Defining Protein Complexes Research Articles

SETS: A Seed-Dense-Expanding Model-Based Topological Structure for the Prediction of Overlapping Protein Complexes

Defining protein complexes by analysing the protein–protein interaction (PPI) networks is a crucial task in understanding the principles of a biological cell. In the last few decades, researchers have proposed numerous methods to explore the topological structure of a PPI network to detect dense protein complexes. In this paper, the overlapping protein complexes with different densities are predicted within an acceptable execution time using seed expanding model and topological structure of the PPI network (SETS). SETS depend on the relation between the seed and its neighbours. The algorithm was compared with six algorithms on six datasets: five for yeast and one for human. The results showed that SETS outperformed other algorithms in terms of F-measure, coverage rate and the number of complexes that have high similarity with real complexes.

Identifying protein complexes from protein-protein interaction networks based on the gene expression profile and core-attachment approach.

Defining protein complexes in the cell is important for learning about cellular processes mechanisms as they perform many of the molecular functions in these processes. Most of the proposed algorithms predict a complex as a dense area in a Protein-Protein Interaction (PPI) network. Others, on the other hand, weight the network using gene expression or geneontology (GO). These approaches, however, eliminate the proteins and their edges that offer no gene expression data. This can lead to the loss of important topological relations. Therefore, in this study, a method based on the Gene Expression and Core-Attachment (GECA) approach was proposed for addressing these limitations. GECA is a new technique to identify core proteins using common neighbor techniques and biological information. Moreover, GECA improves the attachment technique by adding the proteins that have low closeness but high similarity to the gene expression of the core proteins. GECA has been compared with several existing methods and proved in most datasets to be able to achieve the highest F-measure. The evaluation of complexes predicted by GECA shows high biological significance.

Adjustable Bioorthogonal Conjugation Platform for Protein Studies in Live Cells Based on Artificial Compartments.

The investigation of complex biological processes in vivo often requires defined multiple bioconjugation and positioning of functional entities on 3D structures. Prominent examples include spatially defined protein complexes in nature, facilitating efficient biocatalysis of multistep reactions. Mimicking natural strategies, synthetic scaffolds should comprise bioorthogonal conjugation reactions and allow for absolute stoichiometric quantification as well as facile scalability through scaffold reproduction. Existing in vivo scaffolding strategies often lack covalent conjugations on geometrically confined scaffolds or precise quantitative characterization. Addressing these shortcomings, we present a bioorthogonal dual conjugation platform based on genetically encoded artificial compartments in vivo, comprising two distinct genetically encoded covalent conjugation reactions and their precise stoichiometric quantification. The SpyTag/SpyCatcher (ST/SC) bioconjugation and the controllable strain-promoted azide-alkyne cycloaddition (SPAAC) were implemented on self-assembled protein membrane-based compartments (PMBCs). The SPAAC reaction yield was quantified to be 23% ± 3% and a ST/SC surface conjugation yield of 82% ± 9% was observed, while verifying the compatibility of both chemical reactions as well as enhanced proteolytic stability. Using tandem mass spectrometry, absolute concentrations of the proteinaceous reactants were calculated to be 0.11 ± 0.05 attomol/cell for PMBC surface-tethered mCherry-ST-His and 0.22 ± 0.09 attomol/cell for PMBC-constituting pAzF-SC-E20F20-His. The established in vivo conjugation platform enables quantifiable protein-protein interaction studies on geometrically defined scaffolds and paves the road to investigate effects of scaffold-tethering on enzyme activity.

Protein complex prediction via dense subgraphs and false positive analysis.

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes

Understanding the function of the thousands of cellular proteins is a central question in molecular cell biology. As proteins are typically part of multiple dynamic and often overlapping macromolecular complexes exerting distinct functions, the identification of protein–protein interactions (PPI) and their assignment to specific complexes is a crucial but challenging task. We present a protein fragments complementation assay integrated with the proximity-dependent biotinylation technique BioID. Activated on the interaction of two proteins, split-BioID is a conditional proteomics approach that allows in a single and simple assay to both experimentally validate binary PPI and to unbiasedly identify additional interacting factors. Applying our method to the miRNA-mediated silencing pathway, we can probe the proteomes of two distinct functional complexes containing the Ago2 protein and uncover the protein GIGYF2 as a regulator of miRNA-mediated translation repression. Hence, we provide a novel tool to study dynamic spatiotemporally defined protein complexes in their native cellular environment.

Strategies and Methods of Transcription-Coupled Repair Studies In Vitro and In Vivo.

