Mapping Protein Structure and Function
Author Information
Author(s): Kshitiz Gupta, Vivek Sehgal, Andre Levchenko
Primary Institution: Johns Hopkins School of Medicine
Hypothesis
Can probabilistic mapping between protein structure and function taxonomies improve classification accuracy?
Conclusion
The study shows that probabilistic relationships between protein classification databases can enhance classification accuracy.
Supporting Evidence
- The study found significant semantic overlap between SCOP and PROSITE classifications.
- Cross training improved the accuracy of classifiers by an average of 5.2%.
- New features like blocks and 2-D elastic profiles reduced time complexity without compromising performance.
Takeaway
This study helps scientists understand how the shape of a protein can tell us what it does, and vice versa.
Methodology
The study used hierarchical cross training of Support Vector Machine classifiers to establish relationships between SCOP and PROSITE databases.
Potential Biases
The method may reflect biases inherent in the taxonomies used, which could affect the results.
Limitations
The study was conducted on partial taxonomies and may not represent the full complexity of protein classification.
Digital Object Identifier (DOI)
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