Probing cellular protein complexes using single-molecule pull-down

An innovative marriage of techniques, combining the principles of common protein pull-down assays with single-molecule fluorescence microscopy, opens up new ways of visualizing cellular protein complexes. See Article p.484 Analysis of protein interactions is crucial for understanding cellular function and regulation. Here, Taekjip Ha and colleagues develop a novel method for elucidating the identity and stoichiometry of protein complexes from cells and tissues at single-molecule resolution. The method, called single molecule pull-down or SiMPull, can discriminate between multiple association states of a protein, and simultaneously allows determination of complex stoichiometry through photobleaching step analysis. The potential of the assay is demonstrated in a variety of contexts, including endogenous proteins from tissue extracts, organelles and membrane proteins.

High confidence definition of protein interactions is an important objective toward the understanding of biological systems. Isotope labeling in combination with affinity-based isolation of protein complexes has increased in accuracy and reproducibility, yet, larger organisms--including humans--are hardly accessible to metabolic labeling and thus, a major limitation has been its restriction to small animals, cell lines, and yeast. As composition as well as the stoichiometry of protein complexes can significantly differ in primary tissues, there is a great demand for methods capable to combine the selectivity of affinity-based isolation as well as the accuracy and reproducibility of isotope-based labeling with its application toward analysis of protein interactions from intact tissue. Toward this goal, we combined isotope coded protein labeling (ICPL)(1) with immunoprecipitation (IP) and quantitative mass spectrometry (MS). ICPL-IP allows sensitive and accurate analysis of protein interactions from primary tissue. We applied ICPL-IP to immuno-isolate protein complexes from bovine retinal tissue. Protein complexes of immunoprecipitated β-tubulin, a highly abundant protein with known interactors as well as the lowly expressed small GTPase RhoA were analyzed. The results of both analyses demonstrate sensitive and selective identification of known as well as new protein interactions by our method.

Establishment of a Protein Frequency Library and Its Application in the Reliable Identification of Specific Protein Interaction Partners

The quaternary structure with specific stoichiometry is pivotal to the specific function of protein complexes. However, determining the structure of many protein complexes experimentally remains a major bottleneck. Structural bioinformatics approaches, such as the deep learning algorithm Alphafold2-multimer (AF2-multimer), leverage the co-evolution of amino acids and sequence-structure relationships for accurate de novo structure and contact prediction. Pseudo-likelihood maximization direct coupling analysis (plmDCA) has been used to detect co-evolving residue pairs by statistical modeling. Here, we provide evidence that combining both methods can be used for de novo prediction of the quaternary structure and stoichiometry of a protein complex. We achieve this by augmenting the existing AF2-multimer confidence metrics with an interpretable score to identify the complex with an optimal fraction of native contacts of co-evolving residue pairs at intermolecular interfaces. We use this strategy to predict the quaternary structure and non-trivial stoichiometries of Bacillus subtilis spore germination protein complexes with unknown structures. Co-evolution at intermolecular interfaces may therefore synergize with AI-based de novo quaternary structure prediction of structurally uncharacterized bacterial protein complexes.

Many cellular proteins assemble into macromolecular protein complexes. The identification of protein–protein interactions and quantification of their stoichiometry is therefore crucial to understand the molecular function of protein complexes. Determining the stoichiometry of protein complexes is usually achieved by mass spectrometry-based methods that rely on introducing stable isotope-labeled reference peptides into the sample of interest. However, these approaches are laborious and not suitable for high-throughput screenings. Here, we describe a robust and easy to implement label-free relative quantification approach that combines the detection of high-confidence protein–protein interactions with an accurate determination of the stoichiometry of the identified protein–protein interactions in a single experiment. We applied this method to two chromatin-associated protein complexes for which the stoichiometry thus far remained elusive: the MBD3/NuRD and PRC2 complex. For each of these complexes, we accurately determined the stoichiometry of the core subunits while at the same time identifying novel interactors and their stoichiometry.

