Biomolecular Interactions Research Articles

Pleiotropic Nanostructures Built from l-Histidine Show Biologically Relevant Multicatalytic Activities.

The essential amino acid histidine plays a central role in the manifestation of several metabolic processes, including protein synthesis, enzyme-catalysis, and key biomolecular interactions. However, excess accumulation of histidine causes histidinemia, which shows brain-related medical complications, and the molecular mechanism of such histidine-linked complications is largely unknown. Here, we show that histidine undergoes a self-assembly process, leading to the formation of amyloid-like cytotoxic and catalytically active nanofibers. The kinetics of histidine self-assembly was favored in the presence of Mg(II) and Co(II) ions. Molecular dynamics data showed that preferential noncovalent interactions dominated by H-bonds between histidine molecules facilitate the formation of histidine nanofibers. The histidine nanofibers induced amyloid cross-seeding reactions in several proteins and peptides including pathogenic Aβ1-42 and brain extract components. Further, the histidine nanofibers exhibited oxidase activity and enhanced the oxidation of neurotransmitters. Cell-based studies confirmed the cellular internalization of histidine nanofibers in SH-SY5Y cells and subsequent cytotoxic effects through necrosis and apoptosis-mediated cell death. Since several complications including behavioral abnormality, developmental delay, and neurological disabilities are directly linked to abnormal accumulation of histidine, our findings provide a foundational understanding of the mechanism of histidine-related complications. Further, the ability of histidine nanofibers to catalyze amyloid seeding and oxidation reactions is equally important for both biological and materials science research.

Insights into the binding of buspirone to human serum albumin using multi-spectroscopic and molecular docking techniques

Buspirone is an anxiolytic drug that plays a significant role in managing anxiety disorders and alleviating their symptoms as well. Several techniques were utilized to study the interaction between buspirone and human serum albumin under physiological conditions, including UV–vis absorption spectroscopy, fluorescence emission spectroscopy, circular dichroism, Fourier transform infrared spectroscopy (FT-IR), equilibrium dialysis, and molecular docking. The results of this study demonstrated that buspirone quenched the intrinsic fluorescence of human serum albumin through a mixed mechanism. Moreover, the binding constants (Kb), the quenching constants (Ksv), and thermodynamic parameters were calculated at various temperatures. The binding process of buspirone to human serum albumin showed a cooperative binding pattern, confirmed by the Scatchard diagram and Hill coefficient. Molecular docking results showed that buspirone interacted with the IIA, IIIA, and IIB subdomains of human serum albumin and slightly changed its conformation. It was also found that hydrophobic forces played a major role in this interaction. This study consequently proves that BSH as a drug can be transported by blood albumin. Additionally, due to its ratiometric response in absorbance upon binding to a biological target, HSA can be used as a molecular probe to follow biomolecular interactions.

A REVIEW ON MOLECULAR DOCKING AND ITS APPLICATION

Molecular Docking is powerful computer strategy, critical in drug development, structural biology, and biomolecular interaction researchproviding a thorough grasp of its importance in modern scientific study. Molecular docking involves forecasting how a small molecule, often a potential medication, will interact with a target biomolecule like DNA or a protein. This process examines the spatial and energetic compatibility of the ligand with the active site of the receptor, assisting in the discovery of new drug candidates, refining existing compounds, and understanding the intricate interactions between drugs and receptors. Molecular Docking a computational tool that evaluates and ranks different ligand-receptor conformations based on their binding energies. An accurate scoring function is imperative for distinguishing high-affinity ligands from low-affinity ones, this makes it possible to identify potential medication candidates for validation in experiments. Molecular docking finds application in various domains of Drug Progress, comprisingstructure-based medication development, virtual screening,lead optimization.When creating a medicine based on structure, the target biomolecules three-dimensional structure is employed to guide the ligand selection that will communicate with the region that is active. Virtual examination accelerates the discovery of potential drug properties by rapidly evaluating large compound libraries. Lead optimization, on the other hand, is facilitated through iterative docking studies aimed at enhancing binding affinity and pharmacological properties. The field of molecular docking has seen a proliferation of diverse techniques designed to address the unique challenges of structure-based drug design and biomolecular interaction analysis. This report provides an in-depth examination of thedistinct methodologies of docking, its principles, applications, and their importance in advancing our understanding of molecular interactions in different contexts.The diversity of docking methods arises from the need to accommodate different types of molecules, target structures, and research objectives.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Bovine serum albumin uptake and polypeptide disaggregation studies of hypoglycemic ruthenium(II) uracil Schiff-base complexes

