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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Published on: July 14, 2015

Flexible structural protein alignment by a sequence of local transformations.

Jairo Rocha1, Joan Segura, Richard C Wilson

  • 1Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, Spain. jairo@uib.es

Bioinformatics (Oxford, England)
|May 7, 2009
PubMed
Summary

This study introduces a flexible protein structure alignment method that handles local deformations, outperforming existing tools in alignment quality and classification. The approach enables more accurate comparisons of homologous proteins with varying structures.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure analysis

Background:

  • Homologous proteins exhibit conserved rigid regions and flexible variable regions.
  • The Protein Data Bank (PDB) contains a growing number of protein structures.
  • Flexible geometrical alignments are crucial for comparing diverse protein structures.

Purpose of the Study:

  • To develop a reliable and user-friendly method for flexible protein structure alignment.
  • To address the limitations of global rigid transformations in comparing protein structures.

Main Methods:

  • A novel protein structure alignment method is presented.
  • The method allows for different rigid transformations at various sites, accommodating local deformations.
  • It moves beyond a single global rigid transformation for alignment.

Main Results:

  • The method's performance is comparable to top-tier aligners.
  • Achieves high quality alignments against curated datasets.
  • Demonstrates strong protein classification capabilities.
  • Analyzes challenging structure pairs requiring flexible matching.

Conclusions:

  • The developed method offers a significant advancement in flexible protein structure alignment.
  • Local transformations can enhance other classification algorithms.
  • This approach has potential for future protein similarity measures.