Transcription-coupled repair (TCR) serves an important role in preserving genome integrity and maintaining fidelity of replication. Coupling transcription to DNA repair requires a coordinated action of several factors, including transcribing RNA polymerase and various transcription modulators and repair proteins. To study TCR in molecular detail, it is important to employ defined protein complexes in vitro and defined genetic backgrounds in vivo. In this chapter, we present methods to interrogate various aspects of TCR at different stages of repair. We describe promoter-initiated and nucleic acid scaffold-initiated transcription as valid approaches to recapitulate various stages of TCR, and discuss their strengths and weaknesses. We also outline an approach to study TCR in its cellular context using Escherichia coli as a model system.

Defining Protein Complexes that Mediate Bacterial Chemotaxis by Pulsed Dipolar ESR Spectroscopy

Bacterial chemotaxis is the process whereby cells modulate their flagella-driven motility in response to environmental cues. This widespread behavior relies upon a complex sensory apparatus composed of transmembrane receptors, histidine kinases and coupling proteins to achieve great sensitivity, gain and dynamic range in signal processing. We have applied pulsed-dipolar ESR spectroscopy (PDS) combined with site-directed spin labeling to probe the structures and mechanisms of proteins that compose these regulatory circuits. Inherent symmetries within the protein complexes require interpretation of signals from systems that contain more than two spins. Tikhonov regularization and maximum entropy refinement when combined with model simulation reproduce accurate inter-spin distance distributions from such multiply labeled species. Disulfide crosslinking and disruptive mutagenesis verify component interfaces predicted by PDS. Weak but specific interactions that produce low population aggregates relevant to complex assembly are detected by an approach that relies on magnetic dilution and baseline analysis of dipolar spectra. The resulting PDS-based models capture key architectural features of the receptor kinase arrays and the flagellar motor. Moreover, distance distributions derived from the multi- spin sites reveal changes in conformation and dynamics that accompany kinase activation and motor switching. Application to the chemotaxis system demonstrates how PDS effectively reports on key structural features of transient protein complexes and in doing so fills the resolution gap between other well-established biophysical techniques such as electron microscopy and x-ray crystallography.

A Consensus of Core Protein Complex Compositions for Saccharomyces cerevisiae

Analyses of biological processes would benefit from accurate definitions of protein complexes. High-throughput mass spectrometry data offer the possibility of systematically defining protein complexes; however, the predicted compositions vary substantially depending on the algorithm applied. We determine consensus compositions for 409 core protein complexes from Saccharomyces cerevisiae by merging previous predictions with a new approach. Various analyses indicate that the consensus is comprehensive and of high quality. For 85 out of 259 complexes not recorded in GO, literature search revealed strong support in the form of coprecipitation. New complexes were verified by an independent interaction assay and by gene expression profiling of strains with deleted subunits, often revealing which cellular processes are affected. The consensus complexes are available in various formats, including a merge with GO, resulting in 518 protein complex compositions. The utility is further demonstrated by comparison with binary interaction data to reveal interactions between core complexes.

The Association of the Arabidopsis Actin-Related Protein2/3 Complex with Cell Membranes Is Linked to Its Assembly Status But Not Its Activation

In growing plant cells, the combined activities of the cytoskeleton, endomembrane, and cell wall biosynthetic systems organize the cytoplasm and define the architecture and growth properties of the cell. These biosynthetic machineries efficiently synthesize, deliver, and recycle the raw materials that support cell expansion. The precise roles of the actin cytoskeleton in these processes are unclear. Certainly, bundles of actin filaments position organelles and are a substrate for long-distance intracellular transport, but the functional linkages between dynamic actin filament arrays and the cell growth machinery are poorly understood. The Arabidopsis (Arabidopsis thaliana) "distorted group" mutants have defined protein complexes that appear to generate and convert small GTPase signals into an Actin-Related Protein2/3 (ARP2/3)-dependent actin filament nucleation response. However, direct biochemical knowledge about Arabidopsis ARP2/3 and its cellular distribution is lacking. In this paper, we provide biochemical evidence for a plant ARP2/3. The plant complex utilizes a conserved assembly mechanism. ARPC4 is the most critical core subunit that controls the assembly and steady-state levels of the complex. ARP2/3 in other systems is believed to be mostly a soluble complex that is locally recruited and activated. Unexpectedly, we find that Arabidopsis ARP2/3 interacts strongly with cell membranes. Membrane binding is linked to complex assembly status and not to the extent to which it is activated. Mutant analyses implicate ARP2 as an important subunit for membrane association.