Our prior studies have illustrated that the uracil ruthenium(II) diimino complex, [Ru(H3ucp)Cl(PPh3)] (1) (H4ucp = 2,6-bis-((6-amino-1,3-dimethyluracilimino)methylene)pyridine) displayed high hypoglycemic effects in diet-induced diabetic rats. To rationalize the anti-diabetic effects of 1, three new derivatives have been prepared, cis-[Ru(bpy)2(urdp)]Cl2 (2) (urdp = 2,6-bis-((uracilimino)methylene)pyridine), trans-[RuCl2(PPh3)(urdp)] (3), and cis-[Ru(bpy)2(H4ucp)](PF6)2 (4). Various physicochemical techniques were utilized to characterize the structures of the novel ruthenium compounds. Prior to biomolecular interactions or in vitro studies, the stabilities of 1–4 were monitored in anhydrous DMSO, aqueous phosphate buffer containing 2% DMSO, and dichloromethane (DCM) via UV–Vis spectrophotometry. Time-dependent stability studies showed ligand exchange between DMSO nucleophiles and chloride co-ligands of 1 and 3, which was suppressed in the presence of an excess amount of chloride ions. In addition, the metal complexes 1 and 3 are stable in both DCM and an aqueous phosphate buffer containing 2% DMSO. In the case of compounds 2 and 4 with no chloride co-ligands within their coordination spheres, high stability in aqueous phosphate buffer containing 2% DMSO was observed. Fluorescence emission titrations of the individual ruthenium compounds with bovine serum albumin (BSA) showed that the metal compounds interact non-discriminately within the protein's hydrophobic cavities as moderate to strong binders. The metal complexes were capable of disintegrating mature amylin amyloid fibrils. In vivo glucose metabolism studies in liver (Chang) cell lines confirmed enhanced glucose metabolism as evidenced by the increased glucose utilization and glycogen synthesis in liver cell lines in the presence of complexes 2–4.

Detection of insulin oligomeric forms by a novel surface plasmon resonance-diffusion coefficient based approach.

Insulin is commonly used to treat diabetes and undergoes aggregation at the site of repeated injections in diabetic patients. Moreover, aggregation is also observed during its industrial production and transport and should be avoided to preserve its bioavailability to correctly adjust glucose levels in diabetic patients. However, monitoring the effect of various parameters (pH, protein concentration, metal ions, etc.) on the insulin aggregation and oligomerization state is very challenging. In this work, we have applied a novel Surface Plasmon Resonance (SPR)-based experimental approach to insulin solutions at various experimental conditions, monitoring how its diffusion coefficient is affected by pH and the presence of metal ions (copper and zinc) with unprecedented sensitivity, precision, and reproducibility. The reported SPR method, hereby applied to a protein for the first time, besides giving insight into the insulin oligomerization and aggregation phenomena, proved to be very robust for determining the diffusion coefficient of any biomolecule. A theoretical background is given together with the software description, specially designed to fit the experimental data. This new way of applying SPR represents an innovation in the bio-sensing field and expanding the potentiality of commonly used SPR instruments well over the canonical investigation of biomolecular interactions.

Isothermal titration calorimetry and surface plasmon resonance methods to probe protein–protein interactions

Isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) are two commonly used methods to probe biomolecular interactions. ITC can provide information about the binding affinity, stoichiometry, changes in Gibbs free energy, enthalpy, entropy, and heat capacity upon binding. SPR can provide information about the association and dissociation kinetics, binding affinity, and stoichiometry. Both methods can determine the nature of protein–protein interactions and help understand the physicochemical principles underlying complex biochemical pathways and communication networks. This methods article discusses the practical knowledge of how to set up and troubleshoot these two experiments with some examples.