Fluorescence Applications in Molecular Neurobiology

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Cryogenic optical localization in three dimensions (COLD) was recently shown to resolve up to four binding sites on a single protein. However, because COLD relies on intensity fluctuations that result from the blinking behavior of fluorophores, it is limited to cases where individual emitters show different brightness. This significantly lowers the measurement yield. To extend the number of resolved sites as well as the measurement yield, we employ partial labeling and combine it with polarization encoding in order to identify single fluorophores during their stochastic blinking. We then use a particle classification scheme to identify and resolve heterogenous subsets and combine them to reconstruct the three-dimensional arrangement of large molecular complexes. We showcase this method (polarCOLD) by resolving the trimer arrangement of proliferating cell nuclear antigen (PCNA) and six different sites of the hexamer protein Caseinolytic Peptidase B (ClpB) of Thermus thermophilus in its quaternary structure, both with Angstrom resolution. The combination of polarCOLD and single-particle cryogenic electron microscopy (cryoEM) promises to provide crucial insight into intrinsic heterogeneities of biomolecular structures. Furthermore, our approach is fully compatible with fluorescent protein labeling and can, thus, be used in a wide range of studies in cell and membrane biology. Editor's evaluation This paper will be of interest to the structural biology community and people working on cryogenic fluorescence microscopy. This paper is a clear step forward in the use of single-molecule localization microscopy at Å resolution, thanks to low-temperature polarized super-resolution imaging and advanced data processing algorithms. https://doi.org/10.7554/eLife.76308.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Proteins and their various assemblies are among the main constituents of all living systems and govern every aspect of cellular physiology in both healthy and disease states (Nelson, 2017; Mavroidis et al., 2004; Schliwa and Woehlke, 2003; Alberts, 1998). These biomolecular structures adopt sophisticated three-dimensional (3D) configurations during their multifaceted conformational changes. A full understanding of their spatial arrangements and associated heterogeneous configurations is crucial for elucidating their molecular mechanisms, helps guide the engineering of new proteins, and is a great asset for drug discovery (Renaud et al., 2018). Indeed, since the pioneering work of Perutz on protein crystals (Fersht, 2008), a variety of techniques such as X-ray crystallography (Shi, 2014) and nuclear magnetic resonance (NMR) spectroscopy (Kanelis et al., 2001) have been explored for gaining insight into protein structure and function. Advances in sample preparation, detector technology, and image processing based on single-particle analysis have also ushered in atomic resolution in cryogenic electron microscopy (cryoEM) studies of protein structure (Nakane et al., 2020; Kühlbrandt, 2014). The inherent resolution of this method is highly desirable, but lack of specific labeling makes it challenging to identify small variations such as structural inhomogeneities. Fluorescence microscopy, on the other hand, draws its success from an exquisite specificity in labeling, but has traditionally suffered from a limited resolution. The recent advent of super-resolution (SR) fluorescence microscopy has opened new avenues for studying subcellular organization and is on the way to become a workhorse for biological studies (Lelek et al., 2021; Sahl et al., 2017; Weisenburger and Sandoghdar, 2015). However, conventional SR microscopy performed at room temperature is still not considered as a contestant in the arena of structural biology, where Angstrom-level information about the molecular architecture of proteins and protein complexes is sought after. To push the limit of fluorescence microscopy, one can perform measurements under cryogenic conditions (Böning et al., 2021, Hoffman et al., 2020; Dahlberg et al., 2020; Moser et al., 2019; Wang et al., 2019; Hulleman et al., 2018b, Xu et al., 2018; Weisenburger et al., 2017; Furubayashi et al., 2017; Li et al., 2015; Weisenburger et al., 2014; Weisenburger et al., 2013). In addition to slowing down photochemistry, which allows each fluorophore to emit several orders of magnitude more photons than at room temperature (Li et al., 2015; Weisenburger et al., 2014; Weisenburger et al., 2013), a key advantage of cryogenic temperatures is in offering superior sample preservation and high stability for Angstrom-scale structural studies. In one implementation, cryogenic optical localization in 3D (COLD) was introduced, where the stochastic intensity blinking of organic dyes gave access to the positions of up to four labeling sites on a single protein (Weisenburger et al., 2017). We recently employed a more robust protocol to identify individual fluorophores by exploiting the polarization of the fluorescence light dictated by the fluorophore orientation (Böning et al., 2021). This latter method was validated by measuring single distances on one-dimensional DNA nanorulers (Böning et al., 2021). Control of the fluorescence signal via polarization modulation has also been shown to offer an alternative to random blinking (Hulleman et al., 2018a, Hafi et al., 2014). In our current study, we introduce polarCOLD, which exploits polarization encoding for resolving several fluorophores in 3D. Importantly, we show that the distances and arrangements of protein complexes can be determined by combining images recorded from under-sampled structures. To demonstrate this, we first resolve three fluorophores on a trimer protein complex with Angstrom resolution. Next, we use partial labeling, a supervised particle classification procedure to solve a complete hexameric protein arrangement. We discuss the limits of our methodology for resolving structures with a certain degree of disorder as well as its promise for combination with cryoEM. Results polarCOLD on a protein trimer Proliferating cell nuclear antigen (PCNA) is a central functional unit in genome repair and replication (Bruck and O’Donnell, 2001). The structure of this complex protein was solved by X-ray crystallography (Georgescu et al., 2008) and more recently by cryoEM (Madru et al., 2020). These studies have shown that PCNA forms a stable homo-trimer with a pseudo-hexameric shape. The stability and simple configuration of its structure make PCNA a good model system for benchmarking our imaging methodology. To study human PCNA with polarCOLD, we first fully labeled it via a His-tag linker on the N-terminal side of each subunit of the protein, forming an equilateral triangle (see Materials and methods section and Figure 1—figure supplement 1). PCNA complexes were then embedded in a hydrophilic poly-vinyl alcohol (PVA) matrix at sub-nanomolar concentration and spin-coated on a mirror-enhanced substrate (see Böning et al., 2021 for in-depth characterization of the mirror-enhanced substrate). The resulting density corresponds to fewer than one protein per μm2 on average so that individual proteins can be easily identified in diffraction-limited imaging. The samples were immediately imaged in our custom-built microscope (see Materials and methods section). Figure 1a shows a schematic of the imaging setup. A liquid helium cryostat houses the cold stage as well as a scannable microscope objective (Weisenburger et al., 2017; Böning et al., 2021). A polarizing beam splitter in the detection path allows us to determine the orientation of dipole-like emitters projected onto the angular interval θ ϵ [0°, 90°] in the imaging plane. The high photostability of the fluorophores at T=4 K allows us to collect on average 260 photons per frame (per 14 ms) from a single fluorophore with a total number of registered photons exceeding 106 after 50,000 frames (Figure 1—figure supplement 2). Figure 1b–c (blue trace) displays two examples in which three different polarization states recur at various times. The long photo-blinking off-times (Figure 1—figure supplement 2d) allow one to identify each of the three fluorophores on a given protein complex separately (Weisenburger et al., 2017). More insight into polarization trace processing can be found in Figure 1—figure supplement 3. To resolve the polarization histograms in cases where they partially overlap (see, e.g., Figure 1c), we fit the data using an algorithm that combines unsupervised statistical learning tools with change-point detection in a model-independent manner (White et al., 2020). As illustrated by the red traces in Figure 1b and c, we can robustly identify the polarization states over time and hence assign the signal in each frame to a single fluorophore. We also verified the robustness of our assignment procedure by performing random assignment of frames, which resulted in single, unresolved spots (see Materials and methods section and Figure 1—figure supplement 4). Figure 1 with 6 supplements see all Download asset Open asset Photo-physics and co-localization of fluorophores at cryogenic temperatures. (a) Schematics of the cryogenic optical microscope. Polarization-resolved detection allows for direct measurement of the in-plane dipole moment of fluorophores. Here we use circularly polarized light from a laser at λ=635 nm. A polarizing beam splitter in the detection path allows one to resolve the polarization state of each individual molecule. (b, c) Exemplary polarization time traces of two single proteins. (b) demonstrates a case of well-separated polarization states, whereas (c) displays a case with smaller separations between polarization states. Blue traces present the experimental polarization values for each frame, and the red lines show the polarization determined by the algorithm (White et al., 2020). Top panels shows the residuals of the fit. The blinking kinetics are exceptionally slow with on/off times in the range of seconds to minutes. Figure 1—source data 1 Exemplary polarization time traces of two single proteins for Figure 1b-c. https://cdn.elifesciences.org/articles/76308/elife-76308-fig1-data1-v1.zip Download elife-76308-fig1-data1-v1.zip Figure 1—source data 2 Overview of the recorded data and the experimental yield. https://cdn.elifesciences.org/articles/76308/elife-76308-fig1-data2-v1.zip Download elife-76308-fig1-data2-v1.zip The number of fluorophores that can be simultaneously resolved depends on the blinking on-off dynamics (see Materials and methods section). Furthermore, the shot noise determines the angular resolution and, thus, the maximum number of resolvable polarization states per protein. This, in turn, directly affects the yield of resolved particles (see Figure 1—figure supplements 5–6). For example, in our current experiment, we used one polarization basis and projected all orientations to the limited space of θ ϵ [0°, 90°]. By taking the experimental angular resolution of ca. 5° (Figure 1—figure supplements 5–6), we can theoretically expect to resolve 70% of the particles which contain 3 fluorophores, and roughly 15% of the particles which contain 6 fluorophores. Adding a second polarization basis at a tilt of 45°, would allow one to double the angular space (Stallinga and Rieger, 2012). Moreover, using polarized illumination (Backer et al., 2016; Zhanghao et al., 2019) and a full 3D characterization of the dipole moments (Lieb et al., 2004; Mortensen et al., 2010; Hulleman et al., 2021) would result in less overlap and thus enhanced capacity for identifying different polarization states. We remark, however, that even in our current scheme, one can improve the number of resolved polarization states by excluding the ambivalent cases, albeit at the expense of the overall yield (see Figure 1—figure supplement 6 and Figure 1—source data 2 for the statistics of this analysis). Having identified the individual fluorophores, we generate super-resolved images by clustering the respective coordinates and taking their averages (see Figure 2—figure supplements 1 and 2 for moving from traces to 2D resolved image). The top and bottom rows in Figure 2a display a selection of the measured and simulated 2D projection maps. To quantify their similarity, we computed a correlation score ranging from 0 to 1. We obtained 2D correlation scores of 0.92 or higher, representing nearly perfect agreement. To obtain a 3D model from our 2D localization