title: Towards predicting Ca2+-binding sites with different coordination numbers in proteins with atomic resolution.
abstract: Ca(2+)-binding sites in proteins exhibit a wide range of polygonal geometries that directly relate to an equally-diverse set of biological functions. Although the highly-conserved EF-Hand motif has been studied extensively, non-EF-Hand sites exhibit much more structural diversity which has inhibited efforts to determine the precise location of Ca(2+)-binding sites, especially for sites with few coordinating ligands. Previously, we established an algorithm capable of predicting Ca(2+)-binding sites using graph theory to identify oxygen clusters comprised of four atoms lying on a sphere of specified radius, the center of which was the predicted calcium position. Here we describe a new algorithm, MUG (MUltiple Geometries), which predicts Ca(2+)-binding sites in proteins with atomic resolution. After first identifying all the possible oxygen clusters by finding maximal cliques, a calcium center (CC) for each cluster, corresponding to the potential Ca(2+) position, is located to maximally regularize the structure of the (cluster, CC) pair. The structure is then inspected by geometric filters. An unqualified (cluster, CC) pair is further handled by recursively removing oxygen atoms and relocating the CC until its structure is either qualified or contains fewer than four ligand atoms. Ligand coordination is then determined for qualified structures. This algorithm, which predicts both Ca(2+) positions and ligand groups, has been shown to successfully predict over 90% of the documented Ca(2+)-binding sites in three datasets of highly-diversified protein structures with 0.22 to 0.49 A accuracy. All multiple-binding sites (i.e. sites with a single ligand atom associated with multiple calcium ions) were predicted, as were half of the low-coordination sites (i.e. sites with less than four protein ligand atoms) and 14/16 cofactor-coordinating sites. Additionally, this algorithm has the flexibility to incorporate surface water molecules and protein cofactors to further improve the prediction for low-coordination and cofactor-coordinating Ca(2+)-binding sites.
authors.affiliation: Department of Computer Science, Georgia State University, Atlanta, Georgia 30303, USA.
authors.forename: Xue
authors.initials: X
authors.lastname: Wang
authors.forename: Michael
authors.initials: M
authors.lastname: Kirberger
authors.forename: Fasheng
authors.initials: F
authors.lastname: Qiu
authors.forename: Guantao
authors.initials: G
authors.lastname: Chen
authors.forename: Jenny J
authors.initials: JJ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites; D002118:Calcium / Q000737:chemistry / Q000378:metabolism; D002135:Calcium-Binding Proteins / Q000737:chemistry* / Q000378:metabolism*; D002413:Cations, Divalent / Q000737:chemistry; D018360:Crystallography, X-Ray; D008024:Ligands; D008956:Models, Chemical; D008958:Models, Molecular; D010100:Oxygen / Q000737:chemistry / Q000378:metabolism; D017434:Protein Structure, Tertiary
chemical_list: D002135:Calcium-Binding Proteins; D002413:Cations, Divalent; D008024:Ligands; D010100:Oxygen; D002118:Calcium