Hexagonal Boron Nitride Spacers for Fluorescence Imaging of Biomolecules

AbstractFluorescence imaging is an invaluable tool to investigate biomolecular dynamics, mechanics, and interactions in aqueous environments. Two‐dimensional materials offer large‐area, atomically smooth surfaces for wide‐field biomolecule imaging. Despite the success of graphene for on‐chip biosensing and biomolecule manipulation, its strong fluorescence‐quenching properties pose a challenge for biomolecular investigations that are based on direct optical readouts. Here, we employ few‐layer hexagonal boron nitride (hBN) as a precisely tailorable fluorescence spacer between labelled lipid membranes and graphene substrates. By stacking high‐quality hBN crystals in the 10–20 nm thickness range on monolayer graphene, we observe distance‐dependent fluorescence intensity variations. Remarkably, with hBN spacers as thin as 20 nm, the fluorescence intensity is comparable to bare SiO2/Si substrates, while the intensity was reduced to 60 % and 80 % with ~10 nm and ~16 nm hBN thicknesses respectively. We confirm that pre‐determined hBN thicknesses can be employed to control the non‐radiative energy transfer properties of graphene, with fluorescence quenching following a d−4 distance‐dependent behaviour. This seamless integration of electronically active and dielectric van der Waals materials into vertical heterostructures enables multifunctional platforms addressing the manipulation, localization, and visualization of biomolecules for fundamental biophysics and biosensing applications.

Microscale Thermophoresis Analysis of Membrane Proteins

Microscale Thermophoresis Analysis of Membrane Proteins

Variational implicit solvation with Legendre-transformed Poisson–Boltzmann electrostatics

The variational implicit-solvent model (VISM) is an efficient approach to biomolecular interactions, where electrostatic interactions are crucial. The total VISM free energy of a dielectric boundary (i.e. solute–solvent interface) consists of the interfacial energy, solute–solvent interaction energy and dielectric electrostatic energy. The last part is the maximum value of the classical and concave Poisson–Boltzmann (PB) energy functional of electrostatic potentials, with the maximizer being the equilibrium electrostatic potential governed by the PB equation. For the consistency of energy minimization and computational stability, here we propose alternatively to minimize the convex Legendre-transformed Poisson–Boltzmann (LTPB) electrostatic energy functional of all dielectric displacements constrained by Gauss’ Law in the solute region. Both integrable and discrete solute charge densities are treated, and the duality of the LTPB and PB functionals is established. A penalty method is designed for the constrained minimization of the LTPB functional. In application to biomolecular interactions, we minimize the total VISM free energy iteratively, while in each step of such iteration, minimize the LTPB energy. Convergence of such a min–min algorithm is shown. Our numerical results on the solvation of a single ion indicate that the LTPB performs better than the PB formulation, providing possibilities for efficient biomolecular simulations.

Re-Imagining Surface Plasmon Resonance for Characterizing Biomolecular Interactions

Re-Imagining Surface Plasmon Resonance for Characterizing Biomolecular Interactions

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

Conductometric immunosensor for specific Escherichia coli O157:H7 detection on chemically funcationalizaed interdigitated aptasensor

Escherichia coli O157:H7 is a strain of Escherichia coli known for causing foodborne illness through the consumption of contaminated or raw food. To detect this pathogen, a conductometric immunosensor was developed using a conductometric sensing approach. The sensor was constructed on an interdigitated electrode and modified with a monoclonal anti-Escherichia coli O157:H7 aptamer. A total of 200 electrode pairs were fabricated and modified to bind to the target molecule replica. The binding replica, acting as the bio-recognizer, was linked to the electrode surface using 3-Aminopropyl triethoxysilane. The sensor exhibited excellent performance, detecting Escherichia coli O157:H7 in a short time frame and demonstrating a wide detection range of 1 fM to 1 nM. Concentrations of Escherichia coli O157:H7 were detected within this range, with a minimum detection limit of 1 fM. This innovative sensor offers simplicity, speed, high sensitivity, selectivity, and the potential for rapid sample processing. The potential of this proposed biosensor is particularly beneficial in applications such as drug screening, environmental monitoring, and disease diagnosis, where real-time information on biomolecular interactions is crucial for timely decision-making and where cross-reactivity or interference may compromise the accuracy of the analysis.