maps, we use a single-particle reconstruction algorithm (Dvornek et al., 2015; Weisenburger et al., 2017; see Materials and methods section and Figure 2—figure supplement 3 for complete data set). Figure 2b shows that the reconstructed fluorophore volumes (red spheroids) agree well with the crystal structure of the PCNA protein (PDB: 1AXC) containing three identical subunits in an equilateral triangle. The slight asymmetry and deviation from the actual crystal structure can in part be attributed to the uncertainty introduced by the dye linker, 6-histidine linker and possibly the restricted rotational mobility of the dye itself, resulting in a minor localization bias. Indeed, by taking the dye linker into account and calculating the accessible volume (Kalinin et al., 2012), we found that our 3D reconstructed volumes correlate very well (0.96 correlation score) with the simulated accessible volumes (see Figure 2b, Animation 1). Figure 2 with 3 supplements see all Download asset Open asset 3D reconstruction of the PCNA protein trimer. (a) Experimentally obtained super-resolved 2D images (top row) of single proteins and simulated images based on the crystal structure (bottom row). The color code represents the occupation probability determined by the localization precision for each fluorophore. The localization precision in the simulated data was normalized. Scale bar is 3 nm. (b) Overlay of the crystal structure of human PCNA with the reconstructed fluorophore volumes shown as red spheroids (see online Animation 1). The transparent white clouds represent the accessible volume of the dye linker attached to the N-terminal side of the protein, calculated using the parameter of ATTO647N as provided in Kalinin et al., 2012. By fitting the reconstructed 3D volumes obtained from polarCOLD into the theoretical accessible volumes of the dyes, we find a correlation score of 0.96, indicating a correct 3D reconstruction. (c) The Fourier shell correlation (FSC, blue curve) of the two half data sets gives a resolution of 4.9 Å based on the half-bit criterion (red curve). (d) Distribution of the projected side lengths (blue) obtained from the localized positions shown in (b). The model fit (red) takes the finite localization uncertainty and the random particle orientation into account, resulting in 9.9±0.6 nm. The error of the model fit was estimated from 200 fits. The reconstructed 3D volume was calculated from 119 particles. Figure 2—source data 1 Full dataset coordinates of the 2D images used for 3D reconstruction for Figure 2a. https://cdn.elifesciences.org/articles/76308/elife-76308-fig2-data1-v1.zip Download elife-76308-fig2-data1-v1.zip Figure 2—source data 2 Human PCNA 3D reconstructed map for Figure 2b. https://cdn.elifesciences.org/articles/76308/elife-76308-fig2-data2-v1.zip Download elife-76308-fig2-data2-v1.zip Figure 2—source data 3 Fourier shell correlation data for Figure 2c. https://cdn.elifesciences.org/articles/76308/elife-76308-fig2-data3-v1.zip Download elife-76308-fig2-data3-v1.zip Figure 2—source data 4 Distance histogram data and model fit for Figure 2d. https://cdn.elifesciences.org/articles/76308/elife-76308-fig2-data4-v1.zip Download elife-76308-fig2-data4-v1.zip Animation 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 Human PCNA 3D reconstruction. To evaluate the resolution of our 3D reconstructed volumes, we used the well-established method of Fourier shell correlation (FSC) (van Heel and Schatz, 2005). Here, we divide the 2D image data set into two randomly chosen groups and then determine their 3D reconstruction separately. Then we assess the cross-correlation (similarity) between the two 3D volumes in Fourier space as a function of spatial frequency. The overall resolution of a 3D reconstructed volume is thus obtained by finding the maximum spatial frequency corresponding to a correlation above a specific threshold value. Here, we used the half-bit criterion, which is a standard threshold curve used in single-particle cryoEM. As shown in Figure 2c, the intersection of this curve (red) with the FSC curve (blue) indicates at which spatial frequency we have collected a sufficient amount of information in order to interpret the 3D reconstructed volumes accurately (van Heel and Schatz, 2005). We find a remarkable resolution of 4.9 Å. We further quantified the size of the protein-dye conjugate via the pair-wise distances between the localized sites on each particle. The histogram in Figure 2d plots the distribution of the side lengths of the projected triangles and is well-described by a fit that considers the localization uncertainty as well as the random particle orientation (Böning et al., 2021, Weisenburger et al., 2017). We determine a side length of 9.9±0.6 nm in excellent agreement with the expected value. The uncertainty was determined via bootstrapping and is consistent with the resolution obtained from the FSC curve. We note that the high signal-to-noise ratio (SNR) of the method (see, e.g, Figure 2a), and the comparatively low information density per particle, deliver a good results from a total of 119 particles (see Figure 1—source data 2 for overall statistics), which is two to three orders of magnitude lower than the number required for typical cryoEM measurements (Cheng et al., 2015). Indeed, the low SNR in cryo-EM requires data from a large number of particles to be first averaged to establish 2D classes before using them for 3D reconstruction (Rosenthal and Henderson, 2003). In our case, each 2D projection directly contributes to the 3D reconstruction process. Resolving a hexameric protein complex using partial labeling The trimer structure discussed above involves only a single dye-dye distance. We now turn to resolving an example of more complex higher-order protein structures. Considering that a limited number of fluorophores can be resolved via stochastic blinking (Figure 1—figure supplement 6), we pursued a strategy of partial labeling of the sites of interest on a given individual protein complex. The piece-wise information, which involves various fluorophore arrangements and distances is then assembled to solve for the full architecture using prior knowledge of the symmetry. This concept has been successfully used to build structural models