A less-biased analysis of metalloproteins reveals novel zinc coordination geometries

Abstract

Zinc metalloproteins are involved in many biological processes and play crucial biochemical roles across all domains of life. Local structure around the zinc ion, especially the coordination geometry (CG), is dictated by the protein sequence and is often directly related to the function of the protein. Current methodologies in characterizing zinc metalloproteins' CG consider only previously reported CG models based mainly on nonbiological chemical context. Exceptions to these canonical CG models are either misclassified or discarded as "outliers." Thus, we developed a less-biased method that directly handles potential exceptions without pre-assuming any CG model. Our study shows that numerous exceptions could actually be further classified and that new CG models are needed to characterize them. Also, these new CG models are cross-validated by strong correlation between independent structural and functional annotation distance metrics, which is partially lost if these new CGs models are ignored. Furthermore, these new CG models exhibit functional propensities distinct from the canonical CG models.

Keywords: 3D structure; bidentation; carboxylate shift; compressed angle; structural bioinformatics; structure-function relationship.

Figure 1

Three major (in red) and 10 minor canonical CGs of zinc metalloproteins. Magenta balls represent zinc ion, and white balls represent coordination ligands. The abbreviations and number of ligands are in parenthesis. From the lower left to the upper right, the CGs are separated by the lines with six, five, four, and three ligands, respectively.

Figure 2

Histogram of minimum angles with respect to: (A) the number of ligands in the zinc fc‐shells and (B) ligand type for four‐ligand zinc fc‐shells. aa represents standard amino acid, nonaa represents nonstandard amino acid or any substrates from the protein, and bi represents bidentation.

Figure 3

Workflow of the less‐biased analysis for novel CG detection.

Figure 4

Flowchart for iterative algorithm (IA) of mixture canonical CG models.

Figure 5

Workflow for functional validation.

Figure 6

Four most prevalent zinc bidentation of standard amino acids in the wwPDB, with real structures on the top panel and schematic structures on the bottom. Panel A: Glutamate bidentates the zinc ion via two side chain oxygens. Count: 935; percentage: 33.7%. Example shown: PDB ID, 2E4T. Panel B: Aspartate bidentates the zinc ion via two side chain oxygens. Count: 935; percentage: 28.7%. PDB ID: 1RTQ. Panel C: Cysteine bidentates the zinc ion via one side chain sulfur and one back bone oxygen. Count: 153; percentage: 5.5%. PDB ID: 4FGL. Panel D: Cysteine bidentates the zinc ion via one side chain sulfur and one back bone nitrogen. Count: 57; percentage: 2.0%. PDB ID: 4A48.

Figure 7

Comparison of k in k‐means clustering of the normal group with respect to four metrics.

Figure 8

Comparison of k in k‐means clustering of the compressed group with respect to four metrics.

Figure 9

Hierarchical dendrogram (left) and Spearman's correlation (right) of structural and functional distances for k = 10 in the normal group.

Figure 10

Hierarchical dendrogram (left) and Spearman's correlation (right) of structural and functional distances for k = 8 in the compressed group.

Figure 11

Analysis of the deposition history of the March 2013 wwPDB zinc metalloprotein entries with compressed angles. Publication date of the key references are indicated on the graph.