A new technique for predicting intrinsically disordered regions based on average distance map constructed with inter-residue average distance statistics

The sequences of azurin and titin, β sandwich proteins, are analyzed based on inter-residue average distance statistics. A kind of predicted contact map based on inter-residue average distance statistics (Average Distance Map, ADM) is used to pinpoint regions of possible compact regions for two proteins. We compare predicted compact regions with the positions of the residues with experimental high Φ values for these proteins reported in the literature. The results reveal that the regions predicted by ADMs correspond to the positions of residues with the high Φ value. Furthermore, we perform random sampling of 3D conformations using these protein sequences with a potential derived from inter-residue average distance statistics. It is demonstrated that the residues with highest contact frequency during the simulations qualitatively correspond to the residues with the highest Φ values for these proteins. Importantly, analysis with inter-residue average distance statistics predicts the properties of folding processes of the β sandwich proteins starting from only sequence information.

It is well-known that the IgG-binding domain from staphylococcal protein A folds into a 3 alpha helix bundle structure, while the IgG-binding domain of streptococcal protein G forms an (alpha + beta) structure. Recently, He et al. (Biochemistry 44:14055-14061, 2005) made mutants of these proteins from the wild types of protein A and protein G strains. These mutants are referred to as protein A219 and protein G311, and it was showed that these two mutants have different 3D structures, i.e., the 3 alpha helix bundle structure and the (alpha + beta) structure, respectively, despite the high sequence identity (59%). The purpose of our study was to clarify how such 3D structural differences are coded in the sequences with high homology. To address this problem, we introduce a predicted contact map constructed based on the interresidue average-distance statistics for prediction of folding properties of a protein. We refer to this map as an average distance map (ADM). Furthermore, the statistics of interresidue distances can be converted to an effective interresidue potential. We calculated the contact frequency of each residue of a protein in random conformations with this effective interresidue potential, and then we obtained values similar to phi values. We refer to this contact frequency of each residue as a p(mu) value. The comparison of the p(mu) values to the phi values for a protein suggests that p(mu) values reveal the information on the folding initiation site. Using these techniques, we try to extract the information on the difference in the 3D structures of protein A219 and protein G311 coded in their amino acid sequences in the present work. The results show that the ADM analyses and the p(mu) value analyses predict the information of folding initiation sites, which can be used to detect the 3D difference in both proteins.

One of the goals of molecular bioinformatics is decoding amino acid sequences to extract information on the principles of protein folding. However, this is difficult to perform with standard bioinformatics techniques such as multiple sequence alignment and so on. Thus, we propose a technique based on inter-residue average distance statistics to make predictions regarding the protein folding mechanisms of amino acid sequences. Our method involves constructing a kind of predicted contact map called an Average Distance Map (ADM) based on average distance statistics to pinpoint regions of possible folding nuclei for proteins. Only information on the amino acid sequence of a given protein is required for the present method. In this article, we summarize the results of studies using our method to analyze how specific protein sequences affect folding properties. In particular, we present studies on proteins in the phage lysozyme, such as the globin, fatty acid binding protein-like, and the cupredoxin-like fold families. In the present review, we characterize the 3D architectures of these proteins through the properties of the protein ADMs. Furthermore, we combine the information on the conserved residues within the regions predicted by the ADMs with our results obtained so far. Such information may help identify the folding characteristics of each protein. We discuss this possibility in the present review.

BackgroundThe details of the folding mechanisms have not yet been fully understood for many proteins, and it is believed that the information on the folding mechanism of a protein is encoded in its amino acid sequence. β-trefoil proteins are known to have the same 3D scaffold, namely, a three-fold symmetric scaffold, despite the proteins’ low sequence identity among superfamilies. In this study, we extract an initial folding unit from the amino acid sequences of irregular β-trefoil proteins by constructing an average distance map (ADM) and utilizing inter-residue average distance statistics to determine the relative contact frequencies for residue pairs in terms of F values. We compare our sequence-based prediction results with the packing between hydrophobic residues in native 3D structures and a Gō-model simulation.ResultsThe ADM and F-value analyses predict that the N-terminal and C-terminal regions are compact and that the hydrophobic residues at the central region can be regarded as an interaction center with other residues. These results correspond well to those of the Gō-model simulations. Moreover, our results indicate that the irregular parts in the β-trefoil proteins do not hinder the protein formation. Conserved hydrophobic residues on the β5 strand are always the interaction center of packing between the conserved hydrophobic residues in both regular and irregular β-trefoil proteins.ConclusionsWe revealed that the β5 strand plays an important role in β-trefoil protein structure construction. The sequence-based methods used in this study can extract the protein folding information from only amino acid sequence data, and well corresponded to 3D structure-based Gō-model simulation and available experimental results.