Role of tungsten disulfide quantum dots in specific protein-protein interactions at air-water interface.

The intriguing network of antibody-antigen (Ab-Ag) interactions is highly governed by environmental perturbations and the nature of biomolecular interaction. Protein-protein interactions (PPIs) have potential applications in developing protein-adsorption-based sensors and nano-scale materials. Therefore, characterizing PPIs in the presence of a nanomaterial at the molecular level becomes imperative. The present work involves the investigation of antiferritin-ferritin (Ab-Ag) protein interactions under the influence of tungsten disulfide quantum dots (WS2 QDs). Isothermal calorimetry and contact angle measurements validated the strong influence of WS2 QDs on Ab-Ag interactions. The interfacial signatures of nano-bio-interactions were evaluated using sum frequency generation vibration spectroscopy (SFG-VS) at the air-water interface. Our SFG results reveal a variation in the tilt angle of methyl groups by ∼12° ± 2° for the Ab-Ag system in the presence of WS2 QDs. The results illustrated an enhanced ordering of water molecules in the presence of QDs, which underpins the active role of interfacial water molecules during nano-bio-interactions. We have also witnessed a differential impact of QDs on Ab-Ag by raising the concentration of the Ab-Ag combination, which showcased an increased inter-molecular interaction among the Ab and Ag molecules and a minimal influence on the methyl tilt angle. These findings suggest the formation of stronger and ordered Ab-Ag complexes upon introducing WS2 QDs in the aqueous medium and signify the potentiality of WS2 QDs relevant to protein-based sensing assays.

Investigating the binding interaction of quinoline yellow with bovine serum albumin and anti-amyloidogenic behavior of ferulic acid on QY-induced BSA fibrils

Protein aggregation induces profound changes in the structure along with the conformation of the protein, and is responsible for the pathogenesis of a number of neurodegenerative conditions such as Huntington’s, Creutzfeldt–Jacob, Type II diabetes mellitus, Alzheimer’s, etc. Numerous multi-spectroscopic approaches and in-silico experiments were utilized to investigate BSA’s biomolecular interaction and aggregation in the presence of quinoline yellow. The present research investigation evaluated the interaction of BSA with the food colorant (QY) at two different pH (7.4 and 2.0). The development of the BSA-QY complex was established with UV visible and fluorescence spectroscopy. The quenching of fluorescence upon the interaction of BSA with QY revealed the static nature of quenching mechanism. The Kb value obtained from our result is 4. 54 × 10−4 M−1. The results from the competitive site marker study infer that quinoline yellow is binding with the sub-domain IB of bovine serum albumin, specifically on site III. Three-dimensional fluorescence and synchronous fluorescence spectroscopy were applied for monitoring the alterations in the microenvironment of BSA upon the addition of quinoline yellow. The results from turbidity and RLS studies showed that higher concentrations of QY (80–400 µM) triggered bovine serum albumin (BSA) aggregation at pH 2.0. At pH 7.4, QY couldn’t manage to trigger bovine serum albumin aggregation, perhaps because of the repulsion between negatively charged dye (QY) and anionic bovine serum albumin. The results from far-UV CD, Congo Red, and scanning electron microscopy implicate that the QY-induced aggregates exhibit amyloid fibril-like structures. Molecular docking results revealed that hydrophobic interactions, hydrogen bonding, and Pi-Sulfur interactions contribute to QY-induced aggregation of BSA. Further, the amyloid inhibitory potential of ferulic acid (FA), a phenolic acid on QY-induced aggregation of BSA, has also been assessed. The QY-induced amyloid fibrils are FA-soluble, as confirmed by turbidity, RLS, and far-UV CD studies. Far-UV CD results showed that FA retains α helix and inhibits cross β sheet formation when the BSA samples were pre-incubated with increasing concentrations of FA (0–500 µM). Our findings conclude that QY dye successfully stimulates BSA aggregation, but ferulic acid inhibits QY-induced aggregation of BSA. Thus, FA can serve as a therapeutic agent and can help in the treatment of various amyloid-related conditions.

