Proteomic analysis of native metabotropic glutamate receptor 5 protein complexes reveals novel molecular constituents

We used a proteomic approach to identify novel proteins that may regulate metabotropic glutamate receptor 5 (mGluR5) responses by direct or indirect protein interactions. This approach does not rely on the heterologous expression of proteins and offers the advantage of identifying protein interactions in a native environment. The mGluR5 protein was immunoprecipitated from rat brain lysates; co-immunoprecipitating proteins were analyzed by mass spectrometry and identified peptides were matched to protein databases to determine the correlating parent proteins. This proteomic approach revealed the interaction of mGluR5 with known regulatory proteins, as well as novel proteins that reflect previously unidentified molecular constituents of the mGluR5-signaling complex. Immunoblot analysis confirmed the interaction of high confidence proteins, such as phosphofurin acidic cluster sorting protein 1, microtubule-associated protein 2a and dynamin 1, as mGluR5-interacting proteins. These studies show that a proteomic approach can be used to identify candidate interacting proteins. This approach may be particularly useful for neurobiology applications where distinct protein interactions within a signaling complex can dramatically alter the outcome of the response to neurotransmitter release, or the disruption of normal protein interactions can lead to severe neurological and psychiatric disorders.     

Differential binding studies applying functional protein microarrays and surface plasmon resonance

A variety of different in vivo and in vitro technologies provide comprehensive insights in protein-protein interaction networks. Here we demonstrate a novel approach to analyze, verify and quantify putative interactions between two members of the S100 protein family and 80 recombinant proteins derived from a proteome-wide protein expression library. Surface plasmon resonance (SPR) using Biacore technology and functional protein microarrays were used as two independent methods to study protein-protein interactions. With this combined approach we were able to detect nine calcium-dependent interactions between Arg-Gly-Ser-(RGS)-His6 tagged proteins derived from the library and GST-tagged S100B and S100A6, respectively. For the protein microarray affinity-purified proteins from the expression library were spotted onto modified glass slides and probed with the S100 proteins. SPR experiments were performed in the same setup and in a vice-versa approach reversing analytes and ligands to determine distinct association and dissociation patterns of each positive interaction. Besides already known interaction partners, several novel binders were found independently with both detection methods, albeit analogous immobilization strategies had to be applied in both assays.     

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest‐ranked proteins, beta‐arrestin 1, and beta‐arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost‐effective means to identify additional proteins that remain elusive for current 2D gel‐based proteomic profiling techniques.     

A novel approach for the identification of protein–protein interaction with integral membrane proteins

Protein–protein interaction plays a major role in all biological processes. The currently available genetic methods such as the two-hybrid system and the protein recruitment system are relatively limited in their ability to identify interactions with integral membrane proteins. Here we describe the development of a reverse Ras recruitment system (reverse RRS), in which the bait used encodes a membrane protein. The bait is expressed in its natural environment, the membrane, whereas the protein partner (the prey) is fused to a cytoplasmic Ras mutant. Protein–protein interaction between the proteins encoded by the prey and the bait results in Ras membrane translocation and activation of a viability pathway in yeast. We devised the expression of the bait and prey proteins under the control of dual distinct inducible promoters, thus enabling a rapid selection of transformants in which growth is attributed solely to specific protein–protein interaction. The reverse RRS approach greatly extends the usefulness of the protein recruitment systems and the use of integral membrane proteins as baits. The system serves as an attractive approach to explore novel protein–protein interactions with high specificity and selectivity, where other methods fail.     

A novel approach for the identification of protein-protein interaction with integral membrane proteins

Protein-protein interaction plays a major role in all biological processes. The currently available genetic methods such as the two-hybrid system and the protein recruitment system are relatively limited in their ability to identify interactions with integral membrane proteins. Here we describe the development of a reverse Ras recruitment system (reverse RRS), in which the bait used encodes a membrane protein. The bait is expressed in its natural environment, the membrane, whereas the protein partner (the prey) is fused to a cytoplasmic Ras mutant. Protein-protein interaction between the proteins encoded by the prey and the bait results in Ras membrane translocation and activation of a viability pathway in yeast. We devised the expression of the bait and prey proteins under the control of dual distinct inducible promoters, thus enabling a rapid selection of transformants in which growth is attributed solely to specific protein-protein interaction. The reverse RRS approach greatly extends the usefulness of the protein recruitment systems and the use of integral membrane proteins as baits. The system serves as an attractive approach to explore novel protein-protein interactions with high specificity and selectivity, where other methods fail.     

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.     

A Protein-Protein Interaction Map of the Trypanosoma brucei Paraflagellar Rod

We have conducted a protein interaction study of components within a specific sub-compartment of a eukaryotic flagellum. The trypanosome flagellum contains a para-crystalline extra-axonemal structure termed the paraflagellar rod (PFR) with around forty identified components. We have used a Gateway cloning approach coupled with yeast two-hybrid, RNAi and 2D DiGE to define a protein-protein interaction network taking place in this structure. We define two clusters of interactions; the first being characterised by two proteins with a shared domain which is not sufficient for maintaining the interaction. The other cohort is populated by eight proteins, a number of which possess a PFR domain and sub-populations of this network exhibit dependency relationships. Finally, we provide clues as to the structural organisation of the PFR at the molecular level. This multi-strand approach shows that protein interactome data can be generated for insoluble protein complexes.     

Combining affinity enrichment,cross‐linking with photo amino acids,and mass spectrometry for probing protein kinase D2 