Protein Conformational Dynamics Research Articles

Native dynamics and allosteric responses in PTP1B probed by high-resolution HDX-MS.

Protein tyrosine phosphatase 1B (PTP1B) is a validated therapeutic target for obesity, diabetes, and certain types of cancer. In particular, allosteric inhibitors hold potential for therapeutic use, but an incomplete understanding of conformational dynamics and allostery in this protein has hindered their development. Here, we interrogate solution dynamics and allosteric responses in PTP1B using high-resolution hydrogen-deuterium exchange mass spectrometry (HDX-MS), an emerging and powerful biophysical technique. Using HDX-MS, we obtain a detailed map of backbone amide exchange that serves as a proxy for the solution dynamics of apo PTP1B, revealing several flexible loops interspersed among more constrained and rigid regions within the protein structure, as well as local regions that exchange faster than expected from their secondary structure and solvent accessibility. We demonstrate that our HDX rate data obtained in solution adds value to estimates of conformational heterogeneity derived from a pseudo-ensemble constructed from ~200 crystal structures of PTP1B. Furthermore, we report HDX-MS maps for PTP1B with active-site versus allosteric small-molecule inhibitors. These maps suggest distinct and widespread effects on protein dynamics relative to the apo form, including changes in locations distal (>35 Å) from the respective ligand binding sites. These results illuminate that allosteric inhibitors of PTP1B can induce unexpected changes in dynamics that extend beyond the previously understood allosteric network. Together, our data suggest a model of BB3 allostery in PTP1B that combines conformational restriction of active-site residues with compensatory liberation of distal residues that aid in entropic balancing. Overall, our work showcases the potential of HDX-MS for elucidating aspects of protein conformational dynamics and allosteric effects of small-molecule ligands and highlights the potential of integrating HDX-MS alongside other complementary methods, such as room-temperature X-ray crystallography, NMR spectroscopy, and molecular dynamics simulations, to guide the development of new therapeutics.

Linear and Nonlinear Dielectric Response of Intrinsically Disordered Proteins.

Linear and nonlinear dielectric responses of solutions of intrinsically disordered proteins (IDPs) were analyzed by combining molecular dynamics simulations with formal theories. A large increment of the linear dielectric function over that of the solvent is found and related to large dipole moments of IDPs. The nonlinear dielectric effect (NDE) of the IDP far exceeds that of the bulk electrolyte, offering a route to interrogate protein conformational and rotational statistics and dynamics. Conformational flexibility of the IDP makes the dipole moment statistics consistent with the gamma/log-normal distributions and contributes to the NDE through the dipole moment's non-Gaussian parameter. The intrinsic non-Gaussian parameter of the dipole moment combines with the protein osmotic compressibility in the nonlinear dielectric susceptibility when dipolar correlations are screened by the electrolyte. The NDE is dominated by dipolar correlations when electrolyte screening is reduced.

Raman spectroscopy in the study of amyloid formation and phase separation.

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, share a common pathological feature of amyloid structure accumulation. However, the structure-function relationship between these well-ordered, β-sheet-rich, filamentous protein deposits and disease etiology remains to be defined. Recently, an emerging hypothesis has linked phase separation, a process involved in the formation of protein condensates, to amyloid formation, suggesting that liquid protein droplets serve as loci for amyloid initiation. To elucidate how these processes contribute to disease progression, tools that can directly report on protein secondary structural changes are needed. Here, we review recent studies that have demonstrated Raman spectroscopy as a powerful vibrational technique for interrogating amyloid structures; one that offers sensitivity from the global secondary structural level to specific residues. This probe-free technique is further enhanced via coupling to a microscope, which affords structural data with spatial resolution, known as Raman spectral imaging (RSI). In vitro and in cellulo applications of RSI are discussed, highlighting studies of protein droplet aging, cellular internalization of fibrils, and Raman imaging of intracellular water. Collectively, utilization of the myriad Raman spectroscopic methods will contribute to a deeper understanding of protein conformational dynamics in the complex cellular milieu and offer potential clinical diagnostic capabilities for protein misfolding and aggregation processes in disease states.

