Accuracy of Catalytic Site Predictions Using Closeness Centrality
Author Information
Author(s): Chea Eric, Livesay Dennis R
Primary Institution: California State Polytechnic University, Pomona; University of North Carolina at Charlotte
Hypothesis
How accurate and statistically robust are catalytic site predictions based on closeness centrality?
Conclusion
Closeness centrality is a viable prediction scheme for catalytic residues, and filtering methods significantly enhance its predictive power.
Supporting Evidence
- Closeness centrality predictions are statistically significant.
- Filtering by solvent accessibility improves predictive power.
- Filtering by residue identity further enhances prediction accuracy.
Takeaway
The study shows that a method called closeness centrality can help find important parts of proteins that do jobs, and using some simple filters makes it even better.
Methodology
The study benchmarks closeness centrality predictions against 283 unique proteins and uses filtering methods to improve accuracy.
Limitations
The study focuses on a specific dataset and may not generalize to all proteins or prediction methods.
Statistical Information
P-Value
2.7E-9 to less than 8.8E-134
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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