To understand the folding mechanism of a protein is one of the goals in bioinformatics study. Nowadays, it is enigmatic and difficult to extract folding information from amino acid sequence using standard bioinformatics techniques or even experimental protocols which can be time consuming. To overcome these problems, we aim to extract the initial folding unit for titin protein (Ig and fnIII domains) by means of inter-residue average distance statistics, Average Distance Map (ADM) and contact frequency analysis (F-value). TI I27 and TNfn3 domains are used to represent the Ig-domain and fnIII-domain, respectively. Beta-strands 2, 3, 5, and 6 are significant for the initial folding processes of TI I27. The central strands of TNfn3 were predicted as a primary folding segment. Known 3D structure and unknown 3D structure domains were investigated by structure or non-structure based multiple sequence alignment, respectively, to learn the conserved hydrophobic residues and predicted compact region relevant to evolution. Our results show good correspondence to experimental data, phi-value and protection factor from H-D exchange experiments. The significance of conserved hydrophobic residues near F-value peaks for structural stability using hydrophobic packing is confirmed. Our prediction methods once again could extract a folding mechanism only knowing the amino acid sequence.

Intrinsically Disordered Proteins

The location of structural domains in proteins is predicted from the amino acid sequence, based on the analysis of a computed contact map for the protein, the average distance map (ADM). Interactions between residues i and j in a protein are subdivided into several ranges, according to the separation [i-j[ in the amino acid sequence. Within each range, average spatial distances between every pair of amino acid residues are computed from a data base of known protein structures. Infrequently occurring pairs are omitted as being statistically insignificant. The average distances are used to construct a predicted ADM. The ADM is analyzed for the occurrence of regions with high densities of contacts (compact regions). Locations of rapid changes of density between various parts of the map are determined by the use of scanning plots of contact densities. These locations serve to pinpoint the distribution of compact regions. This distribution, in turn, is used to predict boundaries of domains in the protein. The technique provides an objective method for the location of domains both on a contact map derived from a known three-dimensional protein structure, the real distance map (RDM), and on an ADM. While most other published methods for the identification of domains locate them in the known three-dimensional structure of a protein, the technique presented here also permits the prediction of domains in proteins of unknown spatial structure, as the construction of the ADM for a given protein requires knowledge of only its amino acid sequence.

It is a general notion that proteins with very similar three-dimensional structures would show very similar folding kinetics. However, recent studies reveal that the folding kinetic properties of some proteins contradict this thought (i.e., the members in a same protein family fold through different pathways). For example, it has been reported that some beta-proteins in the intracellular lipid-binding protein family fold through quite different pathways (Burns et al., Proteins 1998;33:107-118). Similar differences in folding kinetics are also observed in the members of the globin family (Nishimura et al., Nat Struct Biol 2000;7:679-686). In our study, we examine the possibility of predicting qualitative differences in folding kinetics of the intracellular lipid-binding proteins and two globin proteins (i.e., myoglobin and leghemoglobin). The problem is tackled by means of a contact map based on the average distance statistics between residues, the Average Distance Map (ADM), as constructed from sequence. The ADMs for the three proteins show overall similarity, but some local differences among maps are also observed. Our results demonstrate that some properties of the protein folding kinetics are consistent with local differences in the ADMs. We also discuss the general possibility of predicting folding kinetics from sequence information.

Editor's evaluation: Fixation can change the appearance of phase separation in living cells

