How simple is too simple? Computational perspective on importance of second-shell environment for metal-ion selectivity†
Abstract
The metal-ion selectivity in biomolecules represents one of the most important phenomena in bioinorganic chemistry. The open question to what extent is the selectivity in the complex bioinorganic structures such as metallopeptides determined by the first-shell ligands of the metal ion is answered herein using six model peptides complexed with the set of divalent metal ions (Mn2+, Fe2+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, and Hg2+) and their various first-shell representations. By calculating the differences among the free energies of complexation of metal ions in these peptides and their model (truncated) systems it is quantitatively shown that the definition of the first shell is paramount to this discussion and revolves around the chemical nature of the binding site. Despite the vast conceivable diversity of peptidic structures, that suggest certain fluidity of this definition, major contributing factors are identified and assessed based on their importance for capturing metal-ion selectivity. These factors include soft/hard character of ligands and various non-covalent interactions in the vicinity of the binding site. The relative importance of these factors is considered and specific suggestions for effective construction of the models are made. The relationship of first-shell models and their corresponding parent peptides is discussed thoroughly, both with respect to their chemical similarity and potential disparity introduced by generally “non-alignable” conformational flexibility of the two systems. It is concluded that, in special cases, this disparity can be negligible and that heeding the chemical factors contributing to selectivity during construction of the model can successfully result in models that retain the affinity profile for various metal ions with high fidelity.
- This article is part of the themed collection: Theoretical chemistry developments: from electronic structure to simulations