accelerating structural alignment algorithms.
    • A hybrid approach combining approximations with a targeted full computation can restore and improve alignment accuracy.
    • This optimized approach balances computational efficiency and accuracy in structural bioinformatics.