Biomolecular Interactions Research Articles

Innovative Peptide-Based Plasmonic Optical Biosensor for the Determination of Cholesterol

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10–200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy.

Refined Protein-Sugar Interactions in the Martini Force Field.

Sugar molecules play important roles as mediators of biomolecular interactions in cellular functions, disease, and infections. Molecular dynamics simulations are an indispensable tool to explore these interactions at the molecular level. The large time and length scales involved frequently necessitate the use of coarse-grained representations, which heavily depend on the parametrization of sugar-protein interactions. Here, we adjust the sugar-protein interactions in the widely used Martini 2.2 force field to reproduce the experimental osmotic second virial coefficients between sugars and proteins. In simulations of two model proteins in glucose solutions with adjusted force field parameters, we observe weak protein-sugar interactions. The sugar molecules are thus acting mainly as crowding agents, in agreement with experimental measurements. The procedure to fine-tune sugar-protein interactions is generally applicable and could also prove useful for atomistic force fields.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

In Situ and Label-Free Quantification of Membrane Protein–Ligand Interactions Using Optical Imaging Techniques: A Review

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents.

Dimensional analysis of diffusive association rate equations

Diffusive adsorption/association is a fundamental step in almost all chemical reactions in diluted solutions, such as organic synthesis, polymerization, self-assembly, biomolecular interactions, electrode dynamics, catalysis, chromatography, air and water environmental dynamics, and social and market dynamics. However, predicting the rate of such a reaction is challenging using the equations established over 100 years ago. Several orders of magnitude differences between the theoretical predictions and experimental measurements for various systems, from self-assembled monolayers to protein-protein aggregations, make such calculations meaningless in many situations. I believe the major problem is that the time-dependent evolution curve of Fick’s gradient is an ideal assumption in most cases, and its slope is significantly overestimated. This paper digs into Fick’s gradient problem for 3D cases and provides a solution using the single-molecule diffusion probability density function discretely.

Fabrication of functional interface on magnetic beads via various amino acids and their application in chemiluminescent immunoassay as carrier

Magnetic polymer microspheres with superparamagnetism, high specificity, and monodispersity play a crucial role in the field of in vitro diagnostics. However, the surface modification process of magnetic beads is often complex, and it remains a significant challenge to prepare high-performance magnetic beads easily. To overcome these drawbacks, herein we fabricated functional interface on magnetic bead with the various amino acid via the ring-opening reaction of amino acids with epoxy groups, with attempt to produce carboxylated magnetic beads (MPS-GA) in a convenient way. Results indicate that when compared to other amino acids, the phenylalanine magnetic beads (MPS-GA1) developed in this study exhibit strong adsorption for mouse immunoglobulin (IgG), streptavidin (SA), and protamine (PA), with an IgG adsorption capacity of 53.5μg/mg and a coupling capacity of 52.5μg/mg. It is found that electrostatic forces and hydrophobic interactions are key factors influencing biomolecular interactions. Additionally, these magnetic beads can generate strong chemiluminescent signals, significantly reducing background levels by up to 99.7%. Therefore, the magnetic beads proposed in this paper can serve as carriers for chemiluminescent immunoassay (CLIA), providing new insights into the synthesis of high-quality magnetic bead.

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification.

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Dielectric Surface-Based Biosensors for Enhanced Detection of Biomolecular Interactions: Advances and Applications

Surface plasmon resonance (SPR) biosensors are extensively utilized for analyzing molecular interactions due to their high sensitivity and label-free detection capabilities. Recent innovations in surface-sensitive biosensors with dielectric surfaces address the inherent limitations associated with traditional gold surfaces, such as thermal effects and biocompatibility issues, which can impede broader applications. This review examines state-of-the-art biosensor configurations, including total internal reflection, optical waveguide, photonic crystal resonators, Bloch surface wave biosensors, and surface electrochemical biosensors, which can enhance analyte signals and augment the molecular detection efficiency at the sensor interface. These technological advancements not only improve the resolution of binding kinetics analysis and single-molecule detection but also extend the analytical capabilities of these systems. Additionally, this review explores prospective advancements in augmenting field enhancement and incorporating multimodal sensing functionalities, emphasizing the significant potential of these sophisticated biosensing technologies to profoundly enhance our understanding of molecular interactions.

