Coarse-Grained Simulation of Protein-Imprinted Hydrogels

Rational Design of Molecularly Imprinted Polymers for Curcuminoids Binding: Computational and Experimental Approaches for the Selection of Functional Monomers

Molecularly imprinted polymers (MIPs) have emerged as bespoke materials with versatile molecular applications. In this study, we propose a proof of concept for a methodology employing molecular dynamics (MD) simulations to guide the selection of functional monomers for curcuminoid binding in MIPs. Curcumin, demethoxycurcumin, and bisdemethoxycurcumin are phenolic compounds widely employed as spices, pigments, additives, and therapeutic agents, representing the three main curcuminoids of interest. Through MD simulations, we investigated prepolymerization mixtures composed of various functional monomers, including acrylamide (ACA), acrylic acid (AA), methacrylic acid (MAA), and N-vinylpyrrolidone (NVP), with ethylene glycol dimethacrylate (EGDMA) as the cross-linker and acetonitrile as the solvent. Curcumin was selected as the template molecule due to its structural similarity to the other curcuminoids. Notably, the prepolymerization mixture containing NVP as the functional monomer demonstrated superior molecular recognition capabilities toward curcumin. This observation was supported by higher functional monomer molecules surrounding the template, a lower total nonbonded energy between the template and monomer, and a greater number of hydrogen bonds in the aggregate. These findings suggest a stronger affinity between the functional monomer NVP and the template. We synthesized, characterized, and conducted binding tests on the MIPs to validate the MD simulation results. The experimental binding tests confirmed that the MIP-NVP exhibited higher binding capacity. Consequently, based on MD simulations, our computational methodology effectively guided the selection of the functional monomer, leading to MIPs with binding capacity for curcuminoids. The outcomes of this study provide a valuable reference for the rational design of MIPs through MD simulations, facilitating the selection of components for MIPs. This computational approach holds the potential for extension to other templates, establishing a robust methodology for the rational design of MIPs.

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.

Molecularly Imprinted Nanoparticles towards MMP9 for Controlling Cardiac ECM after Myocardial Infarction: A Predictive Experimental-Computational Chemistry Investigation

The recent advances in nanotechnology are revolutionizing preventive and therapeutic approaches to treating cardiovascular diseases. Controlling the extracellular matrix metalloproteinase (MMP) activation and expression in the failing human left ventricular myocardium represents a significant therapeutic target for heart disease. In this study, we used molecularly imprinting polymers (MIPs) to restore the correct balance between MMPs and their tissue inhibitors (TIMPs), and explored the potential of this technique exhaustively through chemical synthesis, physicochemical and biological characterizations, and computational chemistry methods. By molecular dynamics simulations based on classical force fields, we simulated the early stages of the imprinting process in solution disclosing the pivotal interaction established between the monomers and the MMP9 protein template. The average interaction energies of methacrylic acid (MAA) and poly (ethylene glycol) ethyl ether methacrylate (PEG) units were in the ranges 17–22 and 30–37 kcal/mol, respectively. At low coverage, the PEG monomers seemed firmly anchored to the protein surface and were not displaced by water, while only about 20% of MAA was replaced by water. The synthesis of MIPs was successfully with a monomer conversion higher than 99% and the production of spherical particles with average diameter of 344 ± 33 nm. HPLC analysis showed a specific recognition factor of MMP9 on MIPs of about 1.3. FT-IR Chemical Imaging confirmed the mechanisms necessary to generate a “selective memory” of the MIPs towards the enzyme. HPLC results indicated that the rebound amount of both TIMP1 and MMP2 to MIPs is lower than that of the template, showing a selectivity factor of 2.1 and 2.3, respectively. Preliminary tests on the effect of MIPs on H9C2 cells revealed that this treatment has no cytotoxic effects.

Epitope-imprinted polymers: Design principles of synthetic binding partners for natural biomacromolecules

Molecular imprinting (MI) has been explored as an increasingly viable tool for molecular recognition in various fields. However, imprinting of biologically relevant molecules like proteins is severely hampered by several problems. Inspired by natural antibodies, the use of epitopes as imprinting templates has been explored to circumvent those limitations, offering lower costs and greater versatility. Here, we review the latest innovations in this technology, as well as different applications where MI polymers (MIPs) have been used to target biomolecules of interest. We discuss the several steps in MI, from the choice of epitope and functional monomers to the different production methods and possible applications. We also critically explore how MIP performance can be assessed by various parameters. Last, we present perspectives on future breakthroughs and advances, offering insights into how MI techniques can be expanded to new fields such as tissue engineering.

The Use of Computational Methods for the Development of Molecularly Imprinted Polymers

Recent years have witnessed a dramatic increase in the use of theoretical and computational approaches in the study and development of molecular imprinting systems. These tools are being used to either improve understanding of the mechanisms underlying the function of molecular imprinting systems or for the design of new systems. Here, we present an overview of the literature describing the application of theoretical and computational techniques to the different stages of the molecular imprinting process (pre-polymerization mixture, polymerization process and ligand-molecularly imprinted polymer rebinding), along with an analysis of trends within and the current status of this aspect of the molecular imprinting field.

