In silico design of peptides with binding to the receptor binding domain (RBD) of the SARS-CoV-2 and their utility in bio-sensor development for SARS-CoV-2 detection

Development of Ni-ZnO-ACE-2 peptide hybrids as electrochemical devices for SARS-CoV-2 spike protein detection

Owing to fast SARS-CoV-2 mutations, biosensors employing antibodies as biorecognition elements have presented problems with sensitivity and accuracy. To face these challenges, antibodies can be replaced with the human angiotensin converting enzyme 2 (ACE-2), where it has been shown that the affinity between ACE-2 and the receptor binding domain (RBD) increases with the emergence of new variants. Herein, we report on Ni-doped ZnO nanorod electrochemical biosensors employing an ACE-2 peptide (IEEQAKTFLDKFNHEAEDLFYQS-NH2) as a biorecognition element for detecting Spike (S) Wild-Type (WT) protein. The electrode was fully characterized in terms of electrochemical and physical properties. The sensor showed high cross reactivity with Spike protein B.1.1.7 and Spike protein B.1.351. Still, there was no cross reactivity with the Nucleocapsid protein WT, showing that the biosensor can identify ancestral WT S protein and S protein variants of concern. The device exhibited a LOD of 60.13 ng mL-1 across an S protein WT concentration range from 200 ng mL-1 to 1000 ng mL-1 and a LOQ of 182.22 ng mL-1. The calculated sensitivity and specificity were 88.88 and 100 %, respectively. These results proved that the Ni-ZnO sensor has promising prospects for SARS-CoV-2 detection and diagnosis of other viruses, employing peptides as biorecognition elements.

Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms

Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein's RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)6]3-/4 solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of -250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva.

A review of the role of graphene-based nanomaterials in tackling challenges posed by the COVID-19 pandemic

In 2020, the World Health Organization (WHO) declared a pandemic due to the emergence of the coronavirus disease (COVID-19) which had resulted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At present, the emergence of many new variants and mutants were found to be more harmful compared to the previous strains. As a result, research scientists around the world had devoted significant efforts to understand the mechanism, causes and transmission due to COVID-19 along with the treatment to cure these diseases. However, despite achieving several findings, much more was unknown and yet to be explored. Hence, along with these developments, it is also extremely essential to design effective systems by incorporating smart materials to battle the COVID-19. Therefore, several approaches have been implemented to combat against COVID-19. Recently, the graphene-based materials have been explored for the current COVID-19 and future pandemics due to its superior physicochemical properties, providing efficient nanoplatforms for optical and electrochemical sensing and diagnostic applications with high sensitivity and selectivity. Moreover, based on the photothermal effects or reactive oxygen species formation, the carbon-based nanomaterials have shown its potentiality for targeted antiviral drug delivery and the inhibitory effects against pathogenic viruses. Therefore, this review article sheds light on the recent progress and the most promising strategies related to graphene and related materials and its applications for detection, decontamination, diagnosis, and protection against COVID-19. In addition, the key challenges and future directives are discussed in detail for fundamental design and development of technologies based on graphene-based materials along with the demand aspects of graphene-based products and lastly, our personal opinions on the appropriate approaches to improve these technologies respectively.

Bioinformatics design of peptide binding to the human cardiac troponin I (cTnI) in biosensor development for myocardial infarction diagnosis

Cardiovascular disease has reached a mortality rate of 470,000 patients each year. Myocardial infarction accounts for 49.2% of these deaths, and the cTnI protein is a crucial target in diagnosing myocardial infarction. A peptide-based bioreceptor design using a computational approach is a good candidate to be developed for a rapid, effective, and selective detection method for cTnI although it is still lacking in study. Hence, to address the scientific gap, we develop a new candidate peptide for the cTnI biosensor by bioinformatics method and present new computational approaches. The sequential point mutations were made to the selected peptide to increase its stability and affinity for cTnI. Next, molecular docking was performed to select the mutated peptide, and one of the best results was subjected to the molecular dynamics simulation. Finally, the results showed that the best peptide showed the lowest affinity and good stability among other mutated peptide designs for interacting with the cTnI protein. In addition, the peptide has been tested to have a higher specificity towards cTnI than its major isomer, sTnI, through molecular docking and molecular dynamics simulation. Therefore, the peptide is considered a good potential bioreceptor for diagnosing myocardial infarction diseases.