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Fixing cells with paraformaldehyde (PFA) is an essential step in numerous biological techniques as it is thought to preserve a snapshot of biomolecular transactions in living cells. Fixed-cell imaging techniques such as immunofluorescence have been widely used to detect liquid–liquid phase separation (LLPS) in vivo. Here, we compared images, before and after fixation, of cells expressing intrinsically disordered proteins that are able to undergo LLPS. Surprisingly, we found that PFA fixation can both enhance and diminish putative LLPS behaviors. For specific proteins, fixation can even cause their droplet-like puncta to artificially appear in cells that do not have any detectable puncta in the live condition. Fixing cells in the presence of glycine, a molecule that modulates fixation rates, can reverse the fixation effect from enhancing to diminishing LLPS appearance. We further established a kinetic model of fixation in the context of dynamic protein–protein interactions. Simulations based on the model suggest that protein localization in fixed cells depends on an intricate balance of protein–protein interaction dynamics, the overall rate of fixation, and notably, the difference between fixation rates of different proteins. Consistent with simulations, live-cell single-molecule imaging experiments showed that a fast overall rate of fixation relative to protein–protein interaction dynamics can minimize fixation artifacts. Our work reveals that PFA fixation changes the appearance of LLPS from living cells, presents a caveat in studying LLPS using fixation-based methods, and suggests a mechanism underlying the fixation artifact. Editor's evaluation Chemically fixing cells for fluorescence microscopy is a common practice in cell biology. However, fixation artifacts can lead the incorrect interpretations of experimental results. This article presents compelling evidence showing that in the context of liquid condensates formed by liquid–liquid phase separation (LLPS), paraformaldehyde (PFA) fixation creates a number of artifacts – such as changes in the number, appearance, or disappearance of liquid condensates. These important findings will be of great interest not only for those in the LLPS field but for any cell biologists using fixed samples for microscopy. https://doi.org/10.7554/eLife.79903.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest A typical human cell is a crowded soup of thousands of different proteins. One way that the cell organizes this complex mix of contents is by creating separate droplets within the cell, like oil in water. These droplets can form through a process known as liquid-liquid phase separation, or LLPS, where specific proteins gather in high concentrations to carry out their cellular roles. The critical role of LLPS in cellular organization means that it is widely studied by biologists. To detect LLPS, researchers often subject the cells to treatments designed to hold all the proteins in place, creating a snapshot of their natural state. This process, known as fixing, allows scientists to easily label a protein with a fluorescent tag, take pictures of the cells, and look at whether the protein forms droplets in its natural state. This is often easier to do than imaging cells live, but it relies on LLPS being well-preserved upon fixation. To test if this is true, Irgen-Gioro, Yoshida et al. looked at protein droplets in live cells, and then fixed the cells to check whether the appearance of the droplets had changed. The images taken showed that fixation could alter the size and number of droplets depending on the protein being studied. To explain why the effects of fixing change depending on the protein, Irgen-Gioro, Yoshida et al. hypothesized that a faster fixation – relative to how quickly proteins can bind and unbind to their droplets – can better preserve the LLPS droplets. They verified their idea using a microscopy technique in which they imaged single molecules, allowing them to see how different fixation speeds relative to protein binding affected the droplets. The work of Irgen-Gioro, Yoshida et al. identifies an important caveat to using fixation for the study of LLPS in cells. Their findings suggest that researchers should be cautious when interpreting the results of such studies. Given that LLPS in cells is an area of research with a lot of interest, these results could benefit a broad range of biological and medical fields. In the future, Irgen-Gioro, Yoshida et al.’s findings could prompt scientists to develop new fixing methods that better preserve LLPS in cells. Introduction Fixing cells to preserve a snapshot of biomolecular transactions in vivo is a widely used strategy in numerous techniques in biology and medicine. Due to its small size and high reactivity with a wide range of biological entities, paraformaldehyde (PFA) is one of the most commonly used fixatives to create covalent cross-linking between biomolecules, for example, proteins and nucleic acids. PFA nonselectively ‘fixes’ or cross-links molecules in proximity to enable characterization of biomolecular interactions formed in living cells. Examples of popular techniques that use PFA to fix cells include ChIP-sequencing (Robertson et al., 2007; Solomon and Varshavsky, 1985), chromosome conformation capture (3C)-based techniques (Dekker et al., 2002), immunofluorescence (Richter et al., 2018), fluorescence in situ hybridization (FISH) (Moter and Göbel, 2000), cross-linking mass spectrometry (Sutherland et al., 2008), super-resolution expansion microscopy (Chen et al., 2015), and super-resolution localization microscopies such as stochastic optical reconstruction microscopy (STORM) (Rust et al., 2006). Although PFA fixation has been used to faithfully preserve live-cell conditions in many scenarios, a number of studies have uncovered situations in which fixation fails to cross-link DNA–protein interactions formed in living cells. By imaging different transcription factors (TFs) in live and fixed cells, Schmiedeberg et al., 2009 showed that TFs bound to DNA with fast dissociation dynamics (<5 s residence times as determined by fluorescence recovery after photobleaching [FRAP]) are not cross-linked to DNA upon PFA fixation. Using live-cell single-molecule imaging, Teves et al., 2016 showed that TFs stay bound to chromosome during mitosis and fixing cells can artificially deplete transiently bound TFs from mitotic chromosomes. These studies exemplify the fact that fixation, with limited reaction rates, cannot provide an instantaneous snapshot