Plastic Antibodies Mimicking the ACE2 Receptor for Selective Binding of SARS-CoV-2 Spike

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

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.

Rational In Silico Design of Molecularly Imprinted Polymers: Current Challenges and Future Potential

The rational design of molecularly imprinted polymers has evolved along with state-of-the-art experimental imprinting strategies taking advantage of sophisticated computational tools. In silico methods enable the screening and simulation of innovative polymerization components and conditions superseding conventional formulations. The combined use of quantum mechanics, molecular mechanics, and molecular dynamics strategies allows for macromolecular modelling to study the systematic translation from the pre- to the post-polymerization stage. However, predictive design and high-performance computing to advance MIP development are neither fully explored nor practiced comprehensively on a routine basis to date. In this review, we focus on different steps along the molecular imprinting process and discuss appropriate computational methods that may assist in optimizing the associated experimental strategies. We discuss the potential, challenges, and limitations of computational approaches including ML/AI and present perspectives that may guide next-generation rational MIP design for accelerating the discovery of innovative molecularly templated materials.

Specific capture and determination of glycoprotein using a hybrid epitopes and monomers-mediated molecular-imprinted polymer enzyme-free electrochemical biosensor

A novel molecular-imprinted polymer (MIP)-based enzyme-free biosensor was created for the selective detection of glycoprotein transferrin (Trf). For this purpose, MIP-based biosensor for Trf was prepared by electrochemical co-polymerization of novel hybrid monomers 3-aminophenylboronic acid (M-APBA) and pyrrole on a glassy carbon electrode (GCE) modified with carboxylated multi-walled carbon nanotubes (cMWCNTs). Hybrid epitopes of Trf (C-terminal fragment and glycan) have been selected as templates. The produced sensor exhibited great selective recognition ability toward Trf under optimal preparation conditions, offering good analytical range (0.125-1.25 μM) with a detection limit of 0.024 μM. The proposed hybrid epitope in combination with hybrid monomer-mediated imprinting strategy was successfully applied to detect Trf in spiked human serum samples, with recoveries and relative standard deviations ranging from 94.7 to 106.0% and 2.64 to 5.32%, respectively. This study provided a reliable protocol for preparing hybrid epitopes and monomers-mediated MIP for the synergistic and effective determination of glycoprotein in complicated biological samples.

Molecularly Imprinted Polymers as Synthetic Antibodies for Protein Recognition: The Next Generation

Molecularly imprinted polymers (MIPs) are chemical antibody mimics obtained by nanomoulding the 3D shape and chemical functionalities of a desired target in a synthetic polymer. Consequently, they possess exquisite molecular recognition cavities for binding the target molecule, often with specificity and affinity similar to those of antigen-antibody interactions. Research on MIPs targeting proteins began in the mid-90s, and this review will evaluate the progress made till now, starting from their synthesis in a monolith bulk format through surface imprinting to biocompatible soluble nanogels prepared by solid-phase synthesis. MIPs in the latter format will be discussed more in detail because of their tremendous potential of replacing antibodies in the biomedical domain like in diagnostics and therapeutics, where the workforce of antibodies is concentrated. Emphasis is also put on the development of epitope imprinting, which consists of imprinting a short surface-exposed fragment of a protein, resulting in MIPs capable of selectively recognizing the whole macromolecule, amidst others in complex biological media, on cells or tissues. Thus selecting the 'best' peptide antigen is crucial and in this context a rational approach, inspired from that used to predict peptide immunogens for peptide antibodies, is described for its unambiguous identification.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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.

Molecularly Imprinted Polymer-Based Sensors for SARS-CoV-2: Where Are We Now?

Since the first reported case of COVID-19 in 2019 in China and the official declaration from the World Health Organization in March 2021 as a pandemic, fast and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has played a major role worldwide. For this reason, various methods have been developed, comprising reverse transcriptase-polymerase chain reaction (RT-PCR), immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and bio(mimetic)sensors. Among the developed methods, RT-PCR is so far the gold standard. Herein, we give an overview of the MIP-based sensors utilized since the beginning of the pandemic.