Motions of Allosteric and Orthosteric Ligand-Binding Sites in Proteins are Highly Correlated.

Allostery is the phenomenon in which a ligand binding at one site affects other sites in the same macromolecule. Allostery has important roles in many biological processes. Theoretically, all nonfibrous proteins are potentially allosteric. However, few allosteric proteins have been validated, and the identification of novel allosteric sites remains a challenge. The motion of residues and subunits underlies protein function; therefore, we hypothesized that the motions of allosteric and orthosteric sites are correlated. We utilized a data set of 24 known allosteric sites from 23 monomer proteins to calculate the correlations between potential ligand-binding sites and corresponding orthosteric sites using a Gaussian network model (GNM). Most of the known allosteric site motions showed high correlations with corresponding orthosteric site motions, whereas other surface cavities did not. These high correlations were robust when using different structural data for the same protein, such as structures for the apo state and the orthosteric effector-binding state, whereas the contributions of different frequency modes to motion correlations depend on the given protein. The high correlations between allosteric and orthosteric site motions were also observed in oligomeric allosteric proteins. We applied motion correlation analysis to predict potential allosteric sites in the 23 monomer proteins, and some of these predictions were in good agreement with published experimental data. We also performed motion correlation analysis to identify a novel allosteric site in 15-lipoxygenase (an enzyme in the arachidonic acid metabolic network) using recently reported activating compounds. Our analysis correctly identified this novel allosteric site along with two other sites that are currently under experimental investigation. Our study demonstrates that the motions of allosteric sites are highly correlated with the motions of orthosteric sites. Our correlation analysis method provides new tools for predicting potential allosteric sites.