An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8-140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor

Graphene-based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro/micro biomolecules still require more concerns and further research in order to enhance their applicability in biosensors, etc. Herein, an integrated method has been developed to predict the protein bioactivity performance when interacting with nanomaterials for protein-based biosensor. Molecular dynamics simulation and molecular docking technique were consolidated to investigate several nanomaterials: C60 fullerene, single-walled carbon nanotube, pristine graphene and graphene oxide, and their effect when interacting with protein. The adsorption behavior, secondary structure changes and protein bioactivity changes were simulated, and the results of protein activity simulation were verified in combination with atomic force spectrum, circular dichroism spectrum fluorescence and electrochemical experiments. The best quantification alignment between bioactivity obtained by simulation and experiment measurements was further explored. The two proteins, RNase A and Exonuclease III, were regarded as analysis model for the proof of concept, and the prediction accuracy of protein bioactivity could reach up to 0.98. The study shows an easy-to-operate and systematic approach to predict the effects of graphene-based nanomaterials on protein bioactivity, which holds guiding significance for the design of protein-related biosensors. In addition, the proposed prediction model is not limited to carbon-based nanomaterials and can be extended to other types of nanomaterials. This facilitates the rapid, simple, and low-cost selection of efficient and biosafe nanomaterials candidates for protein-related applications in biosensing and biomedical systems.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Carbon-based biosensors: Next-generation diagnostic tool for target-specific detection of SARS-CoV-2 (COVID-19)

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was declared a global pandemic in 2020. Having rapidly spread around the globe, with the emergence of new variants, there is a crucial need to develop diagnostic kits for its rapid detection. Since it validated accuracy and reliability, the reverse transcription polymerase chain reaction (RT-PCR) test has been declared the gold standard for disease detection. However, despite its reliability, the requirement of specialized facilities, reagents, and duration of a PCR run limits its usage for rapid detection. There is thus a continuous increase in the design and development of rapid, point-of-care (PoC), and cost-effective diagnostic kits. In this review, we discuss the potential of carbon-based biosensors for target-specific detection of coronavirus disease 19 (COVID-19) and present an overview of investigation within the timeframe of the last four years (2019-2022), which have developed novel platforms using carbon nanomaterial-based approaches for viral detection. The approaches discussed offer rapid, accurate, and cost-effective strategies for COVID-19 detection for healthcare personnel and research workers.

Nanotechnology in the COVID-19 era: Carbon-based nanomaterials as a promising solution

The Coronavirus Disease 2019 (COVID-19) pandemic has led to collaboration between nanotechnology scientists, industry stakeholders, and clinicians to develop solutions for diagnostics, prevention, and treatment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections. Nanomaterials, including carbon-based materials (CBM) such as graphene and carbon nanotubes, have been studied for their potential in viral research. CBM unique effects on microorganisms, immune interaction, and sensitivity in diagnostics have made them a promising subject of SARS-CoV-2 research. This review discusses the interaction of CBM with SARS-CoV-2 and their applicability, including CBM physical and chemical properties, the known interactions between CBM and viral components, and the proposed prevention, treatment, and diagnostics uses.

Investigation and comparison of graphene nanoribbon and carbon nanotube based SARS-CoV-2 detection sensors: An ab initio study

The rapid detection of SARS-CoV-2, the pathogen of the Covid-19 pandemic, is obviously of great importance for stopping the spread of the virus by detecting infected individuals. Here, we report the ab initio analysis results of graphene nanoribbon (GNR) and carbon nanotube (CNT) based SARS-CoV-2 detection sensors which are experimentally demonstrated in the literature. The investigated structures are the realistic molecular models of the sensors that are employing 1-pyrenebutyric acid N-hydroxysuccinimide ester as the antibody linker. Density functional theory in conjunction with non-equilibrium Green's function formalism (DFT-NEGF) is used to obtain the transmission spectra, current-voltage and resistance-voltage characteristics of the sensors before and after the attachment of the SARS-CoV-2 spike protein. The operation mechanism of the GNR and CNT based SARS-CoV-2 sensors are exposed using the transmission spectrum analysis. Moreover, it is observed that GNR based sensor has more definitive detection characteristics compared to its CNT based counterpart.

Nanotechnology meets immunology towards a rapid diagnosis solution: the COVID-19 outbreak challenge

