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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules
Thomas Fober1, Marco Mernberger, Gerhard Klebe
1Department of Mathematics and Computer Science, Philipps-Universität Marburg, Marburg, Germany.
Bioinformatics (Oxford, England)
|March 17, 2009
Summary
Multiple graph alignment (MGA) identifies conserved patterns in biomolecules without sequence data. Evolutionary algorithms outperform heuristic methods for optimal structural alignments, though with longer runtimes.
Area of Science:
- Structural bioinformatics
- Computational biology
- Bioinformatics
Background:
- Multiple graph alignment (MGA) is a novel method for biomolecular structural analysis.
- It utilizes approximate graph matching to identify conserved patterns in related structures.
- MGA can characterize functional protein families irrespective of sequence or fold homology.
Purpose of the Study:
- To present the concept of MGA.
- To address the algorithmic challenges of computing optimal alignments.
- To propose and evaluate an evolutionary algorithm-based method for MGA.
Main Methods:
- Recalls the concept of Multiple Graph Alignment (MGA).
- Proposes an evolutionary algorithm-based approach for computing optimal alignments.
- Compares the proposed method with a heuristic approach.
Main Results:
- The evolutionary algorithm approach yields significantly better results than the heuristic method.
- The improved performance comes at the cost of increased computational runtime.
- Demonstrates the effectiveness of MGA for structural analysis.
Conclusions:
- Evolutionary algorithms offer a superior approach for optimal multiple graph alignment.
- MGA is a powerful tool for characterizing protein families based on structure.
- Further research may focus on optimizing runtime for evolutionary MGA methods.
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