Protein Conformational Changes Research Articles

Development of “clean label” gluten-free breads fortified with flaxseed slurry and sesame cake: Implications on batter rheology, bread quality and shelf life

Following the recently emerged trends for products without additives and valorization of food processing by-products, “clean label” gluten-free (GF) breads were developed using a flaxseed slurry (FS) at 3% or 4.5% level (flaxseed basis), instead of the commonly used structurant in GF doughs, methylcellulose, and 3 or 6% (flour mixture basis) sesame cake (SC) for further product nutritional improvement. These alternative ingredients increased batter hardness and cohesiveness (back extrusion test) as well as elastic modulus, complex and steady shear viscosity (rheometry), compared to formulation containing only methylcellulose (control). The fortified breads had lower loaf specific volumes, than the control, but significantly higher than a GF bread made without any added hydrocolloid. FS and SC enhanced the formation of β-sheet structures (FTIR spectroscopy) in the batter and bread protein matrix. FS breads exhibited similar crumb hardness, cohesiveness, and springiness (TPA test) with the control, whereas inclusion of SC compromised these textural attributes. Nevertheless, the latter samples showed less pronounced textural changes upon storage (25°C-48 h), with similar extent of staling to control. For the fortified stored breads, there were no significant changes in protein conformation, compared to fresh products, whereas crumb starch retrogradation (calorimetry-DSC) was lower than the control. Additionally, the FS and SC increased the protein and dietary fiber contents and introduced nutty and sesame-like flavor notes to the breads, leading to improved overall acceptability scores. Overall, FS and SC seemed to be promising functional ingredients for the development of “clean label” GF breads with enhanced quality and shelf life.

Probing the role of protein conformational changes in the mechanism of prenylated-FMN-dependent phenazine-1-carboxylic acid decarboxylase

Phenazine-1-carboxylic acid decarboxylase (PhdA) is a prenylated-FMN-dependent (prFMN) enzyme belonging to the UbiD family of decarboxylases. Many UbiD-like enzymes catalyze (de)carboxylation reactions on aromatic rings and conjugated double bonds and are potentially valuable industrial catalysts. We have investigated the mechanism of PhdA using a slow turnover substrate, 2,3-dimethylquinoxaline-5-carboxylic acid (DQCA). Detailed analysis of the pH dependence and solvent deuterium isotope effects associated with the reaction uncovered unusual kinetic behavior. At low substrate concentrations, a substantial inverse solvent isotope effect (SIE) is observed on Vmax/KM of ∼ 0.5 when reaction rates of DQCA in H2O and D2O are compared. Under the same conditions, a normal SIE of 4.15 is measured by internal competition for proton transfer to the product. These apparently contradictory results indicate that the SIE values report on different steps in the mechanism. A proton inventory analysis of the reaction under Vmax/KM and Vmax conditions points to a "medium effect" as the source of the inverse SIE. Molecular dynamics simulations of the effect of D2O on PhdA structure support that D2O reduces the conformational lability of the enzyme and results in a more compact structure, akin to the active, "closed" conformer observed in crystal structures of some UbiD-like enzymes. Consistent with the simulations, PhdA was found to be more stable in D2O and to bind DQCA more tightly, leading to the observed rate enhancement under Vmax/KM conditions.

Прионы и прионные болезни: мифы и реальность

The article contains modern data about prions and the main prion-associated diseases. The authors describe the main conditions that provide prion-associated infectious diseases, primarily the conformation changes of protein PrPС expressed in healthy tissues of humans and animals and forming the pathogenic PrPSc protein. All prion-caused diseases are considered to be neurodegenerative with fatal outcomes due to a specific spongy brain damage. Information on some aspects of pathogenesis and therapeutic options for prion diseases is presented. The data on the prion safety of biological preparations widely used in healthcare are also presented.

Phosphorylation of Tau Protein by CDK2/cyclin A and GSK3β Recombinant Kinases: Analysis of Phosphorylation Patterns by Nuclear Magnetic Resonance Spectroscopy.