Three-Dimensional (3D) Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Sensing Low-Concentration Molecules in Solution.

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics.

Fava Bean Protein Nanofibrils Modulate Cell Membrane Interfaces for Biomolecular Interactions as Unveiled by Atomic Force Microscopy.

Functional amyloids (protein nanofibrils, PNF) synthesized from plant sources exhibit unique physicochemical and nanomechanical properties that could improve food texture. While environmental factors affecting PNFs are well-known, scientific evidence on how cells (focus on the oral cavity) respond to them under physiological conditions is lacking. Self-assembled PNFs synthesized from fava bean whole protein isolate show a strong pH- and solvent-dependent morphology and elasticity modification measured by atomic force microscopy (AFM). After incubation of PNFs with an oral mechanosensitive model cell line at pH 7.3, difference in cell-surface roughness without significant changes in the overall cell elasticity were measured. The role of cell membrane composition on supported lipid bilayers was also tested, showing an increase in membrane elasticity with increasing fibril concentration and the possible impact of annular phospholipids in binding. Genetic responses of membrane proteins involved in texture and fat perception were detected at the mRNA level by RT-qPCR assay and both mechano- and chemosensing proteins displayed responses highlighting an interface dependent interaction. The outcomes of this study provide a basis for understanding the changing physicochemical properties of PNFs and their effect on flavor perception by altering mouthfeel and fat properties. This knowledge is important in the development of plant-based texture enhancers for sensory-appealing foods that require consumer acceptance and further promote healthy diets.

Fabrication and Simulation of Silicon Nanowire pH Sensor for Diabetes Mellitus Detection

Diabetes Mellitus (DM) is a disease failed to control the balance of blood sugar level due to lack of insulin thereby it effect human health. In Malaysia, there are around 3.9 millions people aged 18 years old and above have diabetes according to National Health and Morbidity Survey 2019. Silicon Nanowire is a nanostructure which has ultra-high sensitivity and non-radioactive that has potential given good performances when applied on pH sensor and biosensor. Silicon nanowire pH sensor and biosensor is an electronic sensor that investigated to improve the sensitivity and accuracy for detecting DM. This project consists of two parts, which are fabrication of silicon nanowire pH sensor and simulation of silicon nanowire biosensor as preliminary study. In fabrication, silicon nanowire of pH sensor is fabricated by conventional lithography process, reaction ion etching (RIE) and metallization to achieved the width of 100 nm silicon nanowire. The pH6, pH7, pH10 and DI water as analytes to analysis the current-voltage (I-V) characteristics of silicon nanowire pH sensor. In second part, the silicon nanowire biosensor as preliminary study is done simulation by Silvaco ATLAS devices simulator. The silicon nanowire with 30 nm in height and 20 nm in width of biosensor is designed and simulated to analyze the performance in terms of sensitivity. I-V characteristics of silicon nanowire biosensor according to different concentration of negative interface charge is determined. The negative interface charge represent as the Retinol Binding Protein 4 (RBP4) which is used to diagnose DM. The I-V characteristic based on the change in current, resistance and conductance to determine sensitivity. Lastly, the sensitivity of silicon nanowire pH sensor obtained 23.9 pS/pH while the sensitivity of simulated silicon nanowire biosensor obtained 3.91 nS/e.cm2. The results shown the more negative charge of concentration analyte attached on surface silicon nanowire has been accumulated more current flow from drain terminal to source terminal. It leads to the resistance becomes highest and obtained good sensitivity. In summary, the silicon nanowire pH sensor exhibited good performance and high sensitivity in detection pH level. The simulated silicon nanowire biosensor is capable of detecting biomolecular interactions charges to obtained high sensitive and accuracy result.

