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.

AceDRG: a stereochemical description generator for ligands

“…To apply this approach we need to analyse the whole COD, classify metals with their environments and then apply them to coordinates as necessary. This approach has been successfully applied (Zheng et al, 2017).…”

CheckMyMetal: a macromolecular metal-binding validation tool

“…The typical iron-ligand distances in these sites are dependent on several factors including coordination number (five or six), oxidation state (II or III) and spin state (low spin or high spin). Consequently, it would be imprecise to define a single typical distance for a given iron-ligand coordination bond, especially in the case of iron-nitrogen interactions (Pidcock, 1995;Zheng et al, 2017). For example, heme may display different iron-nitrogen distances depending not only on the oxidation state of the central iron but also on the spin state of the central iron (Zheng et al, 2017).…”

“…Consequently, it would be imprecise to define a single typical distance for a given iron-ligand coordination bond, especially in the case of iron-nitrogen interactions (Pidcock, 1995;Zheng et al, 2017). For example, heme may display different iron-nitrogen distances depending not only on the oxidation state of the central iron but also on the spin state of the central iron (Zheng et al, 2017). The iron-nitrogen distance could be as low as around 1.98 Å in the case of a low-spin Fe 2+ ion or could be as high as around 2.24 Å in the case of a high-spin Fe 3+ ion.…”

Augmenting High-Performance Mobile Cloud Computations for Big Data in AMBER

“…The investigation is also helpful for the exascale calculation in future [5]. The technique depends on the presumption of "common" bond lengths and points, deviation from which prompts strain, and the presence of torsional connections and appealing or potentially terrible van der Waals and dipolar powers between nonfortified molecules [19]. The strategy is called experimental drive field gullible.…”