Posttranslational modifications (PTMs) of proteins can be investigated by Nuclear Magnetic Resonance (NMR) spectroscopy as a powerful analytical tool to define modification sites, their relative stoichiometry, and crosstalk between modifications. As a Structural Biology method, NMR provides important additional information on changes in protein conformation and dynamics upon modification as well as a mapping of binding sites upon biomolecular interactions. Indeed, PTMs not only mediate functional modulation in protein-protein interactions, but can also induce diverse structural responses with different biological outcomes. Here we present protocols that have been developed for the production and phosphorylation of the neuronal tau protein. Under its aggregated form, tau is a hallmark ofAlzheimer's disease and other neurodegenerative diseases named tauopathies involving tau dysfunction and/or mutations. As a common feature shared by various tauopathies, tau aggregates are found into a form displaying an increased, abnormal phosphorylation, also referred to hyperphosphorylation. We have used NMR to investigate the phosphorylation patterns of tau induced by several kinases or cell extracts, how phosphorylation affects the local and overall conformation of tau, its interactions with partners (proteins, DNA, small-molecules, etc.) including tubulin and microtubules, and its capacity to form insoluble fibrillar aggregates. We present here detailed protocols for in vitro phosphorylation of tau by the recombinant kinases CDK2/cyclin A and GSK3β, the production of the recombinant kinases thereof, as well as the analytical characterization of phosphorylated tau by NMR spectroscopy.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.

Prediction of Inter-residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning.

AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction is of great significance for research on protein multiple conformations exploration. In this study, we proposed an inter-residue multiple distances prediction method, DeepMDisPre, based on an improved network which integrates triangle update, axial attention and ResNet to predict multiple distances of residue pairs. We built a dataset which contains proteins with a single structure and proteins with multiple conformations to train the network. We tested DeepMDisPre on 114 proteins with multiple conformations. The results show that the inter-residue distance distribution predicted by DeepMDisPre tends to have multiple peaks for flexible residue pairs than for rigid residue pairs. On two cases of proteins with multiple conformations, we modeled the multiple conformations relatively accurately by using the predicted inter-residue multiple distances. In addition, we also tested the performance of DeepMDisPre on 279 proteins with a single structure. Experimental results demonstrate that the average contact accuracy of DeepMDisPre is higher than that of the comparative method. In terms of static protein modeling, the average TM-score of the 3D models built by DeepMDisPre is also improved compared with the comparative method. The executable program is freely available at https://github.com/iobio-zjut/DeepMDisPre.

The role of solvation on the conformational landscape of α-synuclein.

Native ion mobility mass spectrometry has been used extensively to characterize ensembles of intrinsically disordered protein (IDP) conformers, but the extent to which the gaseous measurements provide realistic pictures of the solution conformations for such flexible proteins remains unclear. Therefore, we systematically studied the relationship between the solution and gaseous structural ensembles by measuring electrospray charge state and collision cross section (CCS) distributions for cationic and anionic forms of α-synuclein (αSN), an anionic protein in solution, as well as directly probed gas phase residue to residue distances via ion/ion reactions between gaseous α-synuclein cations and disulfonic acid linkers that form strong electrostatic bonds. We also combined results from in-solution protein crosslinking identified from native tandem mass spectrometry (MS/MS) with an initial αSN ensemble generated computationally by IDPConformerGenerator to generate an experimentally restrained solution ensemble of αSN. CCS distributions were directly calculated for the solution ensembles determined by NMR and compared to predicted gaseous conformers. While charge state and collision cross section distributions are useful for qualitatively describing the relative structural dynamics of proteins and major conformational changes induced by changes to solution states, the predicted and measured gas phase conformers include subpopulations that are significantly different than those expected from completely "freezing" solution conformations and preserving them in the gas phase. However, insights were gained on the various roles of solvent in stabilizing various conformers for extremely dynamic proteins like α-synuclein.

Four-dimensional imaging for cryo-electron microscopy experiments using molecular simulations and manifold learning.

Elucidating protein conformational changes is essential because conformational changes are closely related to the functions of proteins. Cryo-electron microscopy (cryo-EM) experiment can be used to reconstruct protein conformational changes via a method that involves using the experimental data (two-dimensional protein images). In this study, a reconstruction method, referred to as the "four-dimensional imaging," was proposed. In our four-dimensional imaging technique, the protein conformational change was obtained using the two-dimensional protein images (the three-dimensional electron density maps used in previously proposed techniques were not used). The protein conformation for each two-dimensional protein image was obtained using our original protocol with molecular dynamics simulations. Using a manifold-learning technique and two-dimensional protein images, the protein conformations were arranged according to the conformational change of the protein. By arranging the protein conformations according to the arrangement of the protein images, four-dimensional imaging is constructed. A simulation for a cryo-EM experiment demonstrated the validity of our four-dimensional imaging technique.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy.

