Molecular dynamics simulation of potentiometric sensor response: the effect of biomolecules, surface morphology and surface charge

An innovative experimental and mathematical approach in electrochemical sensing for mapping a drug sensor landscape

Our study delves into the examination of an electrochemical sensor through both experimentation and mathematical analysis. The sensor demonstrates the ability to identify a specific antipsychotic medication, namely Chlorpromazine Hydrochloride (CPH), even at incredibly low concentrations, reaching the picomolar level. The identification process relies on the utilization of a Glassy Carbon Electrode (GCE) that has been modified with a ceria-doped zirconia (CeO2/ZrO2) nanocomposite. The nanocomposite was synthesized using the co-precipitation technique and extensively characterized through various analytical methods. It is crucial to detect the presence of CPH as an overdose can result in hyperactivity and severe bipolar disorders among both children and adults. The average size of the nanocomposite was estimated to be 10 nm. The electrode surface area after CeO2/ZrO2 modification of the GCE was found to be 0.059 cm2, which was significantly higher than the electrode surface area of the bare GCE (0.0307 cm2). The limit of detection and limit of quantification for CPH were calculated to be 99.3 pM and 3.010 nM, respectively, with the linear dynamic range of CPH detection found to be between 0.10 and 1.90 μM. The modified sensor electrode was tested on human urine samples with good recoveries and exhibited high selectivity, repeatability, reproducibility, and long-term stability. The experimental voltammograms and the simulated stochastic voltammograms exhibited a fair amount of agreement. Examination of the experimental findings alongside analytical and numerical solutions enables a comprehensive analysis of the factors influencing the outcome of electrochemical measurements. The precise findings can be leveraged for the development of efficient sensing devices for medical diagnostics and environmental monitoring.

Signal transduction interfaces for field-effect transistor-based biosensors

Biosensors based on field-effect transistors (FETs) are suitable for use in miniaturized and cost-effective healthcare devices. Various semiconductive materials can be applied as FET channels for biosensing, including one- and two-dimensional materials. The signal transduction interface between the biosample and the channel of FETs plays a key role in translating electrochemical reactions into output signals, thereby capturing target ions or biomolecules. In this Review, distinctive signal transduction interfaces for FET biosensors are introduced, categorized as chemically synthesized, physically structured, and biologically induced interfaces. The Review highlights that these signal transduction interfaces are key in controlling biosensing parameters, such as specificity, selectivity, binding constant, limit of detection, signal-to-noise ratio, and biocompatibility.

Peptide Power: Mechanistic Insights into the Effect of Mitochondria-Targeted Tetrapeptides on Membrane Electrostatics from Molecular Simulations

Mitochondrial dysfunction is implicated in nine of the ten leading causes of death in the US, yet there are no FDA-approved therapeutics to treat it. Synthetic mitochondria-targeted peptides (MTPs), including the lead compound SS-31, offer promise, as they have been shown to restore healthy mitochondrial function and treat a variety of common diseases. At the cellular level, research has shown that MTPs accumulate strongly at the inner mitochondrial membrane (IMM), slow energy sinks (e.g., proton leaks), and improve ATP production. Modulation of electrostatic fields around the IMM has been implicated as a key aspect in the mechanism of action (MoA) of these peptides; however, molecular and mechanistic details have remained elusive. In this study, we employed all-atom molecular dynamics simulations (MD) to investigate the interactions of four MTPs with lipid bilayers and calculate their effect on structural and electrostatic properties. In agreement with previous experimental findings, we observed the modulation of the membrane surface and dipole potentials by MTPs. The simulations reveal that the MTPs achieve a reduction in the dipole potential by acting to disorder both lipid head groups and water layers proximal to the bilayer surface. We also find that MTPs decrease the bilayer thickness and increase the membrane's capacitance. These changes suggest that MTPs may enhance how much potential energy can be stored across the IMM at a given transmembrane potential difference. The MTPs also displace cations away from the bilayer surface, modulating the surface potential and offering an alternative mechanism for how these MTPs reduce mitochondrial energy sinks like proton leaks and mitigate Ca2+ accumulation stress. In conclusion, this study highlights the therapeutic potential of MTPs and underlines how interactions of MTPs with lipid bilayers serve as a fundamental component of their MoA.

