In silico characterization of enantioselective molecularly imprinted binding sites.

A 2-step molecular mechanical and quantum mechanical geometry optimization scheme (MM âž” QM) was used to "computationally imprint" chiral molecules. Using a docking technique, we show the imprinted binding sites to exhibit an enantioselective preference for the imprinted molecule over its enantiomer. Docking of structurally similar chiral molecules showed that the sites computationally imprinted with R- or S-tBOC-tyrosine were able to differentiate between R- and S-forms of other tyrosine derivatives. The cross-enantioselectivity did not hold for chiral molecules that did not share the tyrosine H-bonding functional group orientations. Further analysis of the individual monomer-target interactions within the binding site lead us to conclude that H-bonding functional groups that are located immediately next to the chiral center and therefore spatially fixed relative to the chiral center will have a stronger contribution to the enantioselectivity of the site than those groups separated from the chiral center by 2 or more rotatable bonds. Here, we present our novel approach for computationally imprinting and characterizing enantioselective binding sites. All modeling schemes were designed to minimize the computational expense. In silico analysis of the properties of molecularly imprinted polymer systems will ultimately allow for the fabrication of more sensitive and selective materials.

Computational investigation of stoichiometric effects, binding site heterogeneities, and selectivities of molecularly imprinted polymers.

A series of quantum mechanical (QM) computational optimizations of molecularly imprinted polymer (MIP) systems were used to determine optimal monomer-to-target ratios. Imidazole- and xanthine-derived target molecules were studied. The investigation included both small-scale models (3-7 molecules) and larger-scale models (15-35 molecules). The optimal ratios differed between the small and larger scales. For the larger models containing multiple targets, binding-site surface area analysis was used to quantify the heterogeneity of these sites. The more fully surrounded sites had greater binding energies. No discretization of binding modes was seen, furthering arguments for continuous affinity distribution models. Molecular mechanical (MM) docking was then used to measure the selectivities of the QM-optimized binding sites. Selectivity was also shown to improve as binding sites become more fully encased by the monomers. For internal sites, docking consistently showed selectivity favoring the molecules that had been imprinted via QM geometry optimizations. The computationally imprinted sites were shown to exhibit size-, shape-, and polarity-based selectivity. Here we present a novel approach to investigate the selectivity and heterogeneity of imprinted polymer binding sites, by applying the rapid orientation screening of MM docking to the highly accurate QM-optimized geometries. Modeling schemes were designed such that no computing clusters or other specialized modeling equipment would be required. Improving the in silico analysis of MIP system properties will ultimately allow for the production of more sensitive and selective polymers.