and may miss or obfuscate biomolecular interactions that happen either at or faster than the timescale of fixation. What further complicates the result of cell fixation is that the reactivity and reaction rates of PFA are variable and dependent on its biomolecule substrates (Gavrilov et al., 2015; Shishodia et al., 2018). For example, the efficiency and rates at which PFA reacts with proteins can vary by orders of magnitude (Kamps et al., 2019) and are dependent on their amino acid sequences (Kamps et al., 2019; Metz et al., 2004; Sutherland et al., 2008) and tertiary structures (Hoffman et al., 2015). Among the numerous biomolecular transactions investigated using fixed-cell imaging is liquid–liquid phase separation (LLPS), a long-observed behavior of polymers in solution (Gibbs, 1879; Graham, 1861; Hyman et al., 2014) that has recently generated much excitement in biological research communities due to its proposed roles in cellular organization and functions (Banani et al., 2017; Boeynaems et al., 2018; Mitrea and Kriwacki, 2016; Shin and Brangwynne, 2017). LLPS is driven by excessive levels of transient, selective, and multivalent protein–protein interactions mediated by intrinsically disordered regions (IDRs) within the proteins of interest (Chong et al., 2018; Kato and McKnight, 2018; Li et al., 2012). Whereas rigorous characterization of LLPS in vivo has been challenging and remains a question under active investigation (McSwiggen et al., 2019b), detection of discrete puncta that have a spherical shape, undergo fusion and fission, and dynamically exchange biomolecules with the surrounding according to FRAP is often considered evidence of putative LLPS in living cells. While such diverse measurements have been widely used for studying proteins under overexpression conditions, far fewer approaches are available to probe LLPS under physiological conditions. Detecting local high-concentration regions or puncta of an endogenously expressed protein using immunofluorescence of fixed cells has been used in many studies as evidence of LLPS (Boija et al., 2018; Guo et al., 2019; Owen et al., 2021; Xie et al., 2022; Yang et al., 2020). Not only is the detection of puncta an inconclusive metric for establishing LLPS, whether a punctate distribution observed in fixed cells actually represents the live-cell scenario remains unclear as fixation has only been assumed, but not directly shown, to faithfully preserve multivalent interactions and LLPS formed in living cells. This knowledge gap motivated us to image cells that overexpress various known IDR-containing proteins before and after fixation to evaluate the ability of PFA fixation to preserve LLPS behaviors. We found that, interestingly, fixation can significantly alter the appearance of droplet-like puncta in cells. Our quantitative image analysis suggests that depending on the LLPS-driving protein, fixing cells can either enhance or diminish the apparent LLPS behaviors in vivo. In certain cases, fixation can even cause droplet-like puncta to artificially appear in cells that have a homogeneous protein distribution and no detectable puncta in the live condition. Conversely, fixation can also cause droplet-like puncta in living cells to completely disappear. Combining experiments that modulate fixation rates, live-cell single-molecule imaging that quantifies protein binding dynamics, and simulations based on a kinetic model, we further demonstrated that protein localization in fixed cells depends on an intricate balance of protein–protein interaction dynamics, the overall rate of fixation, and the difference between protein fixation rates in and out of droplet-like puncta. Our work urges caution in the interpretation of previous claims of in vivo phase separation based solely on immunofluorescence imaging of fixed cells and serves to guide future judicious application of PFA fixation. Results Fixation enhances the LLPS appearance of FET family proteins To investigate the effect of PFA fixation on the appearance of LLPS, we first compared confocal fluorescence images of live and fixed U2OS cells that transiently express an IDR tagged with EGFP and a nuclear localization sequence (NLS). We focused on the FET family protein IDRs (AA2-214 of FUS, AA47-266 of EWS, and AA2-205 of TAF15) that are reported to undergo putative LLPS in cells upon overexpression (Altmeyer et al., 2015; Chong et al., 2018; Wang et al., 2018). Figure 1, Video 1, and Figure 1—figure supplement 1 compare the same cells before and after treatment of 4% PFA for 10 min unless otherwise noted, a typical condition utilized for fixed-cell imaging techniques such as immunofluorescence. At high enough expression levels, all three IDRs are able to form discrete and spherical puncta in the live cell nucleus, which show fusion and fission behaviors and are thereby consistent with LLPS droplets (Alberti et al., 2019; Banani et al., 2017). Interestingly, after fixation, the puncta of all three IDRs appear to increase in their numbers, sizes, and contrast compared with the dilute phase. In particular, PFA fixation was able to artificially turn a cell with EGFP-EWS(IDR) homogeneously distributed in the nucleus without any puncta into one with many discrete puncta (Figure 1). We quantified the fixation-induced changes of LLPS appearance by calculating three parameters from the fluorescence images of cells, including the number of puncta, surface roughness, and punctate percentage, and found a significant increase in all three parameters after fixation (Figure 1D–F, Figure 1—source data 1). The number of puncta and punctate percentage (percentage of intranuclear fluorescence intensity in the concentrated phase) are indicators of the propensity to phase separate (Berry et al., 2015). The surface roughness (standard deviation of pixel intensities across the nucleus) quantifies the uneven distribution of a fluorescently labeled protein in the nucleus, allowing for detection of puncta appearance or disappearance without the need for an algorithm to identify individual puncta in the cell. Figure 1 with 4 supplements see all Download asset Open asset Fixation can change the apparent liquid–liquid phase separation (LLPS) behaviors of proteins. (A) EGFP-EWS(IDR), (B) EGFP-FUS(IDR), and (C) EGFP-TAF15(IDR) are transiently expressed in U2OS cells and imaged before and after fixation using confocal fluorescence microscopy. A schematic of each protein construct is shown on the left. A maximum z-projection of a representative live cell expressing its respective protein is shown next to that of the same cell after 