Rational Design of Highly Selective Sialyllactose-Imprinted Nanogels.

We describe a facile method to prepare water-compatible molecularly imprinted polymer nanogels (MIP NGs) as synthetic antibodies against target glycans. Three different phenylboronic acid (PBA) derivatives were explored as monomers for the synthesis of MIP NGs targeting either α2,6- or α2,3-sialyllactose, taken as oversimplified models of cancer-related sT and sTn antigens. Starting from commercially available 3-acrylamidophenylboronic acid, also its 2-substituted isomer and the 5-acrylamido-2-hydroxymethyl cyclic PBA monoester derivative were initially evaluated by NMR studies. Then, a small library of MIP NGs imprinted with the α2,6-linked template was synthesized and tested by mobility shift Affinity Capillary Electrophoresis (msACE), to rapidly assess an affinity ranking. Finally, the best monomer 2-acrylamido PBA was selected for the synthesis of polymers targeting both sialyllactoses. The resulting MIP NGs display an affinity constant≈106 M-1 and selectivity towards imprinted glycans. This general procedure could be applied to any non-modified carbohydrate template possessing a reducing end.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates.

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.