Functional State Of Protein Research Articles

Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations.

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines randomized alanine sequence masking with shallow multiple sequence alignment subsampling to expand the conformational diversity of the predicted structural ensembles and capture conformational changes between apo and holo protein forms. Using several well-established datasets of structurally diverse apo-holo protein pairs, the proposed approach enables robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with the view that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AlphaFold2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that robust modeling of functional protein states may require more accurate characterization of flexible regions in functional conformations and detection of high energy conformations. By incorporating a wider variety of protein structures in training datasets including both apo and holo forms, the model can learn to recognize and predict the structural changes that occur upon ligand binding.

A Stochastic Landscape Approach for Protein Folding State Classification.

Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity.

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

Mass Spectrometry Analysis of Glycopeptides Enriched by Anion Exchange-Mediated Methods Reveals PolyLacNAc-Extended N-Glycans in Integrins and Tetraspanins in Melanoma Cells.

Glycosylation is a key modulator of the functional state of proteins. Recent developments in large-scale analysis of intact glycopeptides have enabled the identification of numerous glycan structures that are relevant in pathophysiological processes. However, one motif found in N-glycans, poly-N-acetyllactosamine (polyLacNAc), still poses a substantial challenge to mass spectrometry-based glycoproteomic analysis due to its relatively low abundance and large size. In this work, we developed approaches for the systematic mapping of polyLacNAc-elongated N-glycans in melanoma cells. We first evaluated five anion exchange-based matrices for enriching intact glycopeptides and selected two materials that provided better overall enrichment efficiency. We then tested the robustness of the methodology by quantifying polyLacNAc-containing glycopeptides as well as changes in protein fucosylation and sialylation. Finally, we applied the optimal enrichment methods to discover glycopeptides containing polyLacNAc motifs in melanoma cells and found that integrins and tetraspanins are substantially modified with these structures. This study demonstrates the feasibility of glycoproteomic approaches for identification of glycoproteins with polyLacNAc motifs.

Structured illumination-based super-resolution live-cell quantitative FRET imaging

Förster resonance energy transfer (FRET) microscopy provides unique insight into the functionality of biological systems via imaging the spatiotemporal interactions and functional state of proteins. Distinguishing FRET signals from sub-diffraction regions requires super-resolution (SR) FRET imaging, yet is challenging to achieve from living cells. Here, we present an SR FRET method named SIM-FRET that combines SR structured illumination microscopy (SIM) imaging and acceptor sensitized emission FRET imaging for live-cell quantitative SR FRET imaging. Leveraging the robust co-localization prior of donor and accepter during FRET, we devised a mask filtering approach to mitigate the impact of SIM reconstruction artifacts on quantitative FRET analysis. Compared to wide-field FRET imaging, SIM-FRET provides nearly twofold spatial resolution enhancement of FRET imaging at sub-second timescales and maintains the advantages of quantitative FRET analysis in vivo. We validate the resolution enhancement and quantitative analysis fidelity of SIM-FRET signals in both simulated FRET models and live-cell FRET-standard construct samples. Our method reveals the intricate structure of FRET signals, which are commonly distorted in conventional wide-field FRET imaging.

Advances in Activity‐Based Protein Profiling of Functional Tyrosines in Proteomes

AbstractActivity‐based protein profiling (ABPP) is a chemical proteomic method for investigating functional states of proteins in native biological settings. By quantifying changes in probe binding states of active and regulatory protein sites, ABPP reveals functional information on protein regulation and can be configured in competitive settings to determine global selectivity profiles of tool compounds and drugs in lysates, cells, and animals. Chemical probes used for ABPP analyses can target protein families with conserved enzymatic or structural features or can broadly profile the proteome using electrophiles with reactivity towards functional groups on amino acid side chains. The latter approach has provided insights to protein sites involved in allosteric regulation and non‐enzymatic functions. This review introduces quantitative ABPP workflows and discusses electrophilic groups used for ABPP profiling of functional sites in the proteome with an emphasis on tyrosine residues.