10 min of fixation with 4% paraformaldehyde (PFA). The inserts show a zoomed-in region of the cell. (D–F) Quantification of percentage change of LLPS parameters after fixation. The values are averaged from 34 (D), 17 (E), or 24 (F) cells measured in 3 (D), 2 (E), or 2 (F) independent transfection and imaging sessions. Error bars represent standard errors. Asterisks indicate a significant difference compared with 0 (p<0.05, Wilcoxon signed-rank test). Figure 1—source data 1 Quantification of puncta parameters used to generate the bar plots. https://cdn.elifesciences.org/articles/79903/elife-79903-fig1-data1-v3.xlsx Download elife-79903-fig1-data1-v3.xlsx Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Real-time imaging of a U2OS cell expressing EGFP-FUS(IDR) during paraformaldehyde (PFA) fixation. We next tested how the fixation artifact is dependent on the length of PFA treatment, PFA concentration, and the type of fixatives. We performed real-time imaging of live cells expressing EGFP-FUS(IDR) and found that their morphology and LLPS appearance start to change immediately upon PFA treatment and reach a steady state after ~100 s of treatment (Video 1, Figure 1—figure supplement 2). We treated cells expressing EGFP-EWS(IDR) with different concentrations of PFA (1, 2, 4, and 8%) and observed statistically significant changes to the above three LLPS-describing parameters upon fixation at all the concentrations (Figure 1—figure supplement 3). PFA in combination with glutaraldehyde (GA) has been shown to reduce fixation artifacts in imaging the distribution of cell membrane receptors (Stanly et al., 2016). However, we still observed statistically significant fixation-induced changes to the apparent LLPS behavior of EGFP-EWS(IDR) using 4% PFA and 0.2% GA in combination (Figure 1—figure supplement 4). We next compared the intracellular distribution of TAF15(IDR) tagged with different fluorescent tags, including, EGFP, DsRed2, and HaloTag, before and after fixation with 4% PFA. The LLPS behavior of DsRed2-TAF15(IDR) is enhanced upon fixation like EGFP-TAF15(IDR) (Figure 2A), but the enhancement has a different appearance. Whereas there is not a significant change to the large preformed DsRed2-TAF15(IDR) puncta, thousands of smaller puncta emerge in the dilute phase within the nucleus (Figure 2B). In contrast, Halo-TAF15(IDR) displays a diminished LLPS behavior after fixation, with its puncta becoming smaller and dimmer or completely disappearing (Figure 2C, Figure 2—figure supplement 1). Quantification of the number of puncta, surface roughness, and punctate percentage of the TAF15(IDR) LLPS systems before and after fixation further confirmed these observations (Figure 2D–F, Figure 2—source data 1). The fact that different phase-separating proteins can have bifurcating behaviors upon fixation is interesting. While it is known that EGFP and DsRed2 can dimerize and HaloTag cannot (Costantini et al., 2012; Sacchetti et al., 2002), it is unclear whether and how the dimerization potential might contribute to the proteins’ bifurcating responses to PFA fixation. We note that the fixation-induced changes to LLPS appearance can affect the physical characterization of in vivo LLPS systems based on fixed-cell imaging, such as the Gibbs energy of transfer between dilute and concentrated phases (Riback et al., 2020) and how far from the critical concentration a system is (Bracha et al., 2018), potentially affecting the interpretation of the functional role of LLPS in cellular processes. Moreover, the fact that PFA fixation can artificially promote puncta formation even in cells without detectable puncta in the live condition presents an important caveat in fixation-based approaches that have been commonly used for characterizing LLPS under physiological conditions, for example, immunofluorescence. Figure 2 with 1 supplement see all Download asset Open asset Paraformaldehyde (PFA) fixation can both enhance and diminish liquid–liquid phase separation (LLPS) appearance. U2OS cells expressing (A) EGFP-TAF15(IDR), (B) DsRed2-TAF15(IDR), and (C) Halo-TAF15(IDR), ligated with the JFX549 Halo ligand, are imaged using confocal fluorescence microscopy before and after 10 min of fixation with 4% PFA. Schematics of the protein constructs are shown on the left. Live- and fixed-cell images are compared. (D–F) Quantification of LLPS parameters after fixation. The values are averaged from 24 (D), 23 (E), or 10 (F) cells measured in 2 (D), 2 (E), or 3 (F) independent transfection and imaging sessions. Error bars represent standard errors. Asterisks indicate a significant difference compared with 0 (p<0.05, Wilcoxon signed-rank test). Figure 2—source data 1 Quantification of puncta parameters used to generate the bar plots. https://cdn.elifesciences.org/articles/79903/elife-79903-fig2-data1-v3.xlsx Download elife-79903-fig2-data1-v3.xlsx Furthermore, to examine whether all phase-separating proteins show the fixation artifact, we compared live- and fixed-cell images of EGFP-tagged full-length FUS (FUS(FL)). Full-length FUS is reported to have a greater LLPS propensity in vitro than its IDR alone (Wang et al., 2018). We found that EGFP-FUS(FL) overexpressed in live U2OS cells forms many small puncta throughout the nucleus, and we did not observe a significant change of this behavior after PFA fixation (Figure 3A, Figure 3—source data 1). We also fused Halo-tagged TAF15(IDR) to FTH1 that forms a 24-mer (Bellapadrona and Elbaum, 2014 and Bracha et al., 2018) to make an artificial protein with a high LLPS propensity. We found that TAF15(IDR)-Halo-FTH1 overexpressed in live U2OS cells forms large droplet-like puncta and the appearance of LLPS does not significantly change after PFA fixation (Figure 3B, Figure 3—source data 1). In addition, we looked into a native IDR-containing protein, EWS::FLI1, an oncogenic TF causing Ewing sarcoma (Grünewald et al., 2018) and known to form local high-concentration hubs at target genes associated with GGAA microsatellites (Chong et al., 2018). Although there is no convincing evidence that EWS::FLI1 undergoes LLPS under physiological conditions, the formation of its hubs is mediated by the homotypic multivalent interactions of EWS(IDR) within the protein. Excessive levels of such multivalent interactions often result in LLPS (Li et al., 2012). We previously Halo-tagged endogenous EWS::FLI1 in an Ewing sarcoma cell line A673 using CRISPR/Cas9-mediated genome editing (Chong et al., 2018). Here, we compared live and fixed A673 cell images of endogenous EWS::FLI1-Halo and did not observe a significant difference