Polyethylene Glycol Impacts Conformation and Dynamics of Escherichia coli Prolyl-tRNA Synthetase Via Crowding and Confinement Effects.

Polyethylene glycol (PEG) is a flexible, nontoxic polymer commonly used in biological and medical research, and it is generally regarded as biologically inert. PEG molecules of variable sizes are also used as crowding agents to mimic intracellular environments. A recent study with PEG crowders revealed decreased catalytic activity of Escherichia coli prolyl-tRNA synthetase (Ec ProRS), where the smaller molecular weight PEGs had the maximum impact. The molecular mechanism of the crowding effects of PEGs is not clearly understood. PEG may impact protein conformation and dynamics, thus its function. In the present study, the effects of PEG molecules of various molecular weights and concentrations on the conformation and dynamics of Ec ProRS were investigated using a combined experimental and computational approach including intrinsic tryptophan fluorescence spectroscopy, atomic force microscopy, and atomistic molecular dynamic simulations. Results of the present study suggest that lower molecular weight PEGs in the dilute regime have modest effects on the conformational dynamics of Ec ProRS but impact the catalytic function primarily via the excluded volume effect; they form large clusters blocking the active site pocket. In contrast, the larger molecular weight PEGs in dilute to semidilute regimes have a significant impact on the protein's conformational dynamics; they wrap on the protein surface through noncovalent interactions. Thus, lower-molecular-weight PEG molecules impact protein dynamics and function via crowding effects, whereas larger PEGs induce confinement effects. These results have implications for the development of inhibitors for protein targets in a crowded cellular environment.

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods.

Ligand-induced protein transition state stabilization switches the binding pathway from conformational selection to induced fit

Protein-ligand complex formation is fundamental to biological function. A central question is whether proteins spontaneously adopt binding-competent conformations to which ligands bind conformational selection (CS) or whether ligands induce the binding-competent conformation induced fit (IF). Here, we resolve the CS and IF binding pathways by characterizing protein conformational dynamics over a wide range of ligand concentrations using NMR relaxation dispersion. We determined the relative flux through the two pathways using a four-state binding model that includes both CS and IF. Experiments conducted without ligand show that galectin-3 exchanges between the ground-state conformation and a high-energy conformation similar to the ligand-bound conformation, demonstrating that CS is a plausible pathway. Near-identical crystal structures of the apo and ligand-bound states suggest that the high-energy conformation in solution corresponds to the apo crystal structure. Stepwise additions of the ligand lactose induce progressive changes in the relaxation dispersions that we fit collectively to the four-state model, yielding all microscopic rate constants and binding affinities. The ligand affinity is higher for the bound-like conformation than for the ground state, as expected for CS. Nonetheless, the IF pathway contributes greater than 70% of the total flux even at low ligand concentrations. The higher flux through the IF pathway is explained by considerably higher rates of exchange between the two protein conformations in the ligand-associated state. Thus, the ligand acts to decrease the activation barrier between protein conformations in a manner reciprocal to enzymatic transition-state stabilization of reactions involving ligand transformation.

Activation and friction in enzymatic loop opening and closing dynamics

Protein loop dynamics have recently been recognized as central to enzymatic activity, specificity and stability. However, the factors controlling loop opening and closing kinetics have remained elusive. Here, we combine molecular dynamics simulations with string-method determination of complex reaction coordinates to elucidate the molecular mechanism and rate-limiting step for WPD-loop dynamics in the PTP1B enzyme. While protein conformational dynamics is often represented as diffusive motion hindered by solvent viscosity and internal friction, we demonstrate that loop opening and closing is activated. It is governed by torsional rearrangement around a single loop peptide group and by significant friction caused by backbone adjustments, which can dynamically trap the loop. Considering both torsional barrier and time-dependent friction, our calculated rate constants exhibit very good agreement with experimental measurements, reproducing the change in loop opening kinetics between proteins. Furthermore, we demonstrate the applicability of our results to other enzymatic loops, including the M20 DHFR loop, thereby offering prospects for loop engineering potentially leading to enhanced designs.

