In silico characterization of enantioselective molecularly imprinted binding sites
Abstract
A 2-step molecular mechanical and quantum mechanical geometry optimization scheme (MM ➔ 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.
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- In silico toxicology protocols
- Automated in Silico Design of Homogeneous Catalysts
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.