Functional Linkages Can Reveal Protein Complexes for Structure Determination

In the study of protein complexes, is there a computational method for inferring which combinations of proteins in an organism are likely to form a crystallizable complex? Here we attempt to answer this question, using the Protein Data Bank (PDB) to assess the usefulness of inferred functional protein linkages from the Prolinks database. We find that of the 242 nonredundant prokaryotic protein complexes shared between the current PDB and Prolinks, 44% (107/242) contain proteins linked at high confidence by one or more methods of computed functional linkages. Similarly, high-confidence linkages detect 47% of known Escherichia coli protein complexes, with 45% accuracy. Together these findings suggest that functional linkages will be useful in defining protein complexes for structural studies, including for structural genomics. We offer a database of inferred linkages corresponding to likely protein complexes for some 629,952 pairs of proteins in 154 prokaryotes and archaea.

A Tandem Affinity Purification-based Technology Platform to Study the Cell Cycle Interactome in Arabidopsis thaliana

Defining protein complexes is critical to virtually all aspects of cell biology because many cellular processes are regulated by stable protein complexes, and their identification often provides insights into their function. We describe the development and application of a high throughput tandem affinity purification/mass spectrometry platform for cell suspension cultures to analyze cell cycle-related protein complexes in Arabidopsis thaliana. Elucidation of this protein-protein interaction network is essential to fully understand the functional differences between the highly redundant cyclin-dependent kinase/cyclin modules, which are generally accepted to play a central role in cell cycle control, in all eukaryotes. Cell suspension cultures were chosen because they provide an unlimited supply of protein extracts of actively dividing and undifferentiated cells, which is crucial for a systematic study of the cell cycle interactome in the absence of plant development. Here we report the mapping of a protein interaction network around six known core cell cycle proteins by an integrated approach comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, tandem affinity purification adapted for plant cells, matrix-assisted laser desorption ionization tandem mass spectrometry, data analysis, and functional assays. We identified 28 new molecular associations and confirmed 14 previously described interactions. This systemic approach provides new insights into the basic cell cycle control mechanisms and is generally applicable to other pathways in plants.

Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

I-DIRT, A General Method for Distinguishing between Specific and Nonspecific Protein Interactions

Isolation of protein complexes via affinity-tagged proteins provides a powerful tool for studying biological systems, but the technique is often compromised by co-enrichment of nonspecifically interacting proteins. We describe a new technique (I-DIRT) that distinguishes contaminants from bona fide interactors in immunopurifications, overcoming this most challenging problem in defining protein complexes. I-DIRT will be of broad value for studying protein complexes in biological systems that can be metabolically labeled.

Identification of Human VPS37C, a Component of Endosomal Sorting Complex Required for Transport-I Important for Viral Budding

Endosomal sorting complex required for transport-I (ESCRT-I) is one of three defined protein complexes in the class E vacuolar protein sorting (VPS) pathway required for the sorting of ubiquitinated transmembrane proteins into internal vesicles of multivesicular bodies. In yeast, ESCRT-I is composed of three proteins, VSP23, VPS28, and VPS37, whereas in mammals only Tsg101(VPS23) and VPS28 were originally identified as ESCRT-I components. Using yeast two-hybrid screens, we identified one of a family of human proteins (VPS37C) as a Tsg101-binding protein. VPS37C can form a ternary complex with Tsg101 and VPS28 by binding to a domain situated toward the carboxyl terminus of Tsg101 and binds to another class E VPS factor, namely Hrs. In addition, VPS37C is recruited to aberrant endosomes induced by overexpression of Tsg101, Hrs, or dominant negative form of the class E VPS ATPase, VPS4. Enveloped viruses that encode PTAP motifs to facilitate budding exploit ESCRT-I as an interface with the class E VPS pathway, and accordingly, VPS37C is recruited to the plasma membrane along with Tsg101 by human immunodeficiency virus, type 1 (HIV-1) Gag. Moreover, direct fusion of VPS37C to HIV-1 Gag obviates the requirement for a PTAP motif to induce virion release. Depletion of VPS37C from cells does not inhibit murine leukemia virus budding, which is not mediated by ESCRT-I, however, if murine leukemia virus budding is engineered to be ESCRT-I-dependent, then it is inhibited by VPS37C depletion, and this inhibition is accentuated if VPS37B is simultaneously depleted. Thus, this study identifies VPS37C as a functional component of mammalian ESCRT-I.

Two-dimensional Blue Native/SDS Gel Electrophoresis of Multi-Protein Complexes from Whole Cellular Lysates: A PROTEOMICS APPROACH

Identification and characterization of multi-protein complexes is an important step toward an integrative view of protein-protein interaction networks that determine protein function and cell behavior. The limiting factor for identifying protein complexes is the method for their separation. Blue native PAGE (BN-PAGE) permits a high-resolution separation of multi-protein complexes under native conditions. To date, BN-PAGE has only been applicable to purified material. Here, we show that dialysis permits the analysis of multi-protein complexes of whole cellular lysates by BN-PAGE. We visualized different multi-protein complexes by immunoblotting including forms of the eukaryotic proteasome. Complex dynamics after gamma interferon stimulation of cells was studied, and an antibody shift assay was used to detect protein-protein interactions in BN-PAGE. Furthermore, we identified defined protein complexes of various proteins including the tumor suppressor p53 and c-Myc. Finally, we identified multi-protein complexes via mass spectrometry, showing that the method has a wide potential for functional proteomics.