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel.

Mechanosensitive channel of large conductance (MscL) protein detect and respond to changes in the pressure profile of cellular membranes and transduce the mechanical energy into electrical and/or chemical signals. MscL can be activated using ultrasonic or chemical activation methods to improve the absorption of medicines and bioactive compounds into cells. However, by re-engineering the MscL, chemical signals such as pH change can trigger the channel's activation. This paper elucidates the activation mechanism of an engineered and wild-type (WT) MscL at an atomic level through a combination of equilibrium and non-equilibrium (NE) molecular dynamics (MD) simulations, the String method with swarms of trajectories (SMwST), and Expectation maximization (EM) methods. Compared to the WT and engineered MscL activation processes, it suggests that the two systems are likely associated with different active states and different transition pathways. These findings indicate that (1) periplasmic loops play a key role in the activation process of MscL; (2) the loss of various backbone-backbone hydrogen bonds and salt bridge interactions in the engineered MscL channel causes the spontaneous opening of the channel; and (3) the most significant interactions that were lost during the activation process were those between the transmembrane helices 1 and 2 in the engineered MscL channel. In this work, the orientation-based biasing approach for producing and optimizing an open MscL model is a promising way to systematically characterize unknown protein functional states and to research the activation processes in ion channels. This work makes it possible to study ways to design pH-triggered channel-functionalized liposomes for drug delivery.

Pre-Molten, Wet, and Dry Molten Globules en Route to the Functional State of Proteins.

Transitions between the unfolded and native states of the ordered globular proteins are accompanied by the accumulation of several intermediates, such as pre-molten globules, wet molten globules, and dry molten globules. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

Spatial Chemoproteomics for Mapping the Active Proteome.

Functional regulation of cell signaling through dynamic changes in protein activity state as well as spatial organization represent two dynamic, complex, and conserved phenomena in biology. Seemingly separate areas of -omics method development have focused on building tools that can detect and quantify protein activity states, as well as map sub-cellular and intercellular protein organization. Integration of these efforts, through the development of chemical tools and platforms that enable detection and quantification of protein functional states with spatial resolution provide opportunities to better understand heterogeneity in the proteome within cell organelles, multi-cellular tissues, and whole organisms. This review provides an overview of and considerations for major classes of chemical proteomic probes and technologies that enable protein activity mapping from sub-cellular compartments to live animals.

Thermal Proteome Profiling for Drug Target Identification and Probing of Protein States.

Proteins are central drivers of physiological and pathological processes in the cell. Methods evaluating protein functional states are therefore vital to fundamental research as well as drug discovery. Thermal proteome profiling (TPP) to this date constitutes the only approach that permits examining protein states in live cells, under native conditions and at a proteome-wide scale. TPP harnesses ligand/perturbation-induced changes in protein thermal stability, which are monitored by multiplexed quantitative mass spectrometry. In this chapter, we describe a modular experimental workflow for TPP experiments using live cells or crude cell extracts. We provide the tools to perform different TPP formats, i.e., temperature range experiments, TPP-TR; isothermal compound titrations, TPP-CCR; and a combination thereof, 2D-TPP.

Number of Hydrogen Bonds per Unit Solvent Accessible Surface Area: A Descriptor of Functional States of Proteins.