Biophysical Properties of Lipid Membranes from Barley Roots during Low-Temperature Exposure and Recovery

Glycerolipid remodeling, a dynamic mechanism for plant subsistence under cold stress, has been posited to affect the biophysical properties of cell membranes. In barley roots, remodeling has been observed to take place upon exposure to chilling stress and to be partially reverted during stress relief. In this study, we explored the biophysical characteristics of membranes formed with lipids extracted from barley roots subjected to chilling stress, or during a subsequent short- or long-term recovery. Our aim was to determine to what extent barley roots were able to offset the adverse effects of temperature on their cell membranes. For this purpose, we analyzed the response of the probe Laurdan inserted in bilayers of different extracts, the zeta potential of liposomes, and the behavior of Langmuir monolayers upon compression. We found important changes in the order of water molecules, which is in agreement with the changes in the unsaturation index of lipids due to remodeling. Regarding Langmuir monolayers, we found that films from all the extracts showed a reorganization at a surface pressure that depends on temperature. This reorganization occurred with an increase in entropy for extracts from control plants and without entropy changes for extracts from acclimated plants. In summary, some membrane properties were recovered after the stress, while others were not, suggesting that the membrane biophysical properties play a role in the mechanism of plant acclimation to chilling. These findings contribute to our understanding of the impact of lipid remodeling on biophysical modifications in plant roots.

Atomistic Simulations of Functionalized Nano-Materials for Biosensors Applications

Nanoscale biosensors, a highly promising technique in clinical analysis, can provide sensitive yet label-free detection of biomolecules. The spatial and chemical specificity of the surface coverage, the proper immobilization of the bioreceptor as well as the underlying interfacial phenomena are crucial elements for optimizing the performance of a biosensor. Due to experimental limitations at the microscopic level, integrated cross-disciplinary approaches that combine in silico design with experimental measurements have the potential to present a powerful new paradigm that tackles the issue of developing novel biosensors. In some cases, computational studies can be seen as alternative approaches to assess the microscopic working mechanisms of biosensors. Nonetheless, the complex architecture of a biosensor, associated with the collective contribution from "substrate-receptor-analyte" conjugate in a solvent, often requires extensive atomistic simulations and systems of prohibitive size which need to be addressed. In silico studies of functionalized surfaces also require ad hoc force field parameterization, as existing force fields for biomolecules are usually unable to correctly describe the biomolecule/surface interface. Thus, the computational studies in this field are limited to date. In this review, we aim to introduce fundamental principles that govern the absorption of biomolecules onto functionalized nanomaterials and to report state-of-the-art computational strategies to rationally design nanoscale biosensors. A detailed account of available in silico strategies used to drive and/or optimize the synthesis of functionalized nanomaterials for biosensing will be presented. The insights will not only stimulate the field to rationally design functionalized nanomaterials with improved biosensing performance but also foster research on the required functionalization to improve biomolecule-surface complex formation as a whole.

Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories

Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT_m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT_m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature.

Mesoporous thin films on graphene FETs: nanofiltered, amplified and extended field-effect sensing

The ionic screening and the response of non-specific molecules are great challenges of biosensors based on field-effect transistors (FETs). In this work, we report the construction of graphene based transistors modified with mesoporous silica thin films (MTF-GFETs) and the unique (bio)sensing properties that arise from their synergy. The developed method allows the preparation of mesoporous thin films free of fissures, with an easily tunable thickness, and prepared on graphene-surfaces, preserving their electronic properties. The MTF-GFETs show good sensing capacity to small probes that diffuse inside the mesopores and reach the graphene semiconductor channel such as H⁺, OH^-, dopamine and H₂O₂. Interestingly, MTF-GFETs display a greater electrostatic gating response in terms of amplitude and sensing range compared to bare-GFETs for charged macromolecules that infiltrate the pores. For example, for polyelectrolytes and proteins of low MW, the amplitude increases almost 100% and the sensing range extends more than one order of magnitude. Moreover, these devices show a size-excluded electrostatic gating response given by the pore size. These features are even displayed at physiological ionic strength. Finally, a developed thermodynamic model evidences that the amplification and extended field-effect properties arise from the decrease of free ions inside the MTFs due to the entropy loss of confining ions in the mesopores. Our results demonstrate that the synergistic coupling of mesoporous films with FETs leads to nanofiltered, amplified and extended field-effect sensing (NAExFES).

Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies

Adsorption and desorption of molecules are key processes in extraction and purification of biomolecules, engineering of drug carriers, and designing of surface-specific coatings. To understand the adsorption process on the atomic scale, state-of-the-art quantum mechanical and classical simulation methodologies are widely used. However, studying adsorption using a full quantum mechanical treatment is limited to picoseconds simulation timescales, while classical molecular dynamics simulations are limited by the accuracy of the existing force fields. To overcome these challenges, we propose a systematic way to generate flexible, application-specific highly accurate force fields by training artificial neural networks. As a proof of concept, we study the adsorption of the amino acid alanine on graphene and gold (111) surfaces and demonstrate the force field generation methodology in detail. We find that a molecule-specific force field with Lennard-Jones type two-body terms incorporating the 3rd and 7th power of the inverse distances between the atoms of the adsorbent and the surfaces yields optimal results, which is surprisingly different from typical Lennard-Jones potentials used in traditional force fields. Furthermore, we present an efficient and easy-to-train machine learning model that incorporates system-specific three-body (or higher order) interactions that are required, for example, for gold surfaces. Our final machine learning-based force field yields a mean absolute error of less than 4.2 kJ/mol at a speed-up of ∼10⁵ times compared to quantum mechanical calculation, which will have a significant impact on the study of adsorption in different research areas.

Addressing the Theoretical and Experimental Aspects of Low-Dimensional-Materials-Based FET Immunosensors: A Review

Electrochemical immunosensors (EI) have been widely investigated in the last several years. Among them, immunosensors based on low-dimensional materials (LDM) stand out, as they could provide a substantial gain in fabricating point-of-care devices, paving the way for fast, precise, and sensitive diagnosis of numerous severe illnesses. The high surface area available in LDMs makes it possible to immobilize a high density of bioreceptors, improving the sensitivity in biorecognition events between antibodies and antigens. If on the one hand, many works present promising results in using LDMs as a sensing material in EIs, on the other hand, very few of them discuss the fundamental interactions involved at the interfaces. Understanding the fundamental Chemistry and Physics of the interactions between the surface of LDMs and the bioreceptors, and how the operating conditions and biorecognition events affect those interactions, is vital when proposing new devices. Here, we present a review of recent works on EIs, focusing on devices that use LDMs (1D and 2D) as the sensing substrate. To do so, we highlight both experimental and theoretical aspects, bringing to light the fundamental aspects of the main interactions occurring at the interfaces and the operating mechanisms in which the detections are based.

Atomistic simulations of gold surface functionalization for nanoscale biosensors applications

A wide class of biosensors can be built via functionalization of gold surface with proper bio conjugation element capable of interacting with the analyte in solution, and the detection can be performed either optically, mechanically or electrically. Any change in physico-chemical environment or any slight variation in mass localization near the surface of the sensor can cause differences in nature of the transduction mechanism. The optimization of such sensors may require multiple experiments to determine suitable experimental conditions for the immobilization and detection of the analyte. Here, we employ molecular modeling techniques to assist the optimization of a gold-surface biosensor. The gold surface of a quartz-crystal-microbalance sensor is functionalized using polymeric chains of poly(ethylene glycol) (PEG) of 2 KDa molecular weight, which is an inert long chain amphiphilic molecule, supporting biotin molecules (bPEG) as the ligand molecules for streptavidin analyte. The PEG linkers are immobilized onto the gold surface through sulphur chemistry. Four gold surfaces with different PEG linker density and different biotinylation ratio between bPEG and PEG, are investigated by means of state-of-the art atomistic simulations and compared with available experimental data. Results suggest that the amount of biotin molecules accessible for the binding with the protein increases upon increasing the linkers density. At the high density a 1:1 ratio of bPEG/PEG can further improve the accessibility of the biotin ligand due to a strong repulsion between linker chains and different degree of hydrophobicity between bPEG and PEG linkers. The study provides a computaional protocol to model sensors at the level of single molecular interactions, and for optimizing the physical properties of surface conjugated ligand which is crucial to enhance output of the sensor.

