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Abstract: 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
Template and target information: histamine, histadine, theophylline, caffeine, theobromine