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Novel evidence on iodoacetic acid-induced immune protein functional and conformational changes: Focusing on cellular and molecular aspects

Elevated levels of iodide occur in raw water in certain regions, where iodination disinfection byproducts are formed during chloramine-assisted disinfection of naturally iodide-containing water. Iodoacetic acid (IAA) is one of the typical harmful products. The mechanisms underlying IAA-induced immunotoxicity and its direct effects on biomolecules remained unclear in the past. Cellular, biochemical, and molecular methods were used to investigate the mechanism of IAA-induced immunotoxicity and its binding to lysozyme. In the presence of IAA, the cell viability of coelomocytes was significantly reduced to 70.8 %, as was the intracellular lysozyme activity. Upon binding to IAA, lysozyme underwent structural and conformational changes, causing elongation and unfolding of the protein due to loosening of the backbone and polypeptide chains. IAA effectively quenched the fluorescence of lysozyme and induced a reduction in particle sizes. Molecular docking revealed that the catalytic residue, Glu 35, which is crucial for lysozyme activity, resided within the docking range, suggesting the preferential binding of IAA to the active site of lysozyme. Moreover, electrostatic interaction emerged as the primary driving force behind the interaction between IAA and lysozyme. In conclusion, the structural and conformational changes induced by IAA in lysozyme resulted in impaired immune protein function in coelomocytes, leading to cellular dysfunction.

Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning.

Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings.

Covalent conjugation with polyphenol reduced the sensitization of walnut and ameliorated allergy by enhancing intestinal epithelial barrier in mice

In order to reduce the sensitization of walnut protein (WP), the effects of the interaction between WP and (−)-Epigallocatechin gallate (EGCG), quercetin, trans-ferulic acid, and resveratrol were investigated. Covalent and non-covalent conjugations were compared. The results suggested that covalent conjugation reduced the free amino acid content, sulfhydryl content, and surface hydrophobicity. When compared to non-covalent conjugation, covalent modification showed a lower IgE binding capacity, accompanied by changes in protein conformation. Moreover, animal experiments revealed that there were up-regulation of transforming growth factor-β, T-box expressed in t cells, and forkhead transcription factor Foxp3 mRNA expression, and down-regulation of IL-4, IL-17, GATA binding protein 3 and retinoid-related orphan nuclear receptor γt mRNA expression in the conjugate groups. These results suggested that covalent conjugation of polyphenols, especially EGCG, likely ameliorated allergy by promoting Th1/Th2 and Treg/Th17 balance and alleviating allergy-induced intestinal barrier damage, which might be a support in reducing the allergenicity of WP.

Unraveling the Role of Flavor Structure and Physicochemical Properties in the Binding Phenomenon with Commercial Food Protein Isolates.

Food protein-flavor binding influences flavor release and perception. The complexity of the binding phenomenon lies in the flavor and protein properties. Thus, molecular interactions between commercial whey- or plant-based protein isolates (PI) such as pea, soy, and lupin, with carbonyl and alcohol flavor compounds were assessed by static headspace (HS) GC-MS. HS results showed that not only the displacement of the carbonyl group from the inner part of the flavor structure toward the edge promoted binding up to 52.76% ± 4.65 but also the flavor's degree of unsaturation. Similarly, thermal treatment led to a slight increase in hexanal-protein binding because of possible protein conformational changes. Protein's residual fat (<1%) seemed insufficient to promote significant flavor binding to PI. Despite the complexity of commercial food protein isolates, the results displayed that binding is predominantly influenced by the flavor structure and physicochemical properties, with the protein source and residual fat playing a secondary role.

Functional Flow Cytometry to Predict PD‐L1 Conformational Changes

AbstractThe programmed cell death protein 1/programmed cell death protein ligand 1 (PD‐1/PD‐L1) axis is one of the most widely recognized targets for cancer immunotherapy. Importantly, PD‐L1 conformational changes can hinder target binding when living cells are used. Antibody affinity, equilibrium binding, association and dissociation rates, and other affinity‐related constants are fundamental to ensure target saturation. Here, PD‐L1 changes in conformation and their potential impact on PD‐L1 function and mutation are explored. Specifically, we present detailed flow cytometry procedures to analyze PD‐L1 reactivity in myeloid‐derived suppressor cells (MDSCs). This approach can also be used to study the contribution of protein conformational changes in living cells. © 2023 Wiley Periodicals LLC.Basic Protocol 1: Sample preparation for PD‐L1+ myeloid‐derived suppressor cells detection by flow cytometryBasic Protocol 2: Protocol preparation, sample acquisition, and gating strategy for flow cytometric screening of PD‐L1+ myeloid‐derived suppressor cells in patients with lung cancerSupport Protocol 1: Bioinformatic tools for the analysis of flow cytometric data

