scholarly journals Split-miniSOG for detecting and localizing intracellular protein-protein interactions: application to correlated light and electron microscopy

2018 ◽  
Author(s):  
Daniela Boassa ◽  
Sakina P. Lemieux ◽  
Varda Lev-Ram ◽  
Junru Hu ◽  
Qing Xiong ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Light And Electron Microscopy ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Intracellular Protein ◽  
Fluorescence Light Microscopy ◽  
Fluorescence Light ◽  
Protein Complementation

AbstractA protein complementation assay (PCA) for detecting and localizing intracellular protein-protein interactions (PPIs) was built by bisection of miniSOG, a fluorescent flavoprotein derived from the light, oxygen, voltage (LOV)-2 domain of Arabidopsis phototropin. When brought together by interacting proteins, the fragments reconstitute a functional reporter that permits tagged protein complexes to be visualized by fluorescence light microscopy (LM), and then by standard as well as “multicolor” electron microscopy (EM) imaging methods via the photooxidation of 3-3’-diaminobenzidine (DAB) and its lanthanide-conjugated derivatives.

scholarly journals Split-miniSOG for Spatially Detecting Intracellular Protein-Protein Interactions by Correlated Light and Electron Microscopy

2019 ◽  
Vol 26 (10) ◽  
pp. 1407-1416.e5 ◽  
Author(s):  
Daniela Boassa ◽  
Sakina P. Lemieux ◽  
Varda Lev-Ram ◽  
Junru Hu ◽  
Qing Xiong ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Protein Interactions ◽  
Light And Electron Microscopy ◽  
Protein Protein Interactions ◽  
Intracellular Protein

Can infrared spectroscopy provide information on protein–protein interactions?

2010 ◽  
Vol 38 (4) ◽  
pp. 940-946 ◽  
Author(s):  
Parvez I. Haris
Keyword(s):  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Physical Phenomenon ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Large Shift ◽  
Amide Bonds

For most biophysical techniques, characterization of protein–protein interactions is challenging; this is especially true with methods that rely on a physical phenomenon that is common to both of the interacting proteins. Thus, for example, in IR spectroscopy, the carbonyl vibration (1600–1700 cm−1) associated with the amide bonds from both of the interacting proteins will overlap extensively, making the interpretation of spectral changes very complicated. Isotope-edited infrared spectroscopy, where one of the interacting proteins is uniformly labelled with 13C or 13C,15N has been introduced as a solution to this problem, enabling the study of protein–protein interactions using IR spectroscopy. The large shift of the amide I band (approx. 45 cm−1 towards lower frequency) upon 13C labelling of one of the proteins reveals the amide I band of the unlabelled protein, enabling it to be used as a probe for monitoring conformational changes. With site-specific isotopic labelling, structural resolution at the level of individual amino acid residues can be achieved. Furthermore, the ability to record IR spectra of proteins in diverse environments means that isotope-edited IR spectroscopy can be used to structurally characterize difficult systems such as protein–protein complexes bound to membranes or large insoluble peptide/protein aggregates. In the present article, examples of application of isotope-edited IR spectroscopy for studying protein–protein interactions are provided.

scholarly journals Improved prediction of protein-protein interactions using AlphaFold2

2021 ◽  
Author(s):  
Patrick Bryant ◽  
Gabriele Pozzati ◽  
Arne Elofsson
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Interacting Proteins ◽  
Bacterial Protein ◽  
Protein Protein Interactions ◽  
Multiple Sequence ◽  
Test Set ◽  
Interacting Protein ◽  
Identification Rate ◽  
Homologous Sequences

Abstract Predicting the structure of interacting protein chains is fundamental for understanding the function of proteins. Here, we examine the use of AlphaFold2 (AF2) for predicting the structure of heterodimeric protein complexes. We find that using the default AF2 protocol, 44% of the models in a test set can be predicted accurately. However, by optimising the multiple sequence alignment, we can increase the accuracy to 59%. In comparison, the alternative fold-and-dock method RoseTTAFold is only successful in 10% of the cases on this set, template-based docking 35% and traditional docking methods 22%. We can distinguish acceptable (DockQ>0.23) from incorrect models with an AUC of 0.85 on the test set by analysing the predicted interfaces. The success is higher for bacterial protein pairs, pairs with large interaction areas consisting of helices or sheets, and many homologous sequences. Further, we test the possibility to distinguish interacting from non-interacting proteins and find that by analysing the predicted interfaces, we can separate truly interacting from non-interacting proteins with an AUC of 0.82 in the ROC curve, compared to 0.76 with a recently published method. In addition, when using a more realistic negative set, including mammalian proteins, the identification rate remains (AUC=0.83), resulting in that 27% of interactions can be identified at a 1% FPR. All scripts and tools to run our protocol are freely available at: https://gitlab.com/ElofssonLab/FoldDock.

scholarly journals CorRelator: An interactive and flexible toolkit for high-precision cryo-correlative light and electron microscopy

