Visualizing Ca2+-Binding Sites using Refined Oxygen and Carbon Clusters

Abstract

Identifying Ca2+-binding sites in proteins is the first step towards 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 in this paper a new graph theory algorithm (MUGC) to predict Ca2+-binding sites. Using second shell carbon atoms, and without explicit reference to side-chain oxygen ligand coordinates, MUGC is able to achieve 94% sensitivity with 76% selectivity on a dataset of X-ray structures comprised of 43 Ca2+-binding proteins. Additionally, prediction of Ca2+-binding sites in NMR structures were obtained by MUGC using a different set of parameters determined by analysis of both Ca2+-constrained and unconstrained Ca2+-loaded structures derived from NMR data. Based on these more inclusive values, MUGC identified 20 out of 21 Ca2+-binding sites in NMR structures inferred without the use of Ca2+ constraints. MUGC 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 for both accurate identification of Ca2+-binding sites in NMR and X-ray structures, and for selective differentiation between Ca2+ and other relevant divalent cations. This algorithm has been applied to channels and receptors and proven to be very accurate.