Proteins function close to native and near-native conformations. These states are evolutionarily selected to ensure the effect of mutations is minimized. The structural organization of a protein is hierarchical and modular, which reduces the dimensionality of the configurational space of the native states. Thus, finding appropriate descriptors that define the native state among all possible states of a protein is a problem of immense interest. The present study explores the correlation between solvent accessible surface areas (SASAs) and different intraprotein as well as protein-water hydrogen bonds of 55 single-chain globular proteins from four different structural classes (all α, all β, α+β, and α/β), 16 multichain proteins, and 4 macromolecular complexes. A systematic analysis of the solvent accessible surface area and intraprotein and protein-water hydrogen bonds suggests a linear relationship between SASAs and hydrogen bonds. The number of protein-water hydrogen bonds per unit SASA ranges from 3 to 4 for all the different structural protein classes. In contrast, the number of intramolecular hydrogen bonds per unit SASA, including the mainchain-mainchain, mainchain-sidechain, and sidechain-sidechain, varies between 0.75 to 2. The solvation free energy of a protein linearly decreases with SASA. Our study also shows that the solvation free energy/SASA varies from -75 to -105 kJ mol-1 nm-2 across all the native states studied here. The number conservancy of intraprotein hydrogen bonds per unit SASA possibly imparts structural stability to the native structure. On the other hand, 3-4 protein-water hydrogen bonds per unit SASA are possibly required to maintain a balance between the solubility and functionality of the native states. This study provides a basis for synthetic biologists to design new folds with improved functionality.

A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer.

Simple SummaryNumerous factors, such as genomic mutations, chromosomal changes, transcriptional controls, phosphorylation, and protein–protein interactions, among others, can affect the activation status of proteins. Although each data type only partially reveals the status of a particular gene’s disruption, downstream expression changes ultimately indicate the functional effects of cancer driver protein alterations. By combining data on transcriptome and genomic alterations, we have developed a Bayesian framework to infer driver activation state, and further tested our method to highlight both statistical and biological significance by applying our model to TCGA HNSCC patient data.Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors.

The impact of genomic variation on protein phosphorylation states and regulatory networks

Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.

СТРУКТУРНО-ФУНКЦИОНАЛЬНОЕ СОСТОЯНИЕ БЕЛКОВ В ЭРИТРОЦИТАРНЫХ МЕМБРАНАХ ПАЦИЕНТОВ С АРТЕРИАЛЬНОЙ ГИПЕРТЕНЗИЕЙ

Objective. To study the structural and functional state of proteins in erythrocyte membranes of AH patients with asymptomatic brain lesions (ABL) and AH patients without brain lesions who suffered from cerebrovascular accident (CVA). Methods. The study included 47 patients diagnosed with AH. The study groups were formed based on the detected neuroimaging changes in the brain and analysis of the structural and functional state of proteins in the membranes of erythrocytes in patients with AH. The following groups were identified: 1-рatients with AH without BL 2-patients with AH and ABL 3-patients with previous cerebrovascular accident Results. A comparative analysis of abnormalities of structural and functional state of proteins in erythrocyte membranes in patients with AH without brain lesions, in patients with AH with asymptomatic brain lesions visualized by MRI, and patients who suffered from cerebrovascular accident showed the following: 1. In patients with AH with asymptomatic brain lesions, and in those suffered from cerebrovascular accident, the value of the maximum reaction rate (Vmax) of AChE in erythrocyte membranes was significantly reduced in comparison with the values of this parameter in patients with AH without brain lesions (Figure 1). 2. The value of Michaelis constant (MC) for AChE in isolated erythrocyte membranes of patients with AH without brain lesions and with asymptomatic brain lesions is increased compared with the group of patients with a history of cerebrovascular accident (Figure 2). Conclusion. In patients with hypertension, the structural and functional state of proteins in erythrocyte membranes is disturbed, depending on the severity of the damage to the brain.

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel

Mechanosensitive channel of large conductance (MscL) detects and responds to changes in the pressure profile of cellular membranes and transduces the mechanical energy into electrical and/or chemical signals. MscL can be activated using ultrasonic or chemical activation methods to improve the absorption of medicines and bioactive compounds into cells. However, re-engineering chemical signals such as pH change can trigger channel activation in MscL. This study elucidates the activation mechanism of an engineered MscL at an atomic level through a combination of equilibrium and non-equilibrium (NE) molecular dynamics (MD) simulations. Comparing the wild-type (WT) and engineered MscLactivation processes suggests that the two systems are likely associated with different active states and different transition pathways. These findings indicate that (1) periplasmic loops play a key role in the activation process of MscL, (2) the loss of various backbone-backbone hydrogen bonds and salt bridge interactions in the engineered MscLchannel causes the spontaneous opening of the channel, and (3) the most significant interactions lost during the activation process are between the transmembrane helices 1 and 2 in engineered MscLchannel. The orientation-based biasing approach for producing and optimizing an open MscL model used in this work is a promising way to characterize unknown protein functional states and investigate the activation processes in ion channels and transmembrane proteins in general. This work paves the way for a computational framework for engineering more efficient pH-sensing mechanosensitive channels.

