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 α-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

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Eric Chea, Dennis R. Livesay
• Keywords: BMCBI 2007, Catalytic Residue Predictions, Closeness Centrality, Closeness Centrality Predictions