The current COVID-19 pandemic presents one of the greatest challenges in human history. There is a consensus that the rapid and accurate diagnosis of COVID-19 directly affects procedures to avoid dissemination, promote treatments, and favor the prognosis of infected patients. This interdisciplinary study aims at designing new synthetic peptides inspired by the SARS-CoV-2 spike protein (SARS-CoV-2S) to produce rapid detection tests relying on nanomaterial-based colorimetric properties. Hence, in silico analyses of SARS-CoV-2S were performed using advanced bioinformatic simulation tools and algorithms. Five novel peptide sequences were proposed, and three were selected (P2, J4, and J5) based on their prospective reactivity against positive serum from naturally COVID-19-infected humans. Next, hyperimmune sera against the selected peptides were produced in rabbits. Concurrently, gold nanoparticles (AuNP) were synthesized using a green aqueous method under mild conditions through in situ reduction by trisodium citrate salt. They were extensively characterized by their morphological, physicochemical, and optical properties. The AuNPs demonstrated colloidal chemical stability in aqueous media, with an average size of approximately 29 nm (metallic core), and zeta potential before and after bioconjugation of -43 mV and -31 mV, respectively. Moreover, they presented an intense reddish-bluish color due to the surface plasmon resonance (SPR) effect, with maxima at λ = 525 nm and 536 nm, before and after bioconjugation, respectively, evidencing their applicability as colorimetric biomarkers for antigen-antibody immunoassay detection. To develop a rapid COVID-19 diagnosis test using lateral flow assay (LFA), semi-purified anti-SARS-CoV-2S sera against the three selected peptides were bioconjugated to the AuNPs as the highly optically sensitive agents using a considerably low antibody concentration (0.2 mg mL-1). All tested peptide sequences (P2, J4, and J5) induced antibodies capable of identifying the presence of SARS-CoV-2 virus inactivate suspensions (1 : 10, 1 : 100, or 1 : 1000 dilutions). For LFA positive test control, an anti-rabbit antibody was used. In summary, this research comprises several contributions and advances to the broad and multidisciplinary field of nanomaterials-based immunodiagnosis tools, encompassing: (a) the novelty of designing and synthesizing new immunogenic peptides inspired by SARS-CoV-2 virus epitopes using in silico bioinformatics; (b) the peptides induced the immune response in rabbit animal model producing hyperimmune serum; (c) the semi-purified hyperimmune serum rendered effective antibodies to detect SARS-CoV-2 virus in cell suspension; (d) colloidal gold nanoparticles were produced and bioconjugated to the antibodies for qualitative colorimetric detection. As the overall result of this study, it was designed, developed, produced, and validated a new simple, rapid, and sensitive LFA diagnostic test for the SARS-CoV-2 virus using a nanotechnology-based qualitative colorimetric assay, which can be envisioned as promising nanoplatforms for detecting other diseases.

Inhibition of SARS-CoV-2 pathogenesis by potent peptides designed by the mutation of ACE2 binding region

The outbreak of COVID-19 has resulted in millions of deaths. Despite all attempts that have been made to combat the pandemic, the re-emergence of new variants complicated SARS-CoV-2 eradication. The ongoing global spread of COVID-19 demands the incessant development of novel agents in vaccination, diagnosis, and therapeutics. Targeting receptor-binding domain (RBD) of spike protein by which the virus identifies host receptor, angiotensin-converting enzyme (ACE2), is a promising strategy for curbing viral infection. This study aims to discover novel peptide inhibitors against SARS-CoV-2 entry using computational approaches. The RBD binding domain of ACE2 was extracted and docked against the RBD. MMPBSA calculations revealed the binding energies of each residue in the template. The residues with unfavorable binding energies were considered as mutation spots by OSPREY. Binding energies of the residues in RBD-ACE2 interface was determined by molecular docking. Peptide inhibitors were designed by the mutation of RBD residues in the virus-receptors complex which had unfavorable energies. Peptide tendency for RBD binding, safety, and allergenicity were the criteria based on which the final hits were screened among the initial library. Molecular dynamics simulations also provided information on the mechanisms of inhibitory action in peptides. The results were finally validated by molecular docking simulations to make sure the peptides are capable of hindering virus-host interaction. Our results introduce three peptides P7 (RAWTFLDKFNHEAEDLRYQSSLASWN), P13 (RASTFLDKFNHEAEDLRYQSSLASWN), and P19 (RADTFLDKFNHEAEDLRYQSSLASWN) as potential effective inhibitors of SARS-CoV-2 entry which could be considered in drug development for COVID-19 treatment.

Peptide mediated colorimetric detection of SARS-CoV-2 using gold nanoparticles: a molecular dynamics simulation study

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has necessitated the development of a rapid, simple yet selective naked-eye detection methodology that does not require any advanced instrumental techniques. In this study, we report our computational findings on the detection of SARS-CoV-2 using peptide- functionalized gold nanoparticles (GNPs). The peptide has been screened from angiotensin-converting enzyme 2 (ACE2) receptor situated on the surface of the host cell membrane which interacts with the spike protein of SARS-CoV-2, resulting entry of the virus into the host cell. As a result, the peptide-functionalized GNPs possess excellent affinity towards the spikes of SARS-CoV-2 and readily get aggregated once exposed to SARS-CoV-2 antigen or virus. The stability of the peptides on the surface of GNPs and their interaction with the spike protein of the virus have been investigated using coarse-grained molecular dynamic simulations. The potential of mean force calculation of spike protein confirmed strong binding between peptide and receptor-binding domain (RBD) of spike protein. Our in silico results demonstrate the potential of the peptide-functionalized GNPs in the development of simple and rapid colorimetric biosensors for clinical diagnosis.