in NMR (Fiaux et al., 2002) and more recently in SR microscopy (Heydarian et al., 2018; Molle et al., 2018). To demonstrate this technique, we the Caseinolytic Peptidase B (ClpB) of Thermus thermophilus in its quaternary structure et al., 2003; and Figure supplement is a molecular that proteins from et al., 2013), and its structure was shown to be very stable in the of at low et al., Here, we labeled the of the protein at such that the between two labeling sites is expected to be nm after for the dye linker (Figure supplement We the labeling to in order to allow for of the particles to three fluorophores as estimated by the distribution (Figure supplement complexes were imaged in the as before in the of 2 to the protein We obtained about photons per frame on average and a significantly total number of photons after frames since of the the of the interval (see Figure supplement for The photo-blinking behavior was to that in PCNA with a slight in the average robust fluorophore we our polarization traces as and only cases that three fluorophores. three into three classes and with pair-wise distances of and nm (Figure supplement To the resulting triangle we used a supervised classification scheme et al., to assign each 2D image to one of the three taking the of its into Here, we simulated large data sets of 2D for each and a procedure based on the 2D cross-correlation between an experimental image and the simulated A based on simulated images obtained from the crystal structure of an of in Figure displays examples of the 2D super-resolved images of each (see Figure supplement 2 for more examples of different and Figure data 1 and Figure data 2 for full We each image to the that the correlation that all classes with a for 1 and and for 2 in the score from a see Figure supplement were from further analysis in order to (see Figure 1—source data 2 for all Next, we 2D images of each with a localization precision than 3 nm (Figure supplement and correlation score than and calculated their respective 3D structures as for we obtained and particles for classes and representing a yield of for all the particles with three polarization states (see Figure 1—source data 2). as illustrated in Figure the reconstructed 3D volumes fit very well to the of the dye on the protein structure given by the accessible volumes on each after taking the dye linker into The correlation scores between the reconstructed 3D volumes and the accessible volumes of the dyes were 0.96, and for classes and As a we also performed a reconstruction of 2D and that structure was identified (Figure supplement 4). the FSC of the volumes (see Figure supplement an exquisite resolution of and for classes and the assignment of the three we them as shown in Figure to obtain the complete 3D of the hexamer structure (see Animation 2 and Figure 3 with supplements see all Download asset Open asset 3D reconstruction of the hexamer protein. (a) Top of super-resolved 2D images for classes and as obtained from single-particle classification Scale bar is 3 nm. (b) of single-particle classification and The reconstructed 3D volume of each in the simulated accessible volume of the fluorophores blue and represent classes and with correlation values of 0.96, and (c) 3D reconstruction of the complete hexamer obtained from the 3D volumes (red of the three Top shows the top of the reconstructed 3D volume of the hexamer and the bottom shows its (see online Animation 2 and 2 and structure of is shown as a in (PDB: et al., 2003; and 3D volumes were calculated from and particles for classes and Figure data 1 Full dataset coordinates of the 2D images used for 3D reconstruction of each for Figure Download Figure data 2 Full dataset coordinates of the particles for Figure Download Figure data 3 3D of each for Figure Download Figure data 4 3D of the hexamer complex for Figure Download Animation 2 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 Thermus thermophilus 3D reconstruction Animation 3 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 Thermus thermophilus 3D reconstruction (top classification from prior knowledge of the To the of our method for samples with or side we a procedure as by et al., by the histogram of our particles obtained from single measurements polarization we identify a at nm as the side length of our (see Figure Next, we three models with different for and but all the side length of nm. We simulated an number of for each model and them with our experimental we used the information criterion 2020; et al., 2021) to find the model that our experimental data (see Materials and methods section). As shown in Figure we found that the hexamer structure our data significantly than the other In we a different case of hexamer model with and found that this model resulted in a fit to our data (Figure Figure 4 with 2 supplements see all Download asset Open asset model selection for (a) of pair-wise distances from particles with two polarization states, a clear at nm as the side length of the (b) on the identified side length we models of the with different but the side length of and performed single-particle classification of the experimental The criterion shows that the hexamer is the the of the hexamer results in a fit. (c) of a model from perfect symmetry. is to be a of The classification procedure for 1 nm. Figure data 1 Distance histogram of the hexamer complex for Figure Download of the robustness of our approach for samples and order the of our current study, but one simple strategy would be to a structure such as a hexamer and allow for each to a of (see Figure show that classification of our current data less robust for nm. We that a structure prior knowledge would only be for complete and reconstruction have recently been to other single-molecule localization microscopy and in cases specific have been to degree of partial labeling (Heydarian et al., 2018; et al., 2018; et al., 2017). As shown in Figure supplement however, the not provide a for our We this to the that the structures in our are randomly in are and classes that are A analysis of the of various for structures of different is the of our current Discussion polarCOLD can be further and to other for example via combination with labeling et al., 2020; Dahlberg et al., 2018;