Sakuranetin ameliorates streptozotocin-induced diabetes in rodents by inhibiting caspase-3 activity, modulating hematological parameters, and suppressing inflammatory cytokines: a molecular docking and dynamics study

Diabetes affects people of all ages, regardless of gender and background. To date, there is no evidence for the effect of sakuranetin against the streptozotocin (STZ)-induced diabetes paradigm. The research was directed to evaluate the antidiabetic activity of sakuranetin in the STZ model invoking the diabetes-induced disease paradigm. STZ (I.P. 60 mg/kg) is directed to induce type 2 diabetes in experimental rats. Recent research pursued to regulate the anti-diabetic ability of sakuranetin at both 10 and 20 mg/kg in STZ-induced rats. Furthermore, molecular docking research was implemented to evaluate sakuranetin requisite attraction to inflammatory indicators. Various anti-diabetic [(glucose, hemoglobin A1c (HbA1c), and insulin)], lipid profile [triglycerides (TG), total cholesterol (TC), and high-density lipoproteins (HDL)], hematological parameters [Hemoglobin (HGB), packed cell volume (PCV), red blood cells (RBC), mean corpuscular volume (MCV), platelet (PLT), and white blood cells (WBC), pro-inflammatory cytokines [tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6)], antioxidant level [catalase (CAT), superoxide dismutase (SOD), glutathione (GSH)], lipid oxidation, and caspase-3 were evaluated. Furthermore, molecular docking and dynamics were performed for TNF-α (2AZ5), IL-6 (1ALU), IL-1β (6Y8M), Caspase-3 (1NME) and serum insulin (4IBM) target ligands. Sakuranetin treatment at both doses restored the biochemical parameters i.e. blood glucose, insulin, HbA1c, lipid profile, hematological parameters, pro-inflammatory markers, antioxidant levels, lipid oxidation, and caspase-3 in the context of diabetic rats. It also showed favorable binding affinity on inflammatory markers. Sakuranetin binds to proteins 2AZ5, 1ALU, 6Y8M, 1NME, and 4IBM at −7.489, −6.381, −6.742, −7.202, and −8.166 Kcal/mol, respectively. All of the findings from the molecular dynamics simulations points toward a considerable change in the conformational dynamics of protein upon binding with sakuranetin. The potential use of sakuranetin as an alternative diabetes medication will aid future research as a potent anti-diabetic agent. Communicated by Ramaswamy H. Sarma

Predicted and Experimental NMR Chemical Shifts at Variable Temperatures: The Effect of Protein Conformational Dynamics.

NMR chemical shifts provide a sensitive probe of protein structure and dynamics but remain challenging to predict and interpret. We examine the effect of protein conformational distributions on 15N chemical shifts for dihydrofolate reductase (DHFR), comparing QM/MM predicted shifts with experimental shifts in solution as well as frozen distributions. Representative snapshots from MD trajectories exhibit variation in predicted 15N chemical shifts of up to 25 ppm. The average over the fluctuations is in significantly better agreement with room temperature solution experimental values than the prediction for any single optimal conformations. Meanwhile, solid-state NMR (SSNMR) measurements of frozen solutions at 105 K exhibit broad lines whose widths agree well with the widths of distributions of predicted shifts for samples from the trajectory. The backbone torsion angle ψi-1 varies over 60° on the picosecond time scale, compensated by φi. These fluctuations can explain much of the shift variation.

