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

Area of Science:

• Materials Science
• Computational Chemistry
• Biotechnology

Background:

• Artificial receptors, like molecularly imprinted polymers (MIPs), are crucial for mimicking natural antibodies.
• Combinatorial synthesis has improved MIP performance, but experimental screening of numerous monomer combinations is challenging.

Purpose of the Study:

• To introduce a computational method for screening monomer combinations to design selective molecular imprints.
• To explore monomer interactions and their impact on MIP binding capacity.

Main Methods:

• Development and application of a molecular mechanics-based multi-monomer simultaneous docking (MMSD) approach.
• Computational screening of monomer combinations for potential binding.
• Mapping of molecular models to analyze intermolecular interactions (H-bonding, hydrophobic).

Main Results:

• MMSD efficiently explores diverse multipoint interactions for peptide surfaces.
• Individual monomer binding capacities were analyzed for constructive or adverse additive effects.
• Complex formation directly impacts the binding capacity of resulting MIPs.
• Validation of MMSD's predictive potential through experimental imprinting studies.

Conclusions:

• MMSD enables the rational design of MIPs.
• The approach facilitates the synthesis of more sensitive and selective artificial receptor materials.
• MMSD is particularly effective for peptide and protein-epitope imprinting.