Molecular Dynamics Simulation Study on Two-Component Solubility Parameters of Carbon Nanotubes and Precisely Tailoring the Thermodynamic Compatibility between Carbon Nanotubes and Polymers

Solubility parameters play an important role in predicting compatibility between components. The current study on solubility parameters of carbon materials (graphene, carbon nanotubes, and fullerene, etc.) is unsatisfactory and stagnant due to experimental limitations, especially the lack of a quantitative relationship between functional groups and solubility parameters. Fundamental understanding of the high-performance nanocomposites obtained by carbon material modification is scarce. Therefore, in the past, the trial and error method was often used for the modification of carbon materials, and no theory has been formed to guide the experiment. In this work, the effect of defects, size, and the number of walls on the Hildebrand solubility parameter (δ_T) of carbon nanotubes (CNTs) was investigated by molecular dynamics (MD) simulation. Besides, three-component Hansen solubility parameters (δ_D, δ_p, δ_H) were transformed into two-component solubility parameters (δ_vdW, δ_elec). The quantitative relation between functional groups and two-component solubility parameters of single-walled carbon nanotubes (SWCNTs) was then given. An important finding is that the δ_T and δ_vdW of SWCNTs first decrease, reach a minimum, and then increase with increasing grafting ratio. The thermodynamic compatibility between functionalized SWCNTs and six typical polymers was investigated by the Flory-Huggins mixing model. Two-component solubility parameters were proven to be able to effectively predict their compatibility. Importantly, we theoretically gave the optimum grafting ratio at which the compatibility between functionalized SWCNTs and polymers is the best. The functionalization principle of SWCNTs toward good compatibility between SWCNTs and polymers was also given. This study gives a new insight into the solubility parameters of functionalized SWCNTs and provides theoretical guidance for the preparation of high-performance SWCNTs/polymers composites.

Biologically Coupled Gate Field-Effect Transistors Meet in Vitro Diagnostics

In this paper, recent works on biologically coupled gate field-effect transistor (bio-FET) sensors are introduced and compared to provide a perspective. Most biological phenomena are closely related to behaviors of ions and biomolecules. This is why biosensing devices for detecting ionic and biomolecular charges contribute to the direct analysis of biological phenomena in a label-free and enzyme-free manner. Potentiometric biosensors such as bio-FET sensors, which allow the direct detection of these charges on the basis of the field effect, meet this requirement and have been developed as simple devices for in vitro diagnostics (IVD). A variety of biological ionic behaviors generated by biomolecular recognition events and cellular activities are being targeted for clinical diagnostics as well as the study of neuroscience using the bio-FET sensors. To realize these applications, bioelectrical interfaces should be formed between the electrolyte solution and the gate electrode by modifying artificially synthesized and biomimetic membranes, resulting in the selective detection of targets based on intrinsic molecular charges. Various types of semiconducting materials, not only inorganic semiconductors but also organic semiconductors, can be selected for use in bio-FET sensors, depending on the application field. In addition, a semiconductor integrated circuit device is ideal for the massively parallel detection of multiple samples. Thus, platforms based on bio-FET sensors are suitable for use in simple and miniaturized electrical circuit systems for IVD to enable the prevention and early detection of diseases.

Molecularly Imprinted Artificial Biointerface for an Enzyme-Free Glucose Transistor

A platform based on a highly selective and sensitive detection device functionalized with a well-designed artificial biointerface is required for versatile biosensors. We develop a molecularly imprinted polymer (MIP)-coated gate field-effect transistor (FET) biosensor for low-concentration glucose detection in biological fluid samples such as tears in an enzyme-free manner. The MIP includes glucose templates (GluMIP), in which glucose binds to vinylphenylboronic acid in the copolymerized membrane, resulting in the change in the density of molecular charges of the phenylboronic acid (PBA)/glucose complex. The FET biosensor can detect small biomolecules as long as biomolecular recognition events cause intrinsic changes in the density of molecular charges. As a result, the changes in the output voltage detected using the GluMIP-based FET sensor are fitted to the Langmuir adsorption isotherm equation at various concentrations of sugars, showing the low detection limit of 3 μM and the high sensitivity of 115 mV/decade from 100 μM to 4 mM glucose. On the basis of the equation, the stability constant ( K_a) of PBA with glucose is calculated and found to markedly increase to K_a = 1192 M^-1, which is higher by a factor of a few hundreds than K_a = 4.6 M^-1 obtained by nonelectrical detection methods. Moreover, the GluMIP-coated gate FET sensor shows an approximately 200-fold higher selectivity for glucose than for fructose. This is because glucose binds to PBA more selectively than fructose in the templates, resulting in the generation of negative charges. The electrical properties of the MIP-coated electrode are also evaluated by measuring capacitance. Our work suggests a new strategy of designing a platform based on the MIP-coated gate FET biosensor, which is suitable for a highly selective, sensitive, enzyme-free biosensing system.