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Diagrams summarizing the different behaviour of the PFN1 mutants in human (a) and bacterial cells (b), as observed by in-cell 1H-15N SOFAST-HMQC. The greyscale is qualitatively proportional to the signal intensity, from dark grey (no signals) to white (well detected signals); (c) diagram showing the differences in isoelectric point (pI) between the PFN1 mutants (see Table 1), color-coded in red-white-blue.
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In-cell NMR allows obtaining atomic-level information on biological macromolecules in their physiological environment. Soluble proteins may interact with the cellular environment in different ways: either specifically, with their functional partners, or non-specifically, with other cellular components. Such behaviour often causes the disappearance...
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... indicates that the mutations do not affect the folding state and the conformation of PFN1. In order to estimate the fraction of free protein over the total protein, a subset of crosspeaks in the in-cell 1 H-15 N NMR spectra of each mutant were integrated (shown in Fig. 2d), and the average intensity was normalized by the total level of intracellular protein measured by SDS-PAGE (ranging from 170 ± 40 μ M for the "A" mutant to 60 ± 15 μ M for the "AIP" mutant, Supplementary Fig. 4a,c). The obtained data show that, assuming all the "AIP" mutant protein to be free in the cytoplasm, WT and single-type PFN1 mutants are for the most part engaged in some kind of interaction, while ~1/4 of the total "AP" and "IP" mutants and ~2/3 of the "AI" mutant are not interacting (Fig. 2e). ...
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... the triple-type mutant "AIP" was also detected in bacterial cells, and gave rise to detectable signals in the 1 H-15 N NMR spectrum similar to the "AI" and "IP" PFN1 mutants (Fig. 3d). The fraction of free protein over the total protein was estimated in bacterial cells, as done for human cells, by integrating a subset of crosspeaks in the in-cell 1 H-15 N NMR spectra of each mutant (shown in Fig. 3d), and normalizing by the total level of intracellular protein (ranging from 300 ± 100 μ M for WT PFN1 to 100 ± 30 μ M for the "AP" mutant, Supplementary Fig. 4b). The resulting data (Fig. 3e) indicate that the non-specific interactions of PFN1 with components of the bacterial cell involve principally the PtdInsP 2 interaction surface, while the actin-and PLP-binding surfaces give a lesser contribution to the overall behaviour of the protein in the bacterial cytoplasm. ...
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... 3e) show the same trend observed by 1 H NMR in human cells ( Supplementary Fig. 5), confirming that PFN1 is involved in non-specific interactions through the PtdInsP 2 -binding surface in both cellular environments. The different behaviour of the PFN1 mutants in human and bacterial cells, observed by 1 H-15 N NMR, is qualitatively summarized in Fig. 4a,b, and show that the actin-and PLP-binding surfaces contribute less to the interactions of PFN1 in bacteria than in human ...
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... while the "I" mutant was unaffected ( Supplementary Fig. 6c,d). Therefore, the occurrence of interactions with RNA may provide a simple explanation for the undetectability of NMR signals of WT PFN1 in the bacterial cytoplasm, and would also explain the increased detectability of the PFN1 mutants with lower pI (i.e. "I", "AI", "IP" and "AIP", Fig. 4c). In summary, the different patterns of 1 H-15 N NMR signal recovery of the PFN1 mutants obtained in human and bacterial cells indicate that, while non-specific electrostatic interactions are abundant in both envi- ronments, functional interactions also contribute to the 1 H-15 N NMR signal loss in human cells due to the formation of ...
Citations
... 15 N-Labeled PED was expressed in Escherichia coli and purified as previously reported [28]. The In-cell NMR analysis of the protein-small molecules interactions [52,53] were conducted on cell lysates prepared as reported in Farina et al. [29]. ...
In a recent study, we have identified BPH03 as a promising scaffold for the development of compounds aimed at modulating the interaction between PED/PEA15 (Phosphoprotein Enriched in Diabetes/Phosphoprotein Enriched in Astrocytes 15) and PLD1 (phospholipase D1), with potential applications in type II diabetes therapy. PED/PEA15 is known to be overexpressed in certain forms of diabetes, where it binds to PLD1, thereby reducing insulin-stimulated glucose transport. The inhibition of this interaction reestablishes basal glucose transport, indicating PED as a potential target of ligands capable to recover glucose tolerance and insulin sensitivity. In this study, we employ computational methods to provide a detailed description of BPH03 interaction with PED, evidencing the presence of a hidden druggable pocket within its PLD1 binding surface. We also elucidate the conformational changes that occur during PED interaction with BPH03. Moreover, we report new NMR data supporting the in-silico findings and indicating that BPH03 disrupts the PED/PLD1 interface displacing PLD1 from its interaction with PED. Our study represents a significant advancement toward the development of potential therapeutics for the treatment of type II diabetes.