2020 ◽  
Author(s):  
Jie E. Yang ◽  
Matthew R. Larson ◽  
Bryan S. Sibert ◽  
Samantha Shrum ◽  
Elizabeth R. Wright
Keyword(s):  
Electron Microscopy ◽  
Light And Electron Microscopy ◽  
Cryo Electron Microscopy ◽  
Cellular Environment ◽  
Transmission Electron ◽  
Desktop Application ◽  
Fluorescence Light Microscopy ◽  
Cross Platform ◽  
Fluorescence Light ◽  
Correlation Process

AbstractCryo-correlative light and electron microscopy (CLEM) is a technique that uses the spatiotemporal cues from fluorescence light microscopy (FLM) to investigate the high-resolution ultrastructure of biological samples by cryo-electron microscopy (cryo-EM). Cryo-CLEM provides advantages for identifying and distinguishing fluorescently labeled proteins, macromolecular complexes, and organelles from the cellular environment. Challenges remain on how correlation workflows and software tools are implemented on different microscope platforms to support microscopy-driven structural studies. Here, we present an open-source desktop application tool, CorRelator, to bridge between cryo-FLM and cryo-EM/ET data collection instruments. CorRelator was designed to be flexible for both on-the-fly and post-acquisition correlation schemes. The CorRelator workflow is easily adapted to any fluorescence and transmission electron microscope (TEM) system configuration. CorRelator was benchmarked under cryogenic and ambient temperature conditions using several FLM and TEM instruments, demonstrating that CorRelator is a rapid and efficient application for image and position registration in CLEM studies. CorRelator is a cross-platform software featuring an intuitive Graphical User Interface (GUI) that guides the user through the correlation process. CorRelator source code is available at: https://github.com/wright-cemrc-projects/corr.

Immuno-Electron Microscopy of Primary Cell Cultures from Genetically Modified Animals in Liquid by Atmospheric Scanning Electron Microscopy

2014 ◽  
Vol 20 (2) ◽  
pp. 469-483 ◽  
Author(s):  
Takaaki Kinoshita ◽  
Yosio Mori ◽  
Kazumi Hirano ◽  
Shinya Sugimoto ◽  
Ken-ichi Okuda ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Protein Interactions ◽  
Light And Electron Microscopy ◽  
Atmospheric Environment ◽  
Primitive Endoderm ◽  
Protein Protein Interactions ◽  
Immuno Electron Microscopy ◽  
Segmentation Boundary ◽  
Scanning Electron

AbstractHigh-throughput immuno-electron microscopy is required to capture the protein–protein interactions realizing physiological functions. Atmospheric scanning electron microscopy (ASEM) allowsin situcorrelative light and electron microscopy of samples in liquid in an open atmospheric environment. Cells are cultured in a few milliliters of medium directly in the ASEM dish, which can be coated and transferred to an incubator as required. Here, cells were imaged by optical or fluorescence microscopy, and at high resolution by gold-labeled immuno-ASEM, sometimes with additional metal staining. Axonal partitioning of neurons was correlated with specific cytoskeletal structures, including microtubules, using primary-culture neurons from wild typeDrosophila, and the involvement of ankyrin in the formation of the intra-axonal segmentation boundary was studied using neurons from anankyrin-deficient mutant. Rubella virus replication producing anti-double-stranded RNA was captured at the host cell’s plasma membrane. Fas receptosome formation was associated with clathrin internalization near the surface of primitive endoderm cells. Positively charged Nanogold clearly revealed the cell outlines of primitive endoderm cells, and the cell division of lactic acid bacteria. Based on these experiments, ASEM promises to allow the study of protein interactions in various complexes in a natural environment of aqueous liquid in the near future.

scholarly journals Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Yang ◽  
Xianglong Tang
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Complexes ◽  
False Negative ◽  
Biological Database ◽  
Interacting Proteins ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Meaning

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

Advances in Correlative Single-Molecule Localization Microscopy and Electron Microscopy

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Ulrike Endesfelder
Keyword(s):  
Electron Microscopy ◽  
Light Microscopy ◽  
Single Molecule ◽  
Light And Electron Microscopy ◽  
Fluorescence Labeling ◽  
Localization Microscopy ◽  
Fluorescence Light Microscopy ◽  
Fluorescence Light ◽  
And Function ◽  
Single Molecule Localization Microscopy

AbstractDuring the last few decades, correlative fluorescence light and electron microscopy (FLM-EM) has gained increased interest in the life sciences concomitant with the advent of fluorescence light microscopy. It has become, accompanied by numerous developments in both techniques, an important tool to study bio-cellular structure and function as it combines the specificity of fluorescence labeling with the high structural resolution and cellular context information given by the EM images. Having the recently introduced single-molecule localization microscopy techniques (SMLM) at hand, FLM-EM can now make use of improved fluorescence light microscopy resolution, single-molecule sensitivity and quantification strategies. Here, currently used methods for correlative SMLM and EM including the special requirements in sample preparation and imaging routines are summarized and an outlook on remaining challenges concerning methods and instrumentation is provided.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Protein Interactions ◽  
Modular Architecture ◽  
Post Translational Modifications ◽  
Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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