Knowledge of elaborate structures of protein complexes is fundamental for understanding their functions and regulations. Although cross-linking coupled with mass spectrometry (MS) has been presented as a feasible strategy for structural elucidation of large multisubunit protein complexes, this method has proven challenging because of technical difficulties in unambiguous identification of cross-linked peptides and determination of cross-linked sites by MS analysis. In this work, we developed a novel cross-linking strategy using a newly designed MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). DSSO contains two symmetric collision-induced dissociation (CID)-cleavable sites that allow effective identification of DSSO-cross-linked peptides based on their distinct fragmentation patterns unique to cross-linking types (i.e. interlink, intralink, and dead end). The CID-induced separation of interlinked peptides in MS/MS permits MS(3) analysis of single peptide chain fragment ions with defined modifications (due to DSSO remnants) for easy interpretation and unambiguous identification using existing database searching tools. Integration of data analyses from three generated data sets (MS, MS/MS, and MS(3)) allows high confidence identification of DSSO cross-linked peptides. The efficacy of the newly developed DSSO-based cross-linking strategy was demonstrated using model peptides and proteins. In addition, this method was successfully used for structural characterization of the yeast 20 S proteasome complex. In total, 13 non-redundant interlinked peptides of the 20 S proteasome were identified, representing the first application of an MS-cleavable cross-linker for the characterization of a multisubunit protein complex. Given its effectiveness and simplicity, this cross-linking strategy can find a broad range of applications in elucidating the structural topology of proteins and protein complexes.