in its distribution (Figure 3C, Figure 3—source data 1). This result suggests that PFA fixation does not change the intracellular distribution of all proteins that have a LLPS potential. Figure 3 Download asset Open asset Not all puncta-forming proteins show the fixation artifact. U2OS cells expressing (A) EGFP-FUS(FL) and (B) TAF15(IDR)-Halo-FTH1, and (C) an A673 cell expressing endogenous EWS::FLI1-Halo are imaged using confocal fluorescence microscopy before and after 10 min of fixation with 4% paraformaldehyde (PFA). Halo-tagged proteins are ligated with the JFX549 Halo ligand before imaging. Schematics of the protein constructs are shown on the left. Live- and fixed-cell images are compared. (D–F) Quantification of puncta parameters after fixation. The values are averaged from 21 (D), 16 (E), or 15 (F) cells measured in 1 (D), 4 (E), or 2 (F) independent transfection and imaging sessions. Error bars represent standard errors. NS: not significant difference compared with 0 (p<0.05, Wilcoxon signed-rank test). None of the examined proteins show significant changes in their liquid–liquid phase separation (LLPS) or hub appearance in the fixed-cell image as compared to the live-cell image. Figure 3—source data 1 Quantification of puncta parameters used to generate the bar plots. https://cdn.elifesciences.org/articles/79903/elife-79903-fig3-data1-v3.xlsx Download elife-79903-fig3-data1-v3.xlsx Switching between enhancing and diminishing the LLPS appearance depends on fixation kinetics To understand what factors are underlying the diverging fixation artifact of in vivo LLPS we performed the fixation imaging with to live cells to PFA fixation. is with and is commonly used to the formation of protein–protein cross-linked by quickly and cross-linked (Hoffman et al., 2015). We utilized to generate a fixation reaction in the cell protein–protein fixation. We found that to live U2OS cells that overexpress DsRed2-TAF15(IDR) the punctate percentage from to from 23 an increase in the of LLPS. Although the underlying mechanism of such increase is we this might be because that an important role in TAF15(IDR) LLPS et al., are enhanced by the presence of of the fixation effect on the LLPS behavior of Whereas PFA fixation in the of enhances the LLPS appearance (Figure Figure in the presence of glycine, fixation many of the smaller puncta formed in live cells to completely and preformed puncta to turn into a shape, with the of the puncta still but the of the protein (Figure None of these fixed-cell images are of live cells, but it that the critical parameters that the artifact of PFA fixation. The that the appearance of droplet-like puncta in fixed cells can be by the presence of that the kinetics of fixation can an essential role in the appearance of LLPS in fixed cells. Figure 4 Download asset Open asset fixation creates a fixation artifact. (A) Fixing U2OS cells that express DsRed2-TAF15(IDR) in the of many small puncta to (B) Fixing cells in the presence of results in a in the number of puncta, with large puncta In both (A) and cells are imaged using confocal fluorescence microscopy before and after 10 min of fixation with 4% paraformaldehyde (PFA). the fixation artifact Given that fixation kinetics are critical to the appearance of LLPS in fixed cells, we a kinetic model et al., 2006). shown in Figure and the model on one protein of interest which before fixation can either be in state – or – of molecules are dynamically in and out of puncta, the percentage of is at an determined by the of the binding and the dissociation These are the exchange rates between and and do not the potential in the rates at the For example, individual molecules at the surface and of a might with different rates, but model does not these We the that PFA is as and fixed of which are cross-linked to proteins within with a fixation rate of and cross-linked to proteins with a fixation rate of fixing to both and are when the cell is fixed after of PFA there is no any concentration in and The fixation artifact of an LLPS system can be as the change in punctate percentage, or the of to after Figure Download asset Open asset bifurcating fixation artifacts. (A) that fixation of a phase-separating protein of interest in the cell. (B) The kinetic model with associated kinetic rates the different (C) of the fixation artifact as a of the punctate percentage and the relative fixation rate the overall fixation rate as as overall protein binding and dissociation rates are fixation liquid–liquid phase separation (LLPS) behavior to be fixation LLPS behavior to be of the fixation artifact as a of the punctate percentage and the relative overall fixation rate individual fixation rates are overall fixation rate compared with protein–protein interaction dynamics the fixation artifact. (C) and punctate from to are on (C) and We hypothesized that the balance between interaction and fixation dynamics in a LLPS system the fixation artifact and tested the by calculating as a of various kinetic and is that the dilute and concentrated phases of an LLPS system have different protein and concentrations and 2022; et al., et al., 2015; et al., The rate of fixation is known to vary with both factors by orders of with the timescale of fixation from to (Hoffman et al., 2015; et al., 2019; Metz et al., Metz et al., protein–protein interactions that LLPS are dynamic with binding residence times in the range of to of (Chong et al., 2018), fixation with either or rates than protein binding and We first examined whether different fixation rates of in and out of puncta can cause a fixation artifact, the overall fixation rates are than protein binding and and how the fixation artifact may on protein–protein interaction we as a of the punctate percentage and the relative fixation rate when the relative overall fixation rate is (Figure In the scenario where the rate of fixation is the same in and out of the puncta the live-cell is in fixed cells of the punctate percentage However, when one fixation rate is faster than the we observe a bifurcating the fixation rate the puncta is greater than the puncta the fixed cell will have a punctate percentage than the live cell, that fixation enhances the apparent LLPS behaviors. the balance is the fixed cells will have diminished apparent LLPS behaviors than in the live cell. For where the punctate percentage is or due to significantly different binding and the dissociation rates or no significant change to LLPS appearance after fixation In suggests that fixation rates in and out of puncta is to cause a fixation artifact of LLPS systems a

