Predicting Ca2+‐binding sites using refined carbon clusters

Identifying Ca²⁺‐binding sites in proteins is the first step toward understanding the molecular basis of diseases related to Ca²⁺‐binding proteins. Currently, these sites are identified in structures either through X‐ray crystallography or NMR analysis. However, Ca²⁺‐binding sites are not always visible in X‐ray structures due to flexibility in the binding region or low occupancy in a Ca²⁺‐binding site. Similarly, both Ca²⁺ and its ligand oxygens are not directly observed in NMR structures. To improve our ability to predict Ca²⁺‐binding sites in both X‐ray and NMR structures, we report a new graph theory algorithm (MUG^C) to predict Ca²⁺‐binding sites. Using carbon atoms covalently bonded to the chelating oxygen atoms, and without explicit reference to side‐chain oxygen ligand co‐ordinates, MUG^C is able to achieve 94% sensitivity with 76% selectivity on a dataset of X‐ray structures composed of 43 Ca²⁺‐binding proteins. Additionally, prediction of Ca²⁺‐binding sites in NMR structures was obtained by MUG^C using a different set of parameters, which were determined by the analysis of both Ca²⁺‐constrained and unconstrained Ca²⁺‐loaded structures derived from NMR data. MUG^C identified 20 of 21 Ca²⁺‐binding sites in NMR structures inferred without the use of Ca²⁺ constraints. MUG^C predictions are also highly selective for Ca²⁺‐binding sites as analyses of binding sites for Mg²⁺, Zn²⁺, and Pb²⁺ were not identified as Ca²⁺‐binding sites. These results indicate that the geometric arrangement of the second‐shell carbon cluster is sufficient not only for accurate identification of Ca²⁺‐binding sites in NMR and X‐ray structures but also for selective differentiation between Ca²⁺ and other relevant divalent cations. © Proteins 2012. © 2012 Wiley Periodicals, Inc.