The recent explosion of high throughput experimental technologies for characterizing protein interactions has generated large amounts of data describing interactions between thousands of proteins and producing genome scale views of protein assemblies. The systems level views afforded by these data hold great promise of leading to new knowledge but also involve many challenges. Deriving meaningful biological conclusions from these views crucially depends on our understanding of the approximation and biases that enter into deriving and interpreting the data. The challenges and rewards of interaction proteomics are reviewed here using as an example the latest comprehensive high throughput analyses of protein interactions in yeast.

An emerging theme in cell signaling is that membrane-bound channels and receptors are organized into supramolecular signaling complexes for optimum function and cross-talk. In this study, we determined how protein kinase C (PKC) phosphorylation influences the scaffolding protein Na(+)/H(+) exchanger regulatory factor 1 (NHERF) to assemble protein complexes of cystic fibrosis transmembrane conductance regulator (CFTR), a chloride ion channel that controls fluid and electrolyte transport across cell membranes. NHERF directs polarized expression of receptors and ion transport proteins in epithelial cells, as well as organizes the homo- and hetero-association of these cell surface proteins. NHERF contains two modular PDZ domains that are modular protein-protein interaction motifs, and a C-terminal domain. Previous studies have shown that NHERF is a phosphoprotein, but how phosphorylation affects NHERF to assemble macromolecular complexes is unknown. We show that PKC phosphorylates two amino acid residues Ser-339 and Ser-340 in the C-terminal domain of NHERF, but a serine 162 of PDZ2 is specifically protected from being phosphorylated by the intact C-terminal domain. PKC phosphorylation-mimicking mutant S339D/S340D of NHERF has increased affinity and stoichiometry when binding to C-CFTR. Moreover, solution small angle x-ray scattering indicates that the PDZ2 and C-terminal domains contact each other in NHERF, but such intramolecular domain-domain interactions are released in the PKC phosphorylation-mimicking mutant indicating that PKC phosphorylation disrupts the autoinhibition interactions in NHERF. The results demonstrate that the C-terminal domain of NHERF functions as an intramolecular switch that regulates the binding capability of PDZ2, and thus controls the stoichiometry of NHERF to assemble protein complexes.

The prediction of protein–protein interactions is a domain which uses the combination of computational and biological data. Understanding protein interactions is important for the representation of the different protein complex levels and helps to know the various aspects of biochemical activities. Algorithms, methods and applications with various techniques have been addressed to predict the protein–protein interactions. The computational methods are used for protein interaction prediction, due to the several demerits in the implementation methods. Physically interacting protein complexes play a vital role in managing the biological activities of the cell. Protein interaction is identified through the structural actions between the proteins that form complexes. The events of the ensuing sequence properly, common ancestor evolved and the interaction conserved by the proteins and genes in the living cells. This ‘This paper discusses some methods … to identify or predict protein interactions’.

Single Molecule Immunoprecipitation

Western blotting is a powerful and widely used method, but limitations in detection sensitivity and specificity, and dependence upon high quality antibodies to detect targeted proteins, are hurdles to overcome. The in situ proximity ligation assay, based on dual antibody recognition and powerful localized signal amplification, offers increased detection sensitivity and specificity, along with an ability to identify complex targets such as phosphorylated or interacting proteins. Here we have applied the in situ proximity ligation assay mechanism in Western blotting. This combination allowed the use of isothermal rolling circle amplification of DNA molecules formed in target-specific ligation reaction, for 16-fold or greater increase in detection sensitivity. The increased specificity because of dual antibody recognition ensured highly selective assays, detecting the specific band when combinations of two cross-reactive antitubulin antibodies were used (i.e. both producing distinct nonspecific bands in traditional Western blotting). We also demonstrated detection of phosphorylated platelet-derived growth factor receptor β by proximity ligation with one antibody directed against the receptor and another directed against the phosphorylated tyrosine residue. This avoided the need for stripping and re-probing the membrane or aligning two separate traditional blots. We demonstrate that the high-performance in situ proximity ligation-based Western blotting described herein is compatible with detection via enhanced chemiluminescence and fluorescence detection systems, and can thus be readily employed in any laboratory.