Residence time prediction in magnetically controlled biomolecular local rebinding-dissociation kinetics

The residence time of drug-target conjugates is a critical factor in drug screening and efficacy prediction. The local rebinding-dissociation kinetics gives insights into in-vivo drug-target interactions. A magnetic torque system (MTS) is designed to observe rebinding-dissociation kinetics for predicting residence time. The system utilizes an alternating magnetic field (AMF) to manipulate the magnetization motion of magnetically labeled biomolecules and the forces acting upon biomolecular bonds. The motion, sensed by a quartz crystal microbalance (QCM), reflects biomolecular interactions occurring at the particle surface. Meanwhile, the motion facilitates the separation of dissociated molecules from the surface, thereby obviating the necessity for fixed and mobile phases in common kinetics observations. The constant and static solution environment minimizes reagent consumption. The MTS was utilized to observe the local rebinding-dissociation of antibodies (PAB and MAB) to magnetic beads (MB) and to HER2 receptors. The residence times recorded by the MTS were larger than the results obtained via SPR method, due to the occurrences of rebinding-dissociation kinetics. Interaction behaviours can be meticulously regulated for varying affinities by modulating the intensity of magnetic field. A high intensity field (400 Oe) was applied for strong binding between antibody-MB (biotin-streptavidin), and a low intensity field (300 Oe) was applied for weak antigen-antibody interactions. An increase in AMF strength enhanced dissociation, with a shift from 300 Oe to 400 Oe resulting in a 1 ∼ 4-fold reduction in residence time. Overall, the MTS provides an interactive and customizable perspective on kinetics observations.

Electrochemical aptasensor for 2-amino-2-thiazoline-4-carboxylic acid (ATCA), a metabolite for cyanide poisoning

An alternative biomarker for assessing the cyanide levels in postmortem materials is crucial for investigating acute cyanide intoxication. Herein, an aptamer–ligand biorecognition system with high specificity was developed to detect acute cyanide poisoning via its secondary metabolite, 2-amino-2-thiazoline-4-carboxylic acid (ATCA). Potential aptamers were screened from a random library of 66-base single-stranded DNA using GO-SELEX, with individual aptamers being identified through single-stranded DNA sequencing. Molecular docking was employed to predict the affinity of these aptamers toward ATCA and selected counter-targets; these predictions were confirmed using thermodynamic analysis with an isothermal titration calorimeter. Owing to its label-free biomolecular binding interactions, Apt46 exhibited the highest affinity against ATCA and notable selectivity against structurally similar counter-targets. Thus, an amino-tagged Apt46 binding aptamer was attached to a carbon electrode modified with EDC–NHS-activated graphene oxide. The binding of Apt46 to ATCA was quantified by measuring current changes using differential pulse voltammetry. The aptasensor achieved a detection limit of 0.05 µg/mL and demonstrated suitability for detecting ATCA across various biological matrices, with the high recovery percentages ranging from 92.29 to 114.22%. Overall, the proposed ATCA aptasensor is promising for identifying ATCA metabolites in cases of acute cyanide exposure.

Recent advances in surface plasmon resonance for the detection of ovarian cancer biomarkers: a thorough review.

Early detection of ovarian cancer (OC) is crucial for effective management and treatment, as well as reducing mortality rates. However, the current diagnostic methods for OC are time-consuming and have low accuracy. Surface plasmon resonance (SPR) biosensors offer a promising alternative to conventional techniques, as they enable rapid and less invasive screening of various circulating indicators. These biosensors are widely used for biomolecular interaction analysis and detecting tumor markers, and they are currently being investigated as a rapid diagnostic tool for early-stage cancer detection. Our main focus is on the fundamental concepts and performance characteristics of SPR biosensors. We also discuss the latest advancements in SPR biosensors that enhance their sensitivity and enable high-throughput quantification of OC biomarkers, including CA125, HE4, CEA, and CA19-9. Finally, we address the future challenges that need to be overcome to advance SPR biosensors from research to clinical applications. The ultimate goal is to facilitate the translation of SPR biosensors into routine clinical practice for the early detection and management of OC.

Covalent modification of protein by chemical probe in living cells for structural and interaction studies.

