Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography

Abstract

The bond-valence model is a reliable way to validate assumed oxidation states based on structural data. It has successfully been employed for analyzing metal-binding sites in macromolecule structures. However, inconsistent results for heme-based structures suggest that some widely used bond-valence R0 parameters may need to be adjusted in certain cases. Given the large number of experimental crystal structures gathered since these initial parameters were determined and the similarity of binding sites in organic compounds and macromolecules, the Cambridge Structural Database (CSD) is a valuable resource for refining metal-organic bond-valence parameters. R0 bond-valence parameters for iron(II), iron(III) and other metals have been optimized based on an automated processing of all CSD crystal structures. Almost all R0 bond-valence parameters were reproduced, except for iron-nitrogen bonds, for which distinct R0 parameters were defined for two observed subpopulations, corresponding to low-spin and high-spin states, of iron in both oxidation states. The significance of this data-driven method for parameter discovery, and how the spin state affects the interpretation of heme-containing proteins and iron-binding sites in macromolecular structures, are discussed. Using all available metal-containing organic compound structures in the Cambridge Structural Database, a novel data-driven method to derive bond-valence R0 parameters was developed. While confirming almost all reference literature values, two distinct populations of FeII - N and FeIII - N bonds are observed, which are interpreted as low-spin and high-spin states of the coordinating iron. Based on the R0 parameters derived here, guidelines for the modeling of iron-ligand distances in macromolecular structures are suggested.