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Prediction of viable circular permutants using a graph theoretic approach
Konrad H Paszkiewicz1, Michael J E Sternberg, Michael Lappe
1Structural Bioinformatics Group, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. konrad.paszkiewicz@imperial.ac.uk
Graph theory measures, like closeness, can predict viable protein circular permutation sites. This method outperforms solvent accessibility for designing split enzymes and understanding protein folding.
Area of Science:
- Computational Biology
- Structural Biology
- Graph Theory
Background:
- Graph-theoretic approaches are increasingly used for complex biological systems analysis.
- Application of graph theory to protein structures and residue interactions shows promise.
- Circular permutants are valuable for studying protein folding and designing split enzymes.
Purpose of the Study:
- To evaluate the graph measure 'closeness' for predicting suitable protein regions for circular permutation.
- To compare the predictive power of closeness with solvent accessibility for identifying circular permutation sites.
Main Methods:
- Utilized graph theory to analyze protein structures and residue interaction networks.
- Applied the 'closeness' centrality measure to identify potential circular permutation sites.
- Tested the method on extensive experimental data from dihydrofolate reductase and other proteins.
Main Results:
- The 'closeness' measure significantly outperformed solvent accessibility in identifying residues suitable for circular permutation.
- Successfully predicted viable circular permutation sites across various protein positions.
- Demonstrated the efficacy of graph-theoretic measures in capturing residue interactions relevant to protein folding.
Conclusions:
- Graph-theoretic 'closeness' is a powerful predictor for designing viable circular protein variants.
- This approach has significant implications for developing split enzyme reporter systems and protein-protein interaction screening.
- The study highlights the utility of graph theory in understanding protein folding dynamics and residue interactions.
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