How accurate and statistically robust are catalytic site predictions based on closeness centrality?

Abstract

BackgroundWe 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.ResultsWe 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.ConclusionOur 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.

Revealing unique properties of the ribosome using a network based analysis

“…The distribution of the three centrality measures of all nucleotides in the 23S rRNA network (1JJ2) is presented in Figure 1. As previously observed in protein structures (17,26), the closeness parameter in the rRNA ribosome network showed a bell-shaped distribution (Figure 1A). Additionally, in accordance with previous observations in protein structures (19,25,54), the degree parameter of the rRNA structure follows a Poisson distribution (Figure 1C).…”

Computational Studies of Allosteric Regulation in the Hsp90 Molecular Chaperone: From Functional Dynamics and Protein Structure Networks to Allosteric Communications and Targeted Anti‐Cancer Modulators

“…[44,45] Statistical analyses and network-based approaches have revealed a smallworld organization of protein topologies that emerged in studies of protein-folding mechanisms, [46][47][48] predictions of the binding-site residues, [49][50][51] analyses of functional and regulatory sites in protein networks, [52,53] and the prediction of catalytic clusters in enzymes. [51,54] Small-world allosteric networks and long-range communications in proteins appeared to be primarily governed by spatially separated clusters of specific functional residues. [55][56][57] These studies suggested that rapid transmission of allosteric interactions through small-world networks encoded in protein folds may have been a universal requirement encoded across diverse protein families.…”

Relating Protein Structure and Function Through a Bijection and Its Implications on Protein Structure Prediction

“…Centrality is reliable when it comes to predict critical residues (Chea & Livesay, 2007), but how can these be used to predict 3D structural and functional features? We have recently reported a tool named "JAMMING" to facilitate this task.…”