N-Glycosidase Rudaea cellulosilytica (PNGase Rc) Column for the Characterization of Glycoprotein Conformational Epitopes by Hydrogen/Deuterium Exchange Mass Spectrometry (HDX-MS)

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is a sensitive technique used to study the conformational dynamics of proteins in solution. However, performing HDX-MS with N-linked glycoproteins is challenging. Analyzing glycoproteins through HDX-MS requires the use of N-glycoprotein deglycosylation enzyme (peptide: N-glycanase, abbreviated as PNGase) to deglycosylate glycoproteins. Traditional PNGases are inactive or have low activity under HDX-MS operating conditions. Here, we recommend the use of ultra-acidic N-glycopeptidase from PNGase Rc, which exhibits optimal enzymatic activity compared to other PNGases at pH 2.2. We covalently fixed PNGase Rc onto POROS resin for HDX-MS online deglycosylation. The results show that the immobilized PNGase Rc combined with pepsin column has good stability under various quenching conditions. Its online deglycosylation efficiency is better than other PNGases. Meanwhile, we used AlphaFold 2 and molecular dynamics (MD) to characterize the structure and stability of PNGase Rc and other PNGases, and demonstrated that PNGase Rc is the most stable.

Inclusion of deuterated glycopeptides provides increased sequence coverage in hydrogen/deuterium exchange mass spectrometry analysis of SARS-CoV-2 spike glycoprotein.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) can provide precise analysis of a protein's conformational dynamics across varied states, such as heat-denatured versus native protein structures, localizing regions that are specifically affected by such conditional changes. Maximizing protein sequence coverage provides high confidence that regions of interest were located by HDX-MS, but one challenge for complete sequence coverage is N-glycosylation sites. The deuteration of peptides post-translationally modified by asparagine-bound glycans (glycopeptides) has not always been identified in previous reports of HDX-MS analyses, causing significant sequence coverage gaps in heavily glycosylated proteins and uncertainty in structural dynamics in many regions throughout a glycoprotein. We detected deuterated glycopeptides with a Tribrid Orbitrap Eclipse mass spectrometer performing data-dependent acquisition. An MS scan was used to identify precursor ions; if high-energy collision-induced dissociation MS/MS of the precursor indicated oxonium ions diagnostic for complex glycans, then electron transfer low-energy collision-induced dissociation MS/MS scans of the precursor identified the modified asparagine residue and the glycan's mass. As in traditional HDX-MS, the identified glycopeptides were then analyzed at the MS level in samples labeled with D2 O. We report HDX-MS analysis of the SARS-CoV-2 spike protein ectodomain in its trimeric prefusion form, which has 22 predicted N-glycosylation sites per monomer, with and without heat treatment. We identified glycopeptides and calculated their average isotopic mass shifts from deuteration. Inclusion of the deuterated glycopeptides increased sequence coverage of spike ectodomain from 76% to 84%, demonstrated that glycopeptides had been deuterated, and improved confidence in results localizing structural rearrangements. Inclusion of deuterated glycopeptides improves the analysis of the conformational dynamics of glycoproteins such as viral surface antigens and cellular receptors.

Markov State Models: To Optimize or Not to Optimize.

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

Beyond Silicon: The Advent of Biomolecular Computing

ABSTRACT: Bio computing is an emerging interdisciplinary field that harnesses the information processing capabilities of biological substrates like DNA, proteins and cells to perform computational tasks. Rather than relying solely on conventional silicon-based computers, bio computing leverages the innate computational properties of biomolecules to encode, store, process and transmit information in unconventional ways. Core approaches include DNA computing, which uses DNA biochemistry to solve problems in a massively parallel fashion. Protein computing utilizes protein conformational dynamics to implement logic gates and communication modules for molecular information processing. Cellular computing focuses on engineering gene circuits and synthetic biology tools to program computational behaviours in living cells. Neural computing builds artificial neural networks inspired by biological brains. Key application areas include biomedicine, smart drug delivery systems, biosensing, hybrid organic-inorganic electronics, and biomolecular manufacturing. While still facing challenges around biocompatibility, programming complexity and ethical concerns, bio computing has achieved major technical milestones demonstrating its promise. Continued progress at the interface of biology and computing could enable future technologies like bio processors, in-vivo biocomputers, living materials and bio-intelligent systems. With responsible development, bio-inspired computation may catalyse the next revolution in human technological capabilities. This emerging field thus warrants enthusiastic attention as computation further converges with the living world.