Ultrasensitive, dynamic, and online monitoring photonic sensors for protein conformation

The monitoring of molecular dynamic transition and protein conformation changes is important for some neurodegenerative diseases (NDs) and has attracted much attention due to its ability to reveal the pathological mechanism of several NDs. Here, a label-free optical detection method with an ultrahigh refractive index sensitivity (S) and dynamical S tuning range was developed to provide a novel technology roadmap for the online monitoring of protein conformation. The real-time conformation variation process was transduced into a visualization optical signal, allowing it to be monitored. The biosensor prototype was constructed by the cross-overlapping of two rows of square capillaries on a gold mirror to form two microcavities with different free spectral ranges (denoted as the sensing and reference cavities). The coupling of the resonance mode between the two microcavities induced the generation of the Vernier effect and the magnifying of S, presented in the form of a modulated envelope in the spectrum. S values ranged from −17,256 nm/RIU to 93,937 nm/RIU, indicating that the biosensor could not only realize large dynamic tuning ranges but also an ultrahigh S. The conformation changes of silk proteins from the random coil to the β–sheet were sensitively monitored by the output spectrum after it was stimulated with ethanol solution. This ultrasensitive biosensor (which is both label-free and optical) provides a novel and supplementary technique to monitor the structural and conformation changes process of proteins. The results reveal its significant potential for clinical diagnosis, healthcare, pharmaceutical screening, and food security.

The interaction of perfluorooctane sulfonate with hemoproteins and its relevance to molecular toxicology

Perfluorooctane sulfonate (PFOS), a representative of perfluorinated compounds in industrial and commercial products, has posed a great threat to animals and humans via environmental exposure and dietary consumption. Herein, we investigated the effects of PFOS binding on the redox state and stability of two hemoproteins (hemoglobin (Hb) and myoglobin (Mb)). Fluorescence spectroscopy, circular dichroism and UV–vis absorption spectroscopy demonstrated that PFOS could induce the conformational changes of proteins along with the exposure of heme cavity and generation of hemichrome, which resulted in the increased release of free hemin. After that, free hemin liberated from hemoproteins led to reactive oxygen species formation, lipid peroxidation, cell membrane damage and loss of cell viability in vascular endothelial cells, while neither Hb nor Mb did show cytotoxicity. Chemical inhibitors of ferroptosis effectively mitigated hemin-caused toxicity, identifying the hemin-dependent ferroptotic cell death mechanisms. These data demonstrated that PFOS posed a potential threat of toxicity through a mechanism which involved its binding to hemoproteins, decreased oxygen transporting capacity, and increased hemin release. Altogether, our findings elucidate the binding mechanisms of PFOS with two hemoproteins, as well as possible risks on vascular endothelial cells, which would have important implications for the human and environmental toxicity of PFOS.

Frequency dependence of ultrasonic effects on the kinetics of hen egg white lysozyme fibrillation

Our study aimed to investigate the effects of ultrasound on the fibrillation kinetics of HEWL (hen egg white lysozyme) and its physicochemical properties. Ultrasound, a mechanical wave, can induce conformational changes in proteins. To achieve this, we developed an ultrasound exposure system and used various biophysical techniques, including ThT fluorescence spectroscopy, ATR-FTIR, Far-UV CD spectrophotometry, Fluorescence microscopy, UV-spectroscopy, and seeding experiments. Our results revealed that higher frequencies significantly accelerated the fibrillation of lysozyme by unfolding the native protein and promoting the fibrillation process, thereby reducing the lag time. We observed a change in the secondary structure of the sonicated protein change to the β-structure, but there was no difference in the Tm of native and sonicated proteins. Furthermore, we found that higher ultrasound frequencies had a greater seeding effect. We propose that the effect of frequency can be explained by the impact of the Reynolds number, and for the Megahertz frequency range, we are almost at the transition regime of turbulence. Our results suggest that laminar flows may not induce any significant change in the fibrillation kinetics, while turbulent flows may affect the process.

Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor.

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the β2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the β2 adrenergic receptor.

Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.