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To better understand the biological significance of Ca(2+), we report a comprehensive statistical analysis of calcium-binding proteins from the Protein Data Bank to identify structural parameters associated with EF-hand and non-EF-hand Ca(2+)-binding sites. Comparatively, non-EF-hand sites utilize lower coordination numbers (6 +/- 2 vs. 7 +/- 1), fewer protein ligands (4 +/- 2 vs. 6 +/- 1), and more water ligands (2 +/- 2 vs. 1 +/- 0) than EF-hand sites. The orders of ligand preference for non-EF-hand and EF-hand sites, respectively, were H(2)O (33.1%) > side-chain Asp (24.5%) > main-chain carbonyl (23.9%) > side-chain Glu (10.4%), and side-chain Asp (29.7%) > side-chain Glu (26.6%) > main-chain carbonyl (21.4%) > H(2)O (13.3%). Less formal negative charge was observed in the non-EF-hand than in the EF-hand binding sites (1 +/- 1 vs. 3 +/- 1). Additionally, over 20% of non-EF-hand sites had formal charge values of zero due to increased utilization of water and carbonyl oxygen ligands. Moreover, the EF-hand sites presented a narrower range of ligand distances and bond angles than non-EF-hand sites, possibly owing to the highly conserved helix-loop-helix motif. Significant differences between ligand types (carbonyl, side chain, bidentate) demonstrated that angles associated with each type must be classified separately, and the EF-hand side-chain Ca-O-C angles exhibited an unusual bimodal quality consistent with an Asp distribution that differed from the Gaussian model observed for non-EF-hand proteins. The results of this survey more accurately describe differences between EF-hand and non-EF-hand proteins and provide new parameters for the prediction and design of different classes of Ca(2+)-binding proteins.

Identifying calcium-binding sites in proteins is one of the first steps towards predicting and understanding the role of calcium in biological systems for protein structure and function studies. Due to the complexity and irregularity of calcium-binding sites, a fast and accurate method for predicting and identifying calcium-binding protein is needed. Here we report our development of a new fast algorithm (GG) to detect calcium-binding sites. The GG algorithm uses a graph theory algorithm to find oxygen clusters of the protein and a geometric algorithm to identify the center of these clusters. A cluster of four or more oxygen atoms has a high potential for calcium binding. High performance with about 90% site sensitivity and 80% site selectivity has been obtained for three datasets containing a total of 123 proteins. The results suggest that a sphere of a certain size with four or more oxygen atoms on the surface and without other atoms inside is necessary and sufficient for quickly identifying the majority of the calcium-binding sites with high accuracy. Our finding opens a new avenue to visualize and analyze calcium-binding sites in proteins facilitating the prediction of functions from structural genomic information.