Cell-cycle regulatory proteins in the podocyte in collapsing glomerulopathy in children

Computational methods, such as the ADM (average distance map) method, have been developed to predict folding of homologous proteins. In this work we used the ADM method to predict the folding pathway and kinetics among selected plant nonsymbiotic (nsHb), symbiotic (Lb), and truncated (tHb) hemoglobins (Hbs). Results predicted that (1) folding of plant Hbs occurs throughout the formation of compact folding modules mostly formed by helices A, B, and C, and E, F, G, and H (folding modules A/C and E/H, respectively), and (2) primitive (moss) nsHbs fold in the C-->N direction, evolved (monocot and dicot) nsHbs fold either in the C-->N or N-->C direction, and Lbs and plant tHbs fold in the C-->N direction. We also predicted relative folding rates of plant Hbs from qualitative analyses of the stability of subdomains and classified plant Hbs into fast and moderate folding. ADM analysis of nsHbs predicted that prehelix A plays a role during folding of the N-terminal domain of Ceratodon nsHb, and that CD-loop plays a role in folding of primitive (Physcomitrella and Ceratodon) but not evolved nsHbs. Modeling of the rice Hb1 A/C and E/H modules showed that module E/H overlaps to the Mycobacterium tuberculosis HbO two-on-two folding. This observation suggests that module E/H is an ancient tertiary structure in plant Hbs.

