How accurate and statistically robust are catalytic site predictions based on closeness centrality?
BACKGROUND
We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid alpha-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices.


RESULTS
We benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.


CONCLUSIONS
Our results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.
Chea
Eric
E
Department of Biological Sciences, California State Polytechnic University, Pomona, CA 91768, USA. erc2009@med.cornell.edu <erc2009@med.cornell.edu>
Livesay
Dennis R
DR
D000595:Amino Acid Sequence / Q000235:genetics; D001665:Binding Sites / Q000235:genetics; D020134:Catalytic Domain / Q000235:genetics*; D016208:Databases, Factual / Q000706:statistics & numerical data; D015233:Models, Statistical; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary / Q000235:genetics; D017434:Protein Structure, Tertiary / Q000235:genetics; D012680:Sensitivity and Specificity