Conformational Dynamics in Proteins: Entangled Slow Fluctuations and Nonequilibrium Reaction Events.

Proteins exhibit conformational fluctuations and changes over various time scales, ranging from rapid picosecond-scale local atomic motions to slower microsecond-scale global conformational transformations. In the presence of these intricate fluctuations, chemical reactions occur and functions emerge. These conformational fluctuations of proteins are not merely stochastic random motions but possess distinct spatiotemporal characteristics. Moreover, chemical reactions do not always proceed along a single reaction coordinate in a quasi-equilibrium manner. Therefore, it is essential to understand spatiotemporal conformational fluctuations of proteins and the conformational change processes associated with reactions. In this Perspective, we shed light on the complex dynamics of proteins and their role in enzyme catalysis by presenting recent results regarding dynamic couplings and disorder in the conformational dynamics of proteins and rare but rapid enzymatic reaction events obtained from molecular dynamics simulations.

Structural and pKa Estimation of the Amphipathic HR1 in SARS-CoV-2: Insights from Constant pH MD, Linear vs. Nonlinear Normal Mode Analysis.

A comprehensive understanding of molecular interactions and functions is imperative for unraveling the intricacies of viral protein behavior and conformational dynamics during cellular entry. Focusing on the SARS-CoV-2 spike protein (SARS-CoV-2 sp), a Principal Component Analysis (PCA) on a subset comprising 131 A-chain structures in presence of various inhibitors was conducted. Our analyses unveiled a compelling correlation between PCA modes and Anisotropic Network Model (ANM) modes, underscoring the reliability and functional significance of low-frequency modes in adapting to diverse inhibitor binding scenarios. The role of HR1 in viral processing, both linear Normal Mode Analysis (NMA) and Nonlinear NMA were implemented. Linear NMA exhibited substantial inter-structure variability, as evident from a higher Root Mean Square Deviation (RMSD) range (7.30 Å), nonlinear NMA show stability throughout the simulations (RMSD 4.85 Å). Frequency analysis further emphasized that the energy requirements for conformational changes in nonlinear modes are notably lower compared to their linear counterparts. Using simulations of molecular dynamics at constant pH (cpH-MD), we successfully predicted the pKa order of the interconnected residues within the HR1 mutations at lower pH values, suggesting a transition to a post-fusion structure. The pKa determination study illustrates the profound effects of pH variations on protein structure. Key results include pKa values of 9.5179 for lys-921 in the D936H mutant, 9.50 for the D950N mutant, and a slightly higher value of 10.49 for the D936Y variant. To further understand the behavior and physicochemical characteristics of the protein in a biologically relevant setting, we also examine hydrophobic regions in the prefused states of the HR1 protein mutants D950N, D936Y, and D936H in our study. This analysis was conducted to ascertain the hydrophobic moment of the protein within a lipid environment, shedding light on its behavior and physicochemical properties in a biologically relevant context.

Biophysical Principles Emerging from Experiments on Protein-Protein Association and Aggregation.

