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Using iterative dynamic programming to obtain accurate pairwise and multiple alignments of protein structures

M Gerstein1, M Levitt

  • 1Department of Structural Biology, Stanford University CA 94305, USA. mbg@hyper.stanford.edu

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1996
PubMed
Summary

This study enhances structural alignment accuracy using sequence alignment techniques like variable gap penalties and consensus of suboptimal alignments. This novel approach improves the alignment of conserved structural regions and extends to multiple structure alignment.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Bioinformatics

Background:

  • Accurate structural alignment is crucial for understanding protein function and evolution.
  • Existing structural alignment methods often lack the sophistication of sequence alignment techniques.
  • Improving alignment accuracy requires incorporating structural features into the alignment process.

Purpose of the Study:

  • To enhance the accuracy of pairwise structural alignment procedures.
  • To adapt sequence alignment improvements, such as position-dependent gap penalties and consensus methods, for structural alignment.
  • To develop a generalized method for multiple structure alignment.

Main Methods:

  • Modified a basic pairwise alignment procedure.

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  • Incorporated variable, position-dependent gap penalties based on secondary structure.
  • Utilized the consensus of suboptimal alignments.
  • Developed a median structure approach for multiple structure alignment.

    • Main Results:

    • Achieved more accurate alignment of conserved structural regions.
    • Demonstrated the feasibility of applying sequence alignment techniques to structural alignment.
    • Presented preliminary results for generalizing the procedure to multiple structure alignment.

    Conclusions:

    • The enhanced structural alignment procedure offers improved accuracy for conserved regions.
    • The method's similarity to sequence alignment facilitates novel improvements.
    • The approach shows promise for multiple structure alignment of protein families.