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1001 optimal PDB structure alignments: integer programming methods for finding the maximum contact map overlap.

Alberto Caprara1, Robert Carr, Sorin Istrail

  • 1D.E.I.S., Università di Bologna, Viale Risorgimento, 2 40136 Bologna, Italy.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 10, 2004
PubMed
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We present contact map overlap (CMO), a novel measure for protein structure comparison. This method accurately compares large proteins and clusters them into families, validating against SCOP classification.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Genomics

Background:

  • Protein structure comparison is crucial for structural genomics, drug design, and evolutionary studies.
  • Existing methods lack rigorous similarity measures.
  • Contact map overlap (CMO) is an emerging measure for protein structure comparison.

Purpose of the Study:

  • To develop and validate the contact map overlap (CMO) measure for accurate protein structure comparison.
  • To demonstrate the computational feasibility and effectiveness of CMO for large proteins.
  • To cluster proteins into families using CMO and validate against established classifications.

Main Methods:

  • Integer linear programming techniques to compute CMO accurately.
  • Heuristics including local search and genetic algorithms.

Related Experiment Videos

  • Comparison of CMO-derived clusters with SCOP classification.

    • Main Results:

    • Optimal alignments for large proteins (approx. 1,000 residues) computed for the first time.
    • Accurate CMO computation achieved using integer linear programming.
    • Near-optimal alignments (within 10% of optimal) computed rapidly.
    • Validated protein clusters using SCOP classification.

    Conclusions:

    • CMO is a robust and accurate measure for protein structure comparison.
    • Integer linear programming and heuristics enable efficient computation of CMO.
    • CMO effectively clusters proteins into biologically relevant families.