Survival of Cryopreserved Stallion Spermatozoa and the Level of Oxidative Modification of Sperm Plasma Proteins

Sperm freezing is one of the ways to preserve the sperm of rare, high-productive, physically strong stallions. Freezing negatively affects the reproductive characteristics of sperm, because of an increase in oxidative stress (OS) in particular. Macromolecules, especially proteins, are damaged in the result of OS. Aldehyde and ketone derivatives are formed, which are stable and available for diagnosis. The aim of the study was to determine the level of spontaneous oxidative protein modification (OPM) of sperm plasma in stallions with high and low sperm survival rates during hypothermic storage of cryopreserved sperm. Two experimental groups were formed: the 1st group consisted of 10 stallions, the survival rate of thawed after freezing spermatozoa of which ranged from 6 to 18h (average 11.0±2.8), the 2nd group included 12 stallions with sperm survival from 72 to 96h (average value - 84.1±2.4). Sperm survival time was defined as the ability of spermatozoa to maintain progressive motility above 5% during hypothermic (+4°C) storage of sperm. To assess the level of OPM in spermoplasma, sperctrophotometric analysis of 2,4-dinitrophenylhydrazones (DNPH) formed during the interaction of carbonyl derivatives of proteins with 2,4-dinitrophenylhydrazine (spectrophotometer “SF-2000”, Russia) was used. The R.L. Levine’ technique was modified by E.E. Dubinina. Measurements of DNPH derivatives were carried out at 14 wavelengths to record neutral aldehyde dinitrophenylhydrazones (ADNPHn) in the range 260-280nm, the main character (ADNPHb) - 258-264 and 428-520nm, to determine ketone-dinitrophenylhydrazones of a neutral nature (CDNPHn) 363-370nm, basic character (CDNPHb) - 430-434 and 524-535nm. The area under the absorption spectrum of DNPH derivatives of carbonyl derivatives of proteins (S) was analyzed. The total level of OPM in sperm plasma in group 1 stallions was statistically significantly higher than in group 2 stallions (677.7 Eod/g protein and 476.2 Eod/g protein, respectively, p<0.05). The result indicates a higher severity of OS in the sperm plasma of stallions with a low sperm survival rate, more significant damage to amino acid residues of proteins. OPM of sperm plasma leads to changes in their secondary, tertiary structure that negatively affects the functional state of proteins, the morphological and reproductive characteristics of sperm deteriorate. Damaged proteins can undergo aggregation and fragmentation processes. The accumulation of OPM products, protein aggregates resistant to proteolysis, disrupts cell metabolism, leading to apoptosis. The total level of OPM of sperm plasma in stallions with low survival rates of thawed after freezing spermatozoa is higher than in animals with high survival rates.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps.

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

Proteomic analysis reveals the effects of melatonin on soybean root tips under flooding stress