Describing the whole story of protein folding is currently the main enigmatic problem in molecular bioinformatics study. Protein folding mechanisms have been intensively investigated with experimental as well as simulation techniques. Since a protein folds into its specific 3D structure from a unique amino acid sequence, it is interesting to extract as much information as possible from the amino acid sequence of a protein. Analyses based on inter-residue average distance statistics and a coarse-grained Gō-model simulation were conducted on Ig and FN3 domains of a titin protein to decode the folding mechanisms from their sequence data and native structure data, respectively. The central region of all domains was predicted to be an initial folding unit, that is, stable in an early state of folding. This common feature coincides well with the experimental results and underscores the significance of the β-sandwich proteins' common structure, namely, the key strands for folding and the Greek-key motif, which is located in the central region. We confirmed that our sequence-based techniques were able to predict the initial folding event just next to the denatured state and that a 3D-based Gō-model simulation can be used to investigate the whole process of protein folding.

Proteins in general consist not only of globular structural domains (SDs), but also of intrinsically disordered regions (IDRs), i.e. those that do not assume unique three-dimensional structures by themselves. Although IDRs are especially prevalent in eukaryotic proteins, the functions are mostly unknown. To elucidate the functions of IDRs, we first divided eukaryotic proteins into subcellular localizations, identified IDRs by the DICHOT system that accurately divides entire proteins into SDs and IDRs, and examined charge and hydropathy characteristics. On average, mitochondrial proteins have IDRs more positively charged than SDs. Comparison of mitochondrial proteins with orthologous prokaryotic proteins showed that mitochondrial proteins tend to have segments attached at both N and C termini, high fractions of which are IDRs. Segments added to the N-terminus of mitochondrial proteins contain not only signal sequences but also mature proteins and exhibit a positive charge gradient, with the magnitude increasing toward the N-terminus. This finding is consistent with the notion that positively charged residues are added to the N-terminus of proteobacterial proteins so that the extended proteins can be chromosomally encoded and efficiently transported to mitochondria after translation. By contrast, nuclear proteins generally have positively charged SDs and negatively charged IDRs. Among nuclear proteins, DNA-binding proteins have enhanced charge tendencies. We propose that SDs in nuclear proteins tend to be positively charged because of the need to bind to negatively charged nucleotides, while IDRs tend to be negatively charged to interact with other proteins or other regions of the same proteins to avoid premature proteasomal degradation.

It has been shown that probable portions which form contacts in a protein can be predicted by means of an average distance map (ADM) as well as regular structures (alpha-helices and beta-turns) defined as short-range compact regions (Kikuchi et al., 1988a,c). In this paper, we analyze the occurrence of those portions and short-range compact regions on ADMs for various proteins regarding their folding types. We have found out that each folding type of proteins shows characteristic distribution of such parts on ADMs. We also discuss the possibility of the prediction of folding types of proteins by ADMs.