Prediction of the interaction strength of an urea-based probe towards ions in water by means of DFT/PCM calculations
Prediction of the interaction strength of an urea-based probe towards ions in water by means of DFT/PCM calculations
BENDA ; VEZIN ; ZUCCHI ; CANCES ; LEBENTAL
Type de document
ARTICLE A COMITE DE LECTURE REPERTORIE DANS BDI (ACL)
Langue
anglais
Auteur
BENDA ; VEZIN ; ZUCCHI ; CANCES ; LEBENTAL
Résumé / Abstract
We study numerically, by means of DFT calculations complemented with an implicit solvation model, a novel chemical probe bearing urea and aromatic phenyl groups. We probe the interaction
in water of the latter with a wide variety of ions relevant to water quality. We perform geometry minimizations using PBE0 functional and aug-cc-pVDZ basis set, and a Polarizable Continuum
Model (PCM) to take into account the aqueous solvent. We underline several methodological detailsconcerning the definition of the binding or interaction energy, and the Basis Set Superposition Error definition in the context of implicit solvation models. We observe two competing interaction modes for this probe : a urea-enhanced, cation-? interaction (with cations only), and hydrogen bonding occurring between the urea group and anions, the former being more favorable than the latter. A Generalized Kohn-Sham Energy Decomposition Analysis (GKS-EDA) [1] in implicit solvent is performed to analyze the nature of the ions - probe interactions. We unveil two families of hydrogen bonding interactions with urea, through oxygen atoms of polyatomic anions on the one hand, and with halides on the other. Magnesium and sodium ions, and respectively glyphosate and hypochlorite ions, are found as the cations (resp. anions) having the largest binding free energies with the probe. This is the first time such an exhaustive selectivity study is carried out in the context of DFT/PCM models. Moreover, this methodology can be used as a general way to gain a valuable insight into the sensitivity of organic ligands towards a variety of ions or pesticides in water, without the need of an explicit solvent description. By predicting possible competitive interactions, and understanding their nature, this methodology can thus help to better design functional groups selective to specific targets.
Source
Journal of Computer-Aided Molecular Design, 27P p.
Editeur
Springer Verlag