Cellular activities are predominantly carried out by proteins that can dynamically adopt different structural conformations and differentially interact with other biomolecules according to cellular needs. Chemical probes are small molecules used to selectively interact and modulate the activities of specific proteins to study their functions such as the validation of potential drug targets. The remarkable performance of AlphaFold algorithms in the prediction of protein structures has pivoted interest toward elucidating the intracellular dynamics of protein structural conformation where covalent modification of proteins by chemical probes could be used to shed light upon. However, due to the barrier to entry by cell membrane and the general unfavorable reactive conditions of the intracellular environment, most studies using reactive chemical probes are still conducted on purified proteins and cell lysates. Nevertheless, recent progresses have been made in designing chemical probes with improved membrane permeability, stability and reactivity. This paper surveys the literature on recent advancements in membrane-permeable chemical probes and their applications with protein mass spectrometry for the intracellular studies of protein structural conformations and biomolecular interactions.

Metal-free ultrathin terahertz absorber with independently tunable dual narrow bands

Abstract A technique is introduced to precisely control the resonance behaviour of a metal-free graphene-based terahertz absorber by independently tuning dual resonant peaks. The proposed ultrathin absorber features a multilayer configuration, with two isolated resonator layers of patterned graphene on dielectric (SiO2) substrates, and a thin graphite sheet at the bottom serving as a reflector. The stacked arrangement enables independent tunability of the high-absorptivity resonant peaks at 7.33THz and 9.34THz. The structure, with a thickness of just λ/15 of the free-space wavelength, offers a compact design suitable for space-constrained applications. Its symmetrical geometry ensures polarization insensitivity and stable performance for incident angles up to 60°. Simulated results, analysed via CST Studio and validated with an Equivalent Circuit Model (ECM), demonstrate excellent thermal stability. Furthermore, the narrowband response of the proposed absorber improves its sensitivity for refractive index variations induced by biomolecular interactions validating its suitability in biosensing applications. The absorber demonstrates peak absorption across both the resonant frequencies with an analyte optimised at 1.5μm thickness. Sensitivity levels of 1.1THz/RIU and 1.05THz/RIU along with figure-of-merit (FOM) values of 2.11 and 2.23. are recorded for the lower and upper bands, respectively. The absorber offers enhanced selectivity owing to low values of full-width at half maximum (FWHM). High Q-factors of 12.85 and 19.3, confirm its strong potential for refractive index sensing. 

Fluorescence-Based Study of Oligonucleotide Interactions With Recombinant Proteins: Insulin, Interferon α2-β, Somatotropin, and Their Receptors

Background. Oligonucleotides (OLNs) can participate in a wide range of protein-ligand interactions and perform numerous cellular functions by forming structures that enable specific interactions with DNA, RNA, and proteins, what is crucial for many biological processes. Advances in understanding these interactions could lead to the development of new technologies for treating various diseases. However, the mechanism of interaction between proteins and OLNs is complex and still requires detailed study. More research is needed to fully elucidate this process and enhance our understanding of these biomolecular interactions. Objective. The aim of this study was to synthesize, purify, and investigate the interaction of OLNs with recombinant signaling proteins interferon α2-β and insulin with their receptors and somatropin by assessing binding strength using fluorescence spectroscopy. Methods. The interactions were analyzed using the Stern–Volmer equation in both general and modified forms, as well as the Hill equation. OLNs were synthesized via the solid-phase phosphoramidite method, purified through solid-phase extraction, and subsequently verified with a spectrophotometer. Results. Fluorometric titration revealed that OLNs bind to proteins within the medium affinity range, forming non-fluorescent complexes, with the most active interactions observed with shorter OLN. Positive cooperative binding of interferon to G20 and T20, and negative cooperative binding of insulin to C20 and A20, were identified. Additionally, negative cooperative binding of somatropin to C20 was observed. Conclusions. The study demonstrated the interaction between OLNs and recombinant signaling proteins and receptors through various binding mechanisms, which could potentially affect their conformation and biolo­gical activity. These findings have implications for the therapeutic use of OLNs in the context of signaling proteins and receptors.