Monomeric and dimeric byproducts are the principal functional elements of higher order P2X1 concatamers.

Heteromultimeric assembly of ion channel subunits generates high diversity in ion channel subtypes with distinct pharmacological and functional properties. To determine the subunit stoichiometry and order of ion channels, constructs with several concatenated subunits have been widely used in electrophysiological studies. Here we used primarily biochemical techniques to analyze the synthesis, assembly, and surface expression of P2X1 concatamers. We found that full-length concatamers consisting of two to six contiguous copies of the P2X1 subunit, although readily synthesized in Xenopus laevis oocytes, were entirely retained as aggregates in the endoplasmic reticulum. In contrast, minute levels of lower order byproducts, such as monomers and dimers, that were inherently formed with all the concatamers combined to form defined protein complexes equal in mass to the homotrimeric P2X1 receptor assembled from P2X1 monomers. Besides these complexes consisting of three monomers or one monomer plus one concatenated dimer, only small amounts of concatenated P2X1 trimers reached the plasma membrane. Complexes comprising more than three subunits were not observed in the plasma membrane. The byproduct complexes can account fully for the ATP-gated currents arising from expression of concatenated P2X1 subunits. These results strongly corroborate a trimeric architecture for P2X receptors yet indicate that formation of lower order by-products can be a pitfall of the concatamer approach.

Use of gel retardation to analyze protein-nucleic acid interactions.

Protein-nucleic acid interactions are crucial in the regulation of many fundamental cellular processes. The nature of these interactions is susceptible to analysis by a variety of methods, but the combination of high analytical power and technical simplicity offered by the gel retardation (band shift) technique has made this perhaps the most widely used such method over the last decade. This procedure is based on the observation that the formation of protein-nucleic complexes generally reduces the electrophoretic mobility of the nucleic acid component in the gel matrix. This review attempts to give a simplified account of the physical basis of the behavior of protein-nucleic acid complexes in gels and an overview of many of the applications in which the technique has proved especially useful. The factors which contribute most to the resolution of the complex from the naked nucleic acid are the gel pore size, the relative mass of protein compared with nucleic acid, and changes in nucleic acid conformation (bending) induced by binding. The consequences of induced bending on the mobility of double-strand DNA fragments are similar to those arising from sequence-directed bends, and the latter can be used to help characterize the angle and direction of protein-induced bends. Whether a complex formed in solution is actually detected as a retarded band on a gel depends not only on resolution but also on complex stability within the gel. This is strongly influenced by the composition and, particularly, the ionic strength of the gel buffer. We discuss the applications of the technique to analyzing complex formation and stability, including characterizing cooperative binding, defining binding sites on nucleic acids, analyzing DNA conformation in complexes, assessing binding to supercoiled DNA, defining protein complexes by using cell extracts, and analyzing biological processes such as transcription and splicing.

Use of gel retardation to analyze protein-nucleic acid interactions.

Protein-nucleic acid interactions are crucial in the regulation of many fundamental cellular processes. The nature of these interactions is susceptible to analysis by a variety of methods, but the combination of high analytical power and technical simplicity offered by the gel retardation (band shift) technique has made this perhaps the most widely used such method over the last decade. This procedure is based on the observation that the formation of protein-nucleic complexes generally reduces the electrophoretic mobility of the nucleic acid component in the gel matrix. This review attempts to give a simplified account of the physical basis of the behavior of protein-nucleic acid complexes in gels and an overview of many of the applications in which the technique has proved especially useful. The factors which contribute most to the resolution of the complex from the naked nucleic acid are the gel pore size, the relative mass of protein compared with nucleic acid, and changes in nucleic acid conformation (bending) induced by binding. The consequences of induced bending on the mobility of double-strand DNA fragments are similar to those arising from sequence-directed bends, and the latter can be used to help characterize the angle and direction of protein-induced bends. Whether a complex formed in solution is actually detected as a retarded band on a gel depends not only on resolution but also on complex stability within the gel. This is strongly influenced by the composition and, particularly, the ionic strength of the gel buffer. We discuss the applications of the technique to analyzing complex formation and stability, including characterizing cooperative binding, defining binding sites on nucleic acids, analyzing DNA conformation in complexes, assessing binding to supercoiled DNA, defining protein complexes by using cell extracts, and analyzing biological processes such as transcription and splicing.