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An efficient computational method for globally optimal threading

Y Xu1, D Xu, E C Uberbacher

  • 1Computational Biosciences Section, Oak Ridge National Laboratory, Tennessee 37831-6480, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 17, 1998
PubMed
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This study introduces an efficient protein threading method for predicting three-dimensional protein structures. The new algorithm optimizes sequence-to-template alignment, improving computational protein fold recognition.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Protein structure prediction is crucial for understanding protein function.
  • Computational recognition of native-like folds is a key approach.
  • Protein threading aligns amino acid sequences with known fold templates.

Purpose of the Study:

  • To present a novel, efficient method for protein fold recognition using protein threading.
  • To improve the accuracy and speed of predicting three-dimensional protein structures.

Main Methods:

  • Developed a protein threading algorithm measuring alignment fitness via singleton fitness, pairwise interactions, and gap penalties.
  • Algorithm runs efficiently by applying a cutoff distance to pairwise interactions.

Related Experiment Videos

  • Analyzed alignment time and space complexity: O(Mn^1.5C+1 + mn^C+1) time and O(Mn^C+1) space.

    • Main Results:

    • The method demonstrates efficient computation, especially when pairwise interactions are limited.
    • Case studies show the parameter C <= 4 for ~75% of unique folds with interactions < 7 A.
    • An approximation scheme is provided for complex fold templates where C > 4.

    Conclusions:

    • The proposed protein threading method offers an efficient solution for computational protein fold recognition.
    • The algorithm's performance is practical for typical workstations, especially with optimizations.
    • This work advances the field of three-dimensional protein structure prediction.