Flooding constrains soybean growth, while melatonin enhances the ability of plants to tolerate abiotic stresses. To interpret the melatonin-mediated flooding response in soybeans, proteomic analysis was performed in root tips. Retarded growth and severe cell death were observed in flooded soybeans, but these phenotypes were ameliorated by melatonin treatment. A total of 634, 1401, and 1205 proteins were identified under control, flood, and flood plus melatonin conditions, respectively; and these proteins were predominantly associated with metabolism of protein, RNA, and the cell wall. Among these melatonin-induced proteins, eukaryotic aspartyl protease family protein was increased after flood compared with melatonin treatment group, in accordance with its upregulated transcript levels during stress. Eukaryotic translation initiation factor 5A was decreased after flood compared with melatonin. When stress was prolonged, its transcript levels were upregulated by flood, while they were not changed by melatonin. Furthermore, 13-hydroxylupanine O-tigloyltransferase was decreased by flood compared with melatonin; however, its transcription was upregulated by melatonin. In addition, reduced lignification in root tips of flooded soybeans was restored by melatonin. These results suggest that factors related to protein degradation and functional states of RNA play critical roles in promoting the effects of melatonin on soybean plants under flooding. SignificanceFlooding stress threatens soybean growth, while melatonin treatment enhances plant tolerance to stress stimuli. To examine the effects of melatonin on flooded soybeans, morphological analysis was performed. Melatonin promoted soybean growth as judged from greater fresh weight of plant, longer seedling length, and less evident cell death in flooding-stressed soybeans treated with melatonin than those plants exposed to flood alone. Proteomic analysis was conducted to explore the promoting effects of melatonin on soybeans under flooding stress. As a result, metabolism of protein metabolism, RNA regulation, and cell wall was enriched by proteins identified under control, flood, and flood plus melatonin conditions. Among these melatonin-induced proteins, abundance of eukaryotic aspartyl protease family protein, eukaryotic translation initiation factor 5A, and 13-hydroxylupanine O-tigloyltransferase displayed similar change patterns between the control and melatonin compared with flood; and transcript levels of genes encoding these proteins responded to flooding stress and melatonin treatment. In addition, activated cell degradation, expanded intercellular spaces, and reduced lignification in root tips of flooded soybeans were ameliorated by melatonin treatment.

Protein Conformational Dynamics upon Association with the Surfaces of Lipid Membranes and Engineered Nanoparticles: Insights from Electron Paramagnetic Resonance Spectroscopy.

Detailed study of conformational rearrangements and dynamics of proteins is central to our understanding of their physiological functions and the loss of function. This review outlines the applications of the electron paramagnetic resonance (EPR) technique to study the structural aspects of proteins transitioning from a solution environment to the states in which they are associated with the surfaces of biological membranes or engineered nanoobjects. In the former case these structural transitions generally underlie functional protein states. The latter case is mostly relevant to the application of protein immobilization in biotechnological industries, developing methods for protein purification, etc. Therefore, evaluating the stability of the protein functional state is particularly important. EPR spectroscopy in the form of continuous-wave EPR or pulse EPR distance measurements in conjunction with protein spin labeling provides highly versatile and sensitive tools to characterize the changes in protein local dynamics as well as large conformational rearrangements. The technique can be widely utilized in studies of both protein-membrane and engineered nanoobject-protein complexes.

Metabolites modulate the functional state of human uridine phosphorylase I.

Metabolic pathways in cancer cells typically become reprogrammed to support unconstrained proliferation. These abnormal metabolic states are often accompanied by accumulation of high concentrations of ATP in the cytosol, a phenomenon known as the Warburg Effect. However, how high concentrations of ATP relate to the functional state of proteins is poorly understood. Here, we comprehensively studied the influence of ATP levels on the functional state of the human enzyme, uridine phosphorylase I (hUP1), which is responsible for activating the chemotherapeutic pro-drug, 5-fluorouracil. We found that elevated levels of ATP decrease the stability of hUP1, leading to the loss of its proper folding and function. We further showed that the concentration of hUP1 exerts a critical influence on this ATP-induced destabilizing effect. In addition, we found that ATP interacts with hUP1 through a partially unfolded state and accelerates the rate of hUP1 unfolding. Interestingly, some structurally similar metabolites showed similar destabilization effects on hUP1. Our findings suggest that metabolites can alter the folding and function of a human protein, hUP1, through protein destabilization. This phenomenon may be relevant in studying the functions of proteins that exist in the specific metabolic environment of a cancer cell.