Rational design of MIPs for the detection of Myxovirus resistance protein A (MxA), a biomarker for viral infection

Abstract

Accurate diagnosis is crucial for effective patient care and the containment of antimicrobial resistance outbreaks. The intricate challenge of distinguishing bacterial from viral infections, coupled with limited diagnostic tools and overlapping symptoms has driven the utilization of molecular imprinting techniques. This study focuses on developing cost-effective, chemically stable antibody analogs for the interferon-induced protein myxovirus resistance protein A (MxA). MxA is an intracellular, cytoplasmic GTPase having activity against a wide range of viruses and serves as a distinctive biomarker for viral infections. We utilized computational design to guide the polymer assembly, centering on epitope imprinting to target MxA-specific regions crucial for interaction. Molecular docking calculations, alongside a pioneering multi-monomer simultaneous docking (MMSD) protocol, efficiently elucidate cooperativity during pre-polymerization. Monomer binding affinity scores, such as for APTMS, exhibited notable increase, ranging from −3.11 to −13.03 kcal/mol across various MMSD combinations compared to a maximum of −2.78 kcal/mol in single monomer docking, highlighting the capacity of MMSD in elucidating crucial monomer-monomer interactions. This computational approach provides a theoretical alternative to labor-intensive experimental optimization, streamlining the development process for synthetic receptors. Simulations reveal unique interactions enhancing MIP-peptide complementarity, yielding optimized receptors selectively binding to MxA epitopes. The obtained MIPs demonstrated a maximum adsorption capacity of approximately 12 mg/g and captured 1.6 times more epitope and 2.6 times more epitope containing MxA protein than corresponding NIPs. A proof-of-concept study demonstrates MxA protein binding to synthetic receptors, highlighting the potential of MIPs, analogous to antibodies, in overcoming current diagnostic challenges for precise detection of viral infection.