Abstract

Nanoscale molecularly imprinted polymers (nano-MIPs) are powerful molecular recognition tools with broad applications in the diagnosis, prognosis, and treatment of complex diseases. In this work, fully atomistic molecular dynamics (MD) simulations are used to assist the design of nanoMIPs with recognition capacity toward L-fucose and D-mannose as prototype disease biomarkers. MD simulations were conducted on prepolymerization mixtures containing different molar ratios of the monomers N-isopropylacrylamide (NIPAM), methacrylamide (MAM), and (4-acrylamidophenyl)(amino)methaniminium acetate (AB) and fixed molar ratios of the cross-linker ethylene glycol dimethacrylate (EGDMA) in explicit acetonitrile as the porogenic solvent. Prepolymerization mixtures containing ternary mixtures of NIPAM (50%), MAM (25%), and AB (25%) exhibit the best imprinting potential for both L-fucose and D-mannose, as they maximize (i) the stability of template-monomer plus template-cross-linker interactions, (ii) the number of functional monomers plus cross-linkers organized around the template, and (iii) the number of hydrogen bonds participating in template recognition. The studied prepolymerization mixtures exhibit an overall increased recognition capacity toward D-mannose over L-fucose, which is attributed to the higher hydrogen-bonding capacity of the former template. Our results are valuable to guide the synthesis of efficient nanoMIPs for sugar recognition and provide a computational framework extensible to any other template, monomer, or cross-linker combination, thus constituting a promising strategy for the rational design of molecularly imprinted materials.