An in silico predictive method to select multi-monomer combinations for peptide imprinting

In silico methods enable optimizing artificial receptors such that constructive mimics of natural antibodies can be envisaged. The introduction of combinatorial synthesis strategies via multi-monomer combinations has improved the performance of molecularly imprinted polymers (MIP) significantly. However, it remains experimentally challenging to screen thousands of combinations resulting from a large library of monomers. The present study introduces a molecular mechanics based multi-monomer simultaneous docking approach (MMSD) to computationally screen monomer combinations according to their potential, facilitating selective molecular imprints. Thereby, the diversity of multipoint interactions realizable with a peptide surface is efficiently explored yielding how individual monomer binding capacities constructively or adversely add up when docked together. Additionally, spatially distributed molecular models were mapped for analyzing intermolecular H-bonding and hydrophobic interactions resulting from single monomer docking, as well as bi- and tri-monomer simultaneous docking. A direct impact of complex formation on the binding capacity of the resulting MIPs has been observed. In a first small-scale study, the predictive potential of the MMSD approach was validated via experimentally applied polymer combinations for peptide imprinting via the scoring functions established during the screening process. MMSD clearly enables rational design of MIPs for synthesizing more sensitive and selective artificial receptor materials especially for peptide and protein-epitope templates.

Automated summary

In this study, a molecular mechanics based multi-monomer simultaneous docking approach (MMSD) was introduced to computationally screen monomer combinations for selective molecular imprints. The predictive potential of the MMSD approach for peptide imprinting was validated experimentally, and the predicted scores were obtained for polymer combinations.