Biomolecular Interactions Research Articles

A prospective evaluation of microscale thermophoresis coupled with mass spectrometry using the “RGB_ex-ante” model

The evaluation of greenness and whiteness is usually made retrospectively (ex-post) – for a developed and validated methodology ready for use. However, it can also be made ex-ante, i.e. taking into account the potential pros and cons that have not yet been experimentally confirmed. The RGB_ex-ante model presented here is a simple assessment framework to help determine whether developing a new specific methodology is worth investing effort. We used it for a prospective evaluation of a new foreseen technique – microscale thermophoresis (MST) coupled online with mass spectrometry (MST-MS). Concurrently, it was demonstrated that biophysical techniques dedicated to the analysis of biomolecular interactions, such as MST, can and should be assessed in terms of greenness and whiteness on an equal footing with chromatography, spectroscopy and other quantitative analysis techniques. We report the original preliminary research results confirming the possibility of combining online MST and MS instruments, which, however, do not yet constitute evidence for analyzing equilibrium processes in this system. For that reason, an ex-ante evaluation was performed. The obtained greenness and whiteness scores suggest that the MST-MS technique would likely be greener than the conventional offline approach, but not whiter. Despite some potential benefits related to all attributes (red, green and blue criteria), the implementation of MST-MS coupling would entail a number of limitations that overall offset the expected merits. The proposed evaluation model turned out to be a simple and helpful tool that is worth using prior developing new methods, procedures and experimental techniques in analytical laboratory and beyond.

Tapered optical fiber LRSPR biosensor based on gold nanoparticle amplification for label-free BSA detection

Surface plasmon resonance (SPR)-based fiber optic sensors are widely used in biosensing and associated analysis of biomolecular interactions. However, the limited penetration depths of conventional SPR (CSPR, <300 nm) and localized SPR (LSPR, <200 nm) restrict their applications in specific biosensing as the surface immobilized biomolecules, i. e. antibody or aptamer, will shield the plasma wave from the target. To solve the problem, an optical fiber long range SPR (LRSPR) biosensor based on Au nanoparticles (AuNPs) amplification is developed for bovine serum albumin (BSA) detection. The single mode fiber spliced between two multimode fibers is firstly tapered for the enhancement of evanescent field, then deposited with Al2O3-Ag-Au multilayers to stimulate LRSPR and realize micron-scale larger penetration depth, finally modified with the BSA antibody by covalent bonding to specifically recognize the target biomolecule. AuNPs are introduced to improve the sensitivity after the BSA antigen-antibody combination is accomplished, taking advantage of the plasma hybridization with LRSPR and their high mass ratio. A good log-linear spectral response is obtained at the BSA concentration range of 1 ng/mL-10 mg/mL, with high sensitivity of 19.46 nm/log(ng/mL) and low limit of detection (LOD) of 0.3 ng/mL. The sensitivity of LRSPR is improved 5.6-fold and the minimum detectable BSA concentration is reduced 4 orders of magnitude by introducing AuNPs amplification. Moreover, the designed sensor shows the merits of high specificity, good stability and fast response, making it of great potential for trace biosensing applications.

Biolayer interferometry for measuring the kinetics of protein-protein interactions and nanobody binding.

Protein-protein interactions underpin nearly all biological processes, and understanding the molecular mechanisms that govern these interactions is crucial for the progress of biomedical sciences. The emergence of artificial intelligence-driven computational tools can help reshape the methods of structural biology; however, model data often require empirical validation. The large scale of predictive modeling data will therefore benefit from optimized methodologies for the high-throughput biochemical characterization of protein-protein interactions. Biolayer interferometry is one of very few approaches that can determine the rate of biomolecular interactions, called kinetics, and, of the commonly available kinetic measurement techniques, it is the most suitable for high-throughput experimental designs. Here we provide step-by-step instructions on how to perform kinetics experiments using biolayer interferometry. We further describe the basis and execution of competition and epitope binning experiments, which are particularly useful for antibody and nanobody screening applications. The procedure requires 3 h to complete and is suitable for users with minimal experience with biochemical techniques.

Boltz-1 Democratizing Biomolecular Interaction Modeling.

Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this paper, we introduce Boltz-1, an open-source deep learning model incorporating innovations in model architecture, speed optimization, and data processing achieving A lpha F old 3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates a performance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training and inference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

MOHGCN: A trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis

With the advancement of cutting-edge sequencing methodologies, the integration of multi-omics data provides invaluable opportunities for researchers to study complex diseases from a molecular perspective while at the same time being challenged by the deployment of safety-critical applications such as computer-aided diagnostics. However, existing methods in multi-omics data integration primarily explore interactions between omics or samples, neglecting high-order interaction information among biomolecules specific to certain diseases. In this work, we propose MOHGCN, a trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis, aiming to maximize the utilization of biomolecular interactions in patients with specific diseases for precise diagnosis to enhance the model’s credibility. In the approach, we constructed a heterogeneous graph of samples and genes and devised the HGCN graph convolution model specifically tailored to the sample–gene heterogeneous graph. Concurrently, techniques such as trustworthy attention weights and self-attention mechanisms were incorporated to unveil relationships between different omics, facilitating the efficient integration of multi-omics data. Through comprehensive experimentation on four publicly available multi-omics medical datasets, our proposed framework consistently demonstrates superior performance across various classification tasks. Simultaneously, the experimental results substantiate the model’s effectiveness in extracting features from multi-omics data and unveiling latent associations among different omics.

Surface plasmon resonance spectrometer in the double prism configuration: Fast characterization of the thickness and dielectric constant dispersion of thin films

Surface plasmon resonance (SPR)-based sensors are widely used to measure thin film thickness, material dielectric constants, biomolecular interactions, and more. However, conventional monochromatic setups limit the simultaneous determination of dielectric constant dispersion and film thickness. To address this, we developed a fully automated wavelength-resolved SPR using a double-prism Kretschmann configuration. Combining this setup with a computational approach based on Fresnel equations and the Nelder-Mead optimization method, we introduced a novel technique to simultaneously determine the dispersion (520–1000 nm) of the real and imaginary components of the dielectric constant of thin films directly from SPR curves. As far as we know, this is the first time an SPR spectrometer has accomplished such a remarkable achievement. As a proof of concept for biosensor applications, we determined the thickness and dielectric constant dispersion of gold and bovine serum albumin thin film. These thicknesses were validated through atomic force microscopy, and the dielectric constants aligned closely with literature values obtained by ellipsometry. The developed SPR system enables precise measurements of these parameters within a short time (10 minutes), paving the way for future applications in advanced sensors and biosensors.

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.

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.

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.

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 53.5 μg/mg and a coupling capacity of 52.5 μg/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.

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.

COCOMO2: A coarse-grained model for interacting folded and disordered proteins.

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parameterized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multi-scale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

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.

Ultra-sensitive refractive index detection with gold-coated PCF-based SPR sensor

This study introduces an innovative photonic crystal fiber (PCF) surface plasmon resonance (SPR) biosensor, notable for its sophisticated design and exceptional performance, utilizing gold as the plasmonic material. The sensor offers an expanded detection range and outstanding sensitivity, operating within a refractive index range of 1.27 to 1.43. It achieves a remarkable wavelength sensitivity of 157,000 nm/RIU with a resolution of 6.37 × 10⁻⁷ RIU and an amplitude sensitivity of 1263 RIU⁻¹ with a resolution of 7.92 × 10⁻⁵ RIU. Additionally, it boasts a figure of merit (FOM) of 1648, underscoring its superior capability in detecting biomolecular interactions. The suggested design is numerically analysed using the finite element method (FEM) of a gold-coated PCF-based SPR sensor designed for detecting changes in the refractive index (RI) within the range of 1.27 to 1.43. This range is related to various biological and chemical samples, including proteins, DNA, and biochemical analytes typically used in biomedical diagnostics and environmental monitoring. The integration of gold plasmonic elements enhances the sensor's sensitivity and stability while extending its detection capabilities across a broader range, allowing for versatile applications in biomedicine, environmental monitoring, and beyond. This research significantly advances optical biosensing technologies by offering a robust platform for high-precision detection across diverse domains. The sensor's advanced design and performance make it an invaluable tool for applications requiring accurate and reliable detection, driving innovations in areas such as biomedical diagnostics, environmental monitoring, food safety, and pharmaceutical quality control.

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.

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. &amp;#xD;