Poly-Thionine/ SWCNT Nanocomposite Coated Electrochemical Sensor for Determination of Vitamin C

Background: The electrochemical sensors convert biological or chemical information, such as analyte concentration or a biomolecular (biochemical receptor) interaction, into electrical signals. In this paper, we describe the development of a poly-thionine/ single-walled carbon nanotube (P-Th/SWCNT) composite for the electrochemical detection of ascorbic acid (vitamin C). Methods: To improve electrochemical performance, we attempted to electro-polymerize the thionine monomers, an essential chemical building block, directly on the surface of singlewalled carbon nanotubes (SWCNT). Results: Field Emission Scanning electron microscopy (FESEM) and energy dispersive spectroscopy (EDS) results revealed that a complex structure of the P-Th/SWCNT was formed. The presence of carbon (C), oxygen (O), nitrogen (N), and sulfur (S) components was confirmed, which indicated the effective fusion of poly-thionine onto SWCNT. Moreover, the X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy confirmed the composite formation. Utilizing cyclic voltammetry, the composite's electrochemical behavior was examined. Conclusions: Excellent electrocatalytic activity towards the oxidation of ascorbic acid was shown by the P-Th/SWCNT composite. The as-prepared P-Th/SWCNT composite-modified sensor can detect ascorbic acid in food, medical, and pharmaceutical samples.

Fluorescence-Based Methods for the Study of Biomolecular Interactions.

Fluorescence-Based Methods for the Study of Biomolecular Interactions.

Effect of coordination mode of thiosemicarbazone on the biological activities of its Ru(II)-benzene complexes: Biomolecular interactions and anticancer activity via ROS-mediated mitochondrial apoptosis

Ru(II)-benzene complexes (P1 and P2) were synthesized using a thiosemicarbazone ligand (L1) in two different coordination modes, monodentate S and bidentate N,S, through carefully adjusted reaction conditions. Comprehensive characterization of the complexes was achieved through single crystal X-ray diffraction, revealing a piano-stool geometry around the Ru(II) ion. To evaluate the binding capabilities of the complexes towards CT DNA and BSA, UV–Vis and/or hydrodynamic methods were utilized. Docking studies further validated the intercalative binding mode with DNA, in agreement with the experimental findings, and identified specific BSA amino acids involved in the binding interactions. Based on the results of binding studies, cytotoxicity of the ligand and complexes was appraised in various cancer and normal cell lines alongside the commercial pharmaceutics. Complexes P1 and P2 displayed a promising activity against MDA-MB-231 [IC50 = 5.11 (P1) and 3.48 μM (P2)] and PANC-1 [IC50 = 7.20 (P1) and 4.85 μM (P2)] cancer cells; with the bidentate system (P2) exhibiting a higher activity than its monodentate congener P1, although both of them showed superior activity than the reference drugs. Various bioassays including Western blot analysis revealed the mode of cell death to be apoptosis, which was further concluded to be via the ROS-mediated mitochondrial signaling pathway.

Curvature-enhanced graph convolutional network for biomolecular interaction prediction

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics.

Surface Plasmon Resonance (SPR) technology is known to be a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram, which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the paper, we will summarize the kinetic data on the interaction between angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 obtained from the results of SPR signal analysis. ACE2 is a material that mediates virus entry. Therefore, understanding the kinetic changes between ACE2 and SARS-CoV-2 caused by the mutation will provide beneficial information for drug discovery, vaccine development, and other therapeutic purposes.