Molecular Imprinting: Green Perspectives and Strategies

Advances in revolutionary technologies pose new challenges for human life; in response to them, global responsibility is pushing modern technologies toward greener pathways. Molecular imprinting technology (MIT) is a multidisciplinary mimic technology simulating the specific binding principle of enzymes to substrates or antigens to antibodies; along with its rapid progress and wide applications, MIT faces the challenge of complying with green sustainable development requirements. With the identification of environmental risks associated with unsustainable MIT, a new aspect of MIT, termed green MIT, has emerged and developed. However, so far, no clear definition has been provided to appraise green MIT. Herein, the implementation process of green chemistry in MIT is demonstrated and a mnemonic device in the form of an acronym, GREENIFICATION, is proposed to present the green MIT principles. The entire greenificated imprinting process is surveyed, including element choice, polymerization implementation, energy input, imprinting strategies, waste treatment, and recovery, as well as the impacts of these processes on operator health and the environment. Moreover, assistance of upgraded instrumentation in deploying greener goals is considered. Finally, future perspectives are presented to provide a more complete picture of the greenificated MIT road map and to pave the way for further development.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Tuning the selective permeability of polydisperse polymer networks

We study the permeability and selectivity ('permselectivity') of model membranes made of polydisperse polymer networks for molecular penetrant transport, using coarse-grained, implicit-solvent computer simulations. In our work, permeability P is determined on the linear-response level using the solution-diffusion model, P = KDin, i.e., by calculating the equilibrium penetrant partition ratio K and penetrant diffusivity Din inside the membrane. We vary two key parameters, namely the network-network interaction, which controls the degree of swelling and collapse of the network, and the network-penetrant interaction, which tunes the selective penetrant uptake and microscopic energy landscape for diffusive transport. We find that the partitioning K covers four orders of magnitude and is a non-monotonic function of the parameters, well interpreted by a second-order virial expansion of the free energy of transferring one penetrant from a reservoir into the membrane. Moreover, we find that the penetrant diffusivity Din in the polydisperse networks, in contrast to highly ordered membrane structures, exhibits relatively simple exponential decays. We propose a semi-empirical scaling law for the penetrant diffusion that describes the simulation data for a wide range of densities and interaction parameters. The resulting permeability P turns out to follow the qualitative behavior (including maximization and minimization) of partitioning. However, partitioning and diffusion are typically anti-correlated, yielding large quantitative cancellations, controlled and fine-tuned by the network density and interactions, as rationalized by our scaling laws. We finally demonstrate that even small changes of network-penetrant interactions, e.g., by half a kBT, modify the permselectivity by almost one order of magnitude.

Preparation of Gas-Responsive Imprinting Hydrogel and Their Gas-Driven Switchable Affinity for Target Protein Recognition

Novel gas-responsive imprinting hydrogels were fabricated by combining N,N'-dimethylaminoethyl methacrylate gas-sensitive monomers, N,N'-methylenebis(acrylamide) cross-linkers, and human serum albumin (HSA) template proteins via a free radical polymerization. The hydrogel exhibited a reversible gas-responsive property upon N₂/CO₂ exchange. This result was supported by the evidences from hydrogen nuclear magnetic resonance spectroscopy and scanning electron microscopy. By applying this property to sensing application, a CO₂-responsive imprinted biosensor was originally designed on the surface of a glassy carbon electrode. The biosensor exhibited unique self-clean and self-recognition properties toward HSA proteins based on reversible conformational changes driven by N₂/CO₂ stimuli. Moreover, the proposed imprinted biosensor favored HSA proteins by showing satisfactory sensitivity and selectivity and a wider detection range with a low detection limit. As a rare example in imprint sensing, the biosensor was successfully applied to the HSA extraction from complex serum samples. With gas stimuli, the whole process was efficient, controllable, and harmless to the proteins. Thus, the developed biosensor may provide a new prospect in molecularly imprinted sensing applications.

From Microscale to Macroscale: Nine Orders of Magnitude for a Comprehensive Modeling of Hydrogels for Controlled Drug Delivery

Because of their inherent biocompatibility and tailorable network design, hydrogels meet an increasing interest as biomaterials for the fabrication of controlled drug delivery devices. In this regard, mathematical modeling can highlight release mechanisms and governing phenomena, thus gaining a key role as complementary tool for experimental activity. Starting from the seminal contribution given by Flory-Rehner equation back in 1943 for the determination of matrix structural properties, over more than 70 years, hydrogel modeling has not only taken advantage of new theories and the increasing computational power, but also of the methods offered by computational chemistry, which provide details at the fundamental molecular level. Simulation techniques such as molecular dynamics act as a "computational microscope" and allow for obtaining a new and deeper understanding of the specific interactions between the solute and the polymer, opening new exciting possibilities for an in silico network design at the molecular scale. Moreover, system modeling constitutes an essential step within the "safety by design" paradigm that is becoming one of the new regulatory standard requirements also in the field-controlled release devices. This review aims at providing a summary of the most frequently used modeling approaches (molecular dynamics, coarse-grained models, Brownian dynamics, dissipative particle dynamics, Monte Carlo simulations, and mass conservation equations), which are here classified according to the characteristic length scale. The outcomes and the opportunities of each approach are compared and discussed with selected examples from literature.