Advances in Nanomaterial-Based Platforms to Combat COVID-19: Diagnostics, Preventions, Therapeutics, and Vaccine Developments

The COVID-19 pandemic caused by the SARS-CoV-2, a ribonucleic acid (RNA) virus that emerged less than two years ago but has caused nearly 6.1 million deaths to date. Recently developed variants of the SARS-CoV-2 virus have been shown to be more potent and expanded at a faster rate. Until now, there is no specific and effective treatment for SARS-CoV-2 in terms of reliable and sustainable recovery. Precaution, prevention, and vaccinations are the only ways to keep the pandemic situation under control. Medical and scientific professionals are now focusing on the repurposing of previous technology and trying to develop more fruitful methodologies to detect the presence of viruses, treat the patients, precautionary items, and vaccine developments. Nanomedicine or nanobased platforms can play a crucial role in these fronts. Researchers are working on many effective approaches by nanosized particles to combat SARS-CoV-2. The role of a nanobased platform to combat SARS-CoV-2 is extremely diverse (i.e., mark to personal protective suit, rapid diagnostic tool to targeted treatment, and vaccine developments). Although there are many theoretical possibilities of a nanobased platform to combat SARS-CoV-2, until now there is an inadequate number of research targeting SARS-CoV-2 to explore such scenarios. This unique mini-review aims to compile and elaborate on the recent advances of nanobased approaches from prevention, diagnostics, treatment to vaccine developments against SARS-CoV-2, and associated challenges.

Antimicrobial peptides: A plausible approach for COVID-19 treatment

INTRODUCTION

Coronavirus disease 2019 (COVID-19), which emerged as a major public health threat, has affected >400 million people globally leading to >5 million mortalities to date. Treatments of COVID-19 are still to be developed as the available therapeutic approaches are not able to combat the virus causing the disease (severe acute respiratory syndrome coronavirus-2; SARS-CoV-2) satisfactorily. However, antiviral peptides (AVPs) have demonstrated prophylactic and therapeutic effects against many coronaviruses (CoVs).

AREAS COVERED

This review critically discusses various types of AVPs evaluated for the treatment of COVID-19 along with their mechanisms of action. Furthermore, the peptides inhibiting the entry of the virus by targeting its binding to angiotensin-converting enzyme 2 (ACE2) or integrins, fusion mechanism as well as activation of proteolytic enzymes (cathepsin L, transmembrane serine protease 2 (TMPRSS2), or furin) are also discussed.

EXPERT OPINION

Although extensively investigated, successful treatment of COVID-19 is still a challenge due to emergence of virus mutants. Antiviral peptides are anticipated to be blockbuster drugs for the management of this serious infection because of their formulation and therapeutic advantages. Although they may act on different pathways, AVPs having a multi-targeted approach are considered to have the upper hand in the management of this infection.

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.

A Comparison of EIS and QCM NanoMIP-Based Sensors for Morphine

In this work we have compared two different sensing platforms for the detection of morphine as an example of a low molecular weight target analyte. For this, molecularly imprinted polymer nanoparticles (NanoMIP), synthesized with an affinity towards morphine, were attached to an electrochemical impedance spectroscopy (EIS) and a quartz crystal microbalance (QCM) sensor. Assay design, sensors fabrication, analyte sensitivity and specificity were performed using similar methods. The results showed that the EIS sensor achieved a limit of detection (LOD) of 0.11 ng·mL-1, which is three orders of magnitude lower than the 0.19 µg·mL-1 achieved using the QCM sensor. Both the EIS and the QCM sensors were found to be able to specifically detect morphine in a direct assay format. However, the QCM method required conjugation of gold nanoparticles (AuNPs) to the small analyte (morphine) to amplify the signal and achieve a LOD in the µg·mL-1 range. Conversely, the EIS sensor method was labor-intensive and required extensive data handling and processing, resulting in longer analysis times (~30-40 min). In addition, whereas the QCM enables visualization of the binding events between the target molecule and the sensor in real-time, the EIS method does not allow such a feature and measurements are taken post-binding. The work also highlighted the advantages of using QCM as an automated, rapid and multiplex sensor compared to the much simpler EIS platform used in this work, though, the QCM method will require sample preparation, especially when a sensitive (ng·mL-1) detection of a small analyte is needed.

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.