The biological interpretation of the genome starts from transcription, and many different signaling pathways are integrated at this level. Transcription factors play a central role in the transcription process, because they select the down-stream genes and determine their spatial and temporal expression. In higher eudicot species around 2000 specific transcription factors are present, which can be classified into families based on conserved common domains. The MADS-box transcription factor family is an important family of transcription regulators in plants and genetic studies revealed that members of this family are involved in various developmental processes, like floral induction, floral organ formation and fruit development. In contrast to this wealth of information concerning MADS-box gene functions, the molecular mode of action of the encoded proteins is far from completely understood. Biochemical and yeast η-hybrid experiments performed in the past showed that MADS-box proteins are able to interact mutually, and based on these findings a hypothetical quaternary model has been proposed as molecular working mechanism. According to this model two MADS-box protein dimers assemble into a higher order complex, which binds DNA and regulates target gene expression. Although, this molecular mechanism sounds plausible, it still lacks evidence from in vivo studies. In this study we investigated physical interactions among members of the Petunia hybrida and Arabidopsis thaliana MADS-box transcription factor families in living plant cells. For this purpose, sophisticated micro-spectroscopy techniques have been implemented and in addition, some novel fluorescent-protein-based tools were developed. The first chapter gives an introduction about the dynamic transcriptional process and describes our current knowledge about transcriptional regulation in eukaryotes. The central question of this chapter is how transcription factors are able to find their specific binding sites (ci's-elements) within the huge genome. The various mechanisms, such as "looping" and "sliding", that have been proposed are discussed, as well as the relevance of direct interactions between transcription factors for the control of gene expression. In a first attempt to detect protein interactions in living cells, we transiently expressed combinations of petunia MADS-box transcription factors labeled with different color variants of the Green Fluorescent Protein (GFP) in leaf protoplasts (Chapter 2). Subsequently, the transfected protoplasts were analyzed by means of FRET-FLIM (Fluorescence Resonance Energy Transfer - Fluorescence Lifetime Imaging) to identify specific dimerization. In addition, we have obtained indirect evidence for higher-order complex formation of the petunia MADS-box proteins FLORAL BINDING PROTEIN2 (FBP2), FBP11, and FBP24 in living cells. Similar kind of analyses for Arabidopsis MADS-box proteins involved in petal and stamen development revealed clear differences in interaction affinities in vivo and furthermore, many homodimers were identified that could not be detected by yeast-based systems in the past (Chapter 3). This result demonstrated the robustness of the FRET-FLIM approach. Based on our observations, we hypothesize that 'partner selectivity' plays an important role in complex formation at particular developmental stages. To study differences in interaction affinity and selectivity and the consequences for complex formation in more detail, a novel method was developed (Chapter 4). The technique, designated "Competition-FRET", allows the verification of competition effects between proteins, and furthermore, it may provide information about the formation of higher-order complexes between different proteins under study. The developed method was implemented to investigate in depth the preference for homo- or heterodimer interactions of the Arabidopsis MADS-box proteins AGAMOUS (AG) and SEPALLATA3 (SEP3). The detection of interactions in living cells by FRET as it has been done in the studies described above demands a sophisticated microscopy set-up, and therefore, we decided to test and implement an alternative and theoretically simple technique (Chapter 5). This method for the in vivo detection of protein-protein interaction is called BiFC (Bimolecular Fluorescence Complementation), or "Split-YFP". In this system, a fluorescent molecule is split into two inactive domains and these two non-fluorescent parts are fused to the proteins under study. Only upon interaction of the two protein partners the two non-fluorescent parts of the fluorescent molecule are brought into close proximity, which enables the recovery of fluorescence. We used the EYFP (Enhanced Yellow Fluorescence Protein) molecule as fluorescent molecule and were able to detect the interaction between AG and SEP3 in nuclei of Arabidopsis leaf protoplasts. Techniques like this and FRET-FLIM allow the analyses of interactions between proteins in living cells, but give no information about the size of the formed complexes. To get a first indication about the stoichiometry of protein complexes, we monitored the diffusion time of in vitro synthesized AG-EYFP and SEP3-EYFP fusion proteins by means of FCS (Fluorescence Correlation Spectroscopy). From these experiments described in Chapter 6, we could speculate that SEP3 is present as a dimer and also as a higher order complex, whilst AG on its own is able to assemble into larger complexes. The diffusion time of the product formed upon co-translation of both AG and SEP3, suggests that a multimenc protein complex with a high molecular weight is formed upon interaction between AG and SEP3. Even though FCS is a powerful technique, these interpretations should be taken cautiously, mainly because these experiments were done in vitro instead of in living cells. Finally, in the last chapter we discuss the various methods that have been implemented and developed to monitor protein-protein interactions and complex formation of MADS-box transcription factors in living plant cells. Furthermore, we made a first step to monitor interactions in intact tissues under endogenous expression levels, and the preliminary results obtained from these in planta FRET-FLIM measurements are discussed.

Quantification of lipid peroxidation in tissue extracts based on Fe(III)xylenol orange complex formation