Multiple Geometries Carbon Oxygen Algorithm Python (MUGCOPy): calcium binding sites prediction platform

Algorithm Developed by Kun Zhao and Jenny J. Yang

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About MUGC:

Identifying Ca(2+) -binding sites in proteins is the first step toward understanding the molecular basis of diseases related to Ca(2+) -binding proteins. Currently, these sites are identified in structures either through X-ray crystallography or NMR analysis. However, Ca(2+) -binding sites are not always visible in X-ray structures due to flexibility in the binding region or low occupancy in a Ca(2+) -binding site. Similarly, both Ca(2+) and its ligand oxygens are not directly observed in NMR structures. To improve our ability to predict Ca(2+) -binding sites in both X-ray and NMR structures, we report a new graph theory algorithm (MUG(C) ) to predict Ca(2+) -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 Ca(2+) -binding proteins. Additionally, prediction of Ca(2+) -binding sites in NMR structures was obtained by MUG(C) using a different set of parameters, which were determined by the analysis of both Ca(2+) -constrained and unconstrained Ca(2+) -loaded structures derived from NMR data. MUG(C) identified 20 of 21 Ca(2+) -binding sites in NMR structures inferred without the use of Ca(2+) constraints. MUG(C) predictions are also highly selective for Ca(2+) -binding sites as analyses of binding sites for Mg(2+) , Zn(2+) , and Pb(2+) were not identified as Ca(2+) -binding sites. These results indicate that the geometric arrangement of the second-shell carbon cluster is sufficient not only for accurate identification of Ca(2+) -binding sites in NMR and X-ray structures but also for selective differentiation between Ca(2+) and other relevant divalent cations.

Please cite:
Zhao K et al, Predicting Ca2+ -binding sites using refined carbon clusters. Proteins. 2012 Dec;80(12):2666-79.