Protein-protein association and aggregation are fundamental processes that play critical roles in various biological phenomena, from cellular signaling to disease progression. Understanding the underlying biophysical principles governing these processes is crucial for elucidating their mechanisms and developing strategies for therapeutic intervention. In this review, we provide an overview of recent experimental studies focused on protein-protein association and aggregation. We explore the key biophysical factors that influence these processes, including protein structure, conformational dynamics, and intermolecular interactions. We discuss the effects of environmental conditions such as temperature, pH and related buffer-specific effects, and ionic strength and related ion-specific effects on protein aggregation. The effects of polymer crowders and sugars are also addressed. We list the techniques used to study aggregation. We analyze emerging trends and challenges in the field, including the development of computational models and the integration of multidisciplinary approaches for a comprehensive understanding of protein-protein association and aggregation. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20–100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.

Probing macromolecular crowding at the lipid membrane interface with genetically-encoded sensors.

Biochemical processes within the living cell occur in a highly crowded environment, where macromolecules, first of all proteins and nucleic acids, occupy up to 30 % of the volume. The phenomenon of macromolecular crowding is not an exclusive feature of the cytoplasm and can be observed in the densely protein-packed, nonhomogeneous cellular membranes and at the membrane interfaces. Crowding affects diffusional and conformational dynamics of proteins within the lipid bilayer, alters kinetic and thermodynamic properties of biochemical reactions, and modulates the membrane organization. Despite its importance, the non-invasive quantification of the membrane crowding is not trivial. Here, we developed the genetically-encoded fluorescence-based sensor for probing the macromolecular crowding at the membrane interfaces. Two sensor variants, both composed of fluorescent proteins and a membrane anchor, but differing by the flexible linker domains were characterized in vitro, and the procedures for the membrane reconstitution were established. Steric pressure induced by membrane-tethered synthetic and protein crowders altered the sensors' conformation, causing increase in the intramolecular Förster's resonance energy transfer. Notably, the effect of protein crowders only weakly correlated with their molecular weight, suggesting that other factors, such as shape and charge contribute to the crowding via the quinary interactions. Finally, measurements performed in inner membrane vesicles of E. coli validated the crowding-dependent dynamics of the sensors in the physiologically relevant environment. The sensors offer broad opportunities to study interfacial crowding in a complex environment of native membranes, and thus add to the toolbox of methods for studying membrane dynamics and proteostasis. This article is protected by copyright. All rights reserved.

Sugar osmolyte inhibits and attenuates the fibrillogenesis in RNase A: An in vitro and in silico characterizations

Mechanisms of protein aggregation are of immense interest in therapeutic biology and neurodegenerative medicine. Biochemical processes within the living cell occur in a highly crowded environment. The phenomenon of macromolecular crowding affects the diffusional and conformational dynamics of proteins and modulates their folding. Macromolecular crowding is reported to cause protein aggregation in some cases, so it is a cause of concern as it leads to a plethora of neurodegenerative disorders and systemic amyloidosis. To divulge the mechanism of aggregation, it is imperative to study aggregation in well-characterized model proteins in the presence of macromolecular crowder. One such protein is ribonuclease A (RNase A), which deciphers neurotoxic function in humans; therefore we decided to explore the amyloid fibrillogenesis of this thermodynamically stable protein. To elucidate the impact of crowder, dextran-70 and its monomer glucose on the aggregation profile of RNase-A various techniques such as Absorbance, Fluorescence, Fourier Transforms Infrared, Dynamic Light Scattering and circular Dichroism spectroscopies along with imaging techniques like Atomic Force Microscopy and Transmission Electron Microscopy were employed. Thermal aggregation and fibrillation were further promoted by dextran-70 while glucose counteracted the effect of the crowding agent in a concentration-dependent manner. This study shows that glucose provides stability to the protein and prevents fibrillation. Intending to combat aggregation, which is the hallmark of numerous late-onset neurological disorders and systemic amyloidosis, this investigation unveils that naturally occurring osmolytes or other co-solutes can be further exploited in novel drug design strategies.

Oscillations in $$p$$-Adic Diffusion Processes and Simulation of the Conformational Dynamics of Protein

Oscillations in $$p$$-Adic Diffusion Processes and Simulation of the Conformational Dynamics of Protein