Plasmonic Nanogap-Enhanced Tunable Three-Dimensional Nanoframes in Application to Clinical Diagnosis of Alzheimer's Disease.

Advancements in nanotechnology led to significant improvements in synthesizing plasmon-enhanced nanoarchitectures for biosensor applications, and high-yield productivity at low cost is vital to step further into medical commerce. Metal nanoframes via wet chemistry are gaining attention for their homogeneous structure and outstanding catalytic and optical properties. However, nanoframe morphology should be considered delicately when brought to biosensors to utilize its superior characteristics thoroughly, and the need to prove its clinical applicability still remains. Herein, we controlled the frameworks of double-walled nanoframes (DWFs) precisely via wet chemistry to construct a homogeneous plasmon-enhanced nanotransducer for localized surface plasmon resonance biosensors. By tuning the physical properties considering the finite-difference time-domain simulation results, biomolecular interactions were feasible in the electromagnetic field-enhanced nanospace. As a result, DWF10 exhibited a 10-fold lower detection limit of 2.21 fM compared to DWF14 for tau detection. Further application into blood-based clinical and Alzheimer's disease (AD) diagnostics, notable improvement in classifying mild cognitive impairment patients against healthy controls and AD patients, was demonstrated along with impressive AUC values. Thus, in response to diverse detection methods, optimizing nanoframe dimensions such as nanogap and frame thickness to maximize sensor performance is critical to realize future POCT diagnosis.

Insights into the Theranostic Activity of Nonoxido VIV: Lysosome-Targeted Anticancer Metallodrugs.

Developing new anticancer agents can be useful, with the ability to diagnose and treat cancer worldwide. Previously, we focused on examining the effects of nonoxidovanadium(IV) complexes on insulin mimetic and cytotoxicity activity. In this study, in addition to the cytotoxic activity, we evaluated their bioimaging properties. This study investigates the synthesis of four stable nonoxido VIV complexes [VIV(L1-4)2] (1-4) using aroylhydrazone ligands (H2L1-4) and their full characterization in solid state and the solution phase stability using various physicochemical techniques. The biomolecular (DNA/HSA) interaction of the complexes was evaluated by using conventional methods. The in vitro cytotoxicity of 1-4 was studied against A549 and LN-229 cancer cell lines and found that drug 2 displayed the highest activity among the four. Since 1-4 are fluorescently active, live cell imaging was used to evaluate their cellular localization activity. Complexes specifically target the lysosome and damage lysosome integrity by producing an excessive amount (9.7-fold) of reactive oxygen species (ROS) compared to the control, which may cause cell apoptosis. Overall, this study indicates that 2 has the greatest potential for the development of multifunctional theranostic agents that combine imaging capabilities and anticancer properties of nonoxidovanadium(IV)-based metallodrugs.

Innovative pedagogy: nurturing excellence through an effective student-centered approach in medicinal chemistry courses

Medicinal chemistry educators face challenges in providing students with essential tools to ensure an ideal learning environment. The suspension of on-site classes during the COVID-19 pandemic introduced further obstacles. This study investigated learning outcomes in an online versus on-site medicinal chemistry course in Saudi Arabia, focusing on drug classes and biomolecular interactions. We utilized a causal-comparison study method using data from first-year Pharmacy students (P1) enrolled in the Medicinal Chemistry I course (semester 1) during the 2019–2020 (on-site class) and 2020–2021 (online class) academic years. Data were analyzed using JMP software. The online course utilized advanced virtual technology such as Blackboard Collaborate Ultra and other computational platforms. Data representing the performance of 196 students were obtained. The analyses of the assessment scores showed a mean of 80.45 ± 12.65 for the online class group compared with 71.90 ± 9.50 for the on-site class group. Most online students showed improvement in their active engagement and learning experience. Current data support the argument that there is no apparent difference between outcomes for online versus on-site medicinal chemistry courses. Furthermore, advanced tools and their application within the online instructional methodology play a critical role in the overall learning experience.