The increasing ubiquity of charged organic contaminants in environmental systems has elevated the need for predictive sorption models. Current models for organic cation sorption to organic matter use an additive framework, predicting sorption coefficients based on sorbate size plus an electrostatic charge contribution. However, maps of the electrostatic surface potential of organic cations show electron distributions to vary across the whole molecule with the addition of neutral substituent groups. Thus, there is a need for predictive tools that incorporate the intrinsic electron distributions of charged compounds in determining sorption interactions. This problem is analogous to that of drug-protein binding predictions in pharmaceutical sciences for which effective computational chemistry tools have been applied. Here, we examined the extent to which molecular docking techniques can be applied for prediction of organic cation/organic matter binding affinities using an atomistic representation of the “Schulten humic acid” molecule with the software tool, AutoDock. Calculated sorbate binding affinities were correlated strongly with experimental measures of sorption coefficients for organic cations and Pahokee Peat organic matter. Further, computational tools allow for exploration of the importance of sorption energy contributions by hydrogen bonding, electrostatic attraction and van der Waals interactions. Primary, secondary, and tertiary amines interacted with organic matter carboxylic groups via hydrogen bonds while positive charges on quaternary amine hydrogens are too weak to participate in interactions at organic matter charge sites. Secondary electrostatic interactions between organic cations and organic matter also gradually weakened with increasing amine order due to increasing charge delocalization. For compounds with the same amine order, sorption coefficients were correlated with van der Waals energy contributions, suggesting these interactions largely drive the extent of uptake; however, correlations were unique to amine order. Our work shows that molecular docking tools provide an efficient means for predicting binding mechanisms and affinities of cationic organic contaminants.

Sorption of organic cations to organic matter: Use of molecular docking simulations to understand binding interactions

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.