... The intracellular space of living organisms is highly crowded with macromolecules, which can occupy up to nearly one-third of the entire cellular volume [1]. The resulting highly crowded environment poses challenges of nonspecific interactions, critically influencing issues such as protein folding, stability, and adsorption [2][3][4]. In human cells, these issues are especially crucial since the intracellular proteins that fail to fold correctly into their native shapes tend to aggregate and cause cellular malfunction and death, resulting in detrimental pathological consequences [5]. ...
Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.
... Firstly, such toxicity often follows a dose-dependent pattern, where higher concentrations of a substance lead to increased adverse effects [57]. Additionally, at elevated concentrations, DbGTi protein may engage in non-specific interactions with cellular components, disrupting normal physiological processes and triggering stress responses [58]. In the in vitro studies, at the same concentration (45 μM), no toxic effects were observed within the experimental duration of 24 h. ...
... [1] The resulting highly crowded environment poses challenges of nonspecific interactions, critically influencing issues such as protein folding, stability, and adsorption. [2][3][4] In human cells, these issues are specifically crucial since the intracellular proteins that fail to fold correctly into their native shapes tend to aggregate and cause cellular malfunction and death, resulting in detrimental pathological consequences. [5] In particular, cytoplasmic proteins with high abundance, i.e., highly expressed proteins, are more likely to encounter nonspecific interactions due to the law of mass action. ...
Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantify the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was adopted for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, B-factor, beta structure proportion, roughness, and disordered regions proportion. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.
... Such studies have therefore been mostly carried out in E. coli, in which a protein can be recombinantly expressed at high levels. However, for functional studies, the inside of a bacterial cell is clearly very different from that of yeast, and both of them differ from a human cell (Barbieri et al. 2015). Hence, when investigating a protein involved in functional interactions, one may want to mimic as much as possible the environment of the source organism. ...
In-cell NMR, i.e., NMR spectroscopy applied to studying specific macromolecules within living cells, is becoming the technique of choice for the structural and mechanistic description of proteins and nucleic acids within increasingly complex cellular environments, as well as of the temporal evolution of biological systems over a broad range of timescales. Furthermore, in-cell NMR has already shown its potentialities in the early steps of drug development. In this Perspective, we report some of the most recent methodological advancements and successful applications of in-cell NMR spectroscopy, focusing particularly on soluble proteins. We show how the combination of the atomic-level characterization of NMR with its application to a cellular context can provide crucial insights on cellular processes and drug efficacy with unprecedented level of detail. Finally, we discuss the main challenges to overcome and share our vision of the future developments of in-cell NMR and the applications that will be made possible.
... The approach is so capacious that it is possible to construct a classification similar to the structure of this paper: from metabolite to protein [84] from protein to metabolite [85]. Advantages of NMR include versatility, sensitivity and upcoming potential to conduct in vivo studies [86]. A shortcoming of the base method is its limited capacity for working with large (≥50 KDa) proteins, but this limitation can be circumvented by the use of several modifications of the method, such as the Nuclear Overhauser Effect (NOE) or Saturation-Transfer Difference (STD) [83]. ...
Increasing attention has been focused on the study of protein–metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein–protein interactions, protein–metabolite interactions are still not clearly defined. Existing assays for detecting protein–metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein–metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term “interaction” to advance the field of interactomics further.
... An advantage of this technique is that it can also provide structural information about the nature of binding. The use of in-cell NMR can allow the study of biomolecules within living cells, allowing for the study of proteins in the native environment (Barbieri et al. 2015;Li et al. 2017). ...
Within the complex milieu of a cell, which comprises a large number of different biomolecules, interactions are critical for function. In this post-reductionist era of biochemical research, the ‘holy grail’ for studying biomolecular interactions is to be able to characterize them in native environments. While there are a limited number of in situ experimental techniques currently available, there is a continuing need to develop new methods for the analysis of biomolecular complexes that can cope with the additional complexities introduced by native-like solutions. We think approaches that use microfluidics allow researchers to access native-like environments for studying biological problems. This review begins with a brief overview of the importance of studying biomolecular interactions and currently available methods for doing so. Basic principles of diffusion and microfluidics are introduced and this is followed by a review of previous studies that have used microfluidics to measure molecular diffusion and a discussion of the advantages and challenges of this technique.
... As proof of principle, the diffusive motions of proteins in live cells display strong correlations with their net-negative surfacecharge density, and when they are mutated to obtain a lower repulsive charge they tend to get stuck to their intracellular environment (Mu et al., 2017). This situation, which is observed in both bacterial and mammalian cells (Barbieri et al., 2015;Mu et al., 2017;Ye et al., 2019;Leeb et al., 2020aLeeb et al., , 2020b, has led to the interpretation that the diffusive protein-protein interactions are under selective pressure and optimised to assure suitable transientencounter times (Berg and von Hippel, 1985;Schreiber and Fersht, 1996;Camacho et al., 1999;Mu et al., 2017;Wennerstrom et al., 2020). ...
The chemical potential of water (
$ {\mu}_{{\mathrm{H}}_2\mathrm{O}} $
) provides an essential thermodynamic characterization of the environment of living organisms, and it is of equal significance as the temperature. For cells,
$ {\mu}_{{\mathrm{H}}_2\mathrm{O}} $
is conventionally expressed in terms of the osmotic pressure (πosm). We have previously suggested that the main contribution to the intracellular πosm of the bacterium E. coli is from soluble negatively-charged proteins and their counter-ions. Here, we expand on this analysis by examining how evolutionary divergent cell types cope with the challenge of maintaining πosm within viable values. Complex organisms, like mammals, maintain constant internal πosm ≈ 0.285 osmol, matching that of 0.154 M NaCl. For bacteria it appears that optimal growth conditions are found for similar or slightly higher πosm (0.25-0.4 osmol), despite that they represent a much earlier stage in evolution. We argue that this value reflects a general adaptation for optimising metabolic function under crowded intracellular conditions. Environmental πosm that differ from this optimum require therefore special measures, as exemplified with gram-positive and gram-negative bacteria. To handle such situations, their membrane encapsulations allow for a compensating turgor pressure that can take both positive and negative values, where positive pressures allow increased frequency of metabolic events through increased intracellular protein concentrations. A remarkable exception to the rule of 0.25-0.4 osmol, is found for halophilic archaea with internal πosm ≈ 15 osmol. The internal organization of these archaea differs in that they utilize a repulsive electrostatic mechanism operating only in the ionic-liquid regime to avoid aggregation, and that they stand out from other organisms by having no turgor pressure.
... The negative ppm region can be exploited to quantify the relative amount of a folded protein or to qualitatively assess the presence and tumbling rate of the protein of interest. 88,90 The imino region has found broad application in the study of nucleic acids delivered to oocytes or human cells. Indeed, given the high cost of the reagents required to synthesize isotopically labeled DNAs and RNAs in the large amounts required for cellular delivery, being able to observe unlabeled nucleic acids in the background-free 1 H imino region represents a useful compromise. ...
... 117 Our group investigated the extent and the nature of functional interactions versus diffuse, nonspecific interactions experienced by profilin 1 (PFN1), a human protein required for actin polymerization and interacting with many other functional partners. 90 In addition to the finding that PFN1 experienced different types of interactions in bacteria and human cells (see section 3.2), it was observed that some interactions with bacterial components were strong enough to still be present even after cell lysis. ...
... Notably, while the works described above did not explore in detail the molecular nature of the strongly interacting, nonspecific partners causing NMR line broadening, some insight came from further processing the cell lysates. Indeed, a treatment with ribonuclease A in the presence of Mg 2+ disrupted the residual interactions experienced by both Tat-GB1 and PFN1 in bacterial cell lysates, leading to sharper and stronger NMR signals, 90,190 thus suggesting that, for those proteins, the line broadening was caused by the interaction with bacterial RNA. ...
A detailed knowledge of the complex processes that make cells and organisms alive is fundamental in order to understand diseases and to develop novel drugs and therapeutic treatments. To this aim, biological macromolecules should ideally be characterized at atomic resolution directly within the cellular environment. Among the existing structural techniques, solution NMR stands out as the only one able to investigate at high resolution the structure and dynamic behavior of macromolecules directly in living cells. With the advent of more sensitive NMR hardware and new biotechnological tools, modern in-cell NMR approaches have been established since the early 2000s. At the coming of age of in-cell NMR, we provide a detailed overview of its developments and applications in the 20 years that followed its inception. We review the existing approaches for cell sample preparation and isotopic labeling, the application of in-cell NMR to important biological questions, and the development of NMR bioreactor devices, which greatly increase the lifetime of the cells allowing real-time monitoring of intracellular metabolites and proteins. Finally, we share our thoughts on the future perspectives of the in-cell NMR methodology.
... We decided to characterize, by means of NMR in a cell lysate environment, the interaction between PED and PLD1 combining a structural technique with the complexity of the cellular environment, including macromolecular crowding, its pH and redox properties. 27 Our choice is based mainly on two ideas: to verify under conditions as close as possible to the physiological and in presence of the entire PLD1 whether the identified interacting surface of PED is confirmed and to reveal protein−protein or protein-small molecules interactions that can possibly act as a connection between structural and cellular biology. 28 To identify PED residues involved in the interaction with PLD1, PLD1 was expressed as heterologous protein in HEK 293 cells to perform NMR experiments in the presence of 15 Nlabeled PED/PEA15. ...
... Soluble proteins when located in their physiological environment are frequently involved in many weak, non-specific interactions with various cellular components. 27 Such proteins, although, also interact with their specific partners, and if conditions do not prevent further analysis, many of these interactions can be characterized and preciously complement the data that have been already obtained in vitro. PED is a small multifunctional protein scaffold, which regulates various cellular functions through its interaction with other proteins, such as FADD and caspase 8 in apoptotic processes, 30−32 ERK1/2 in the ERK/MAPK kinase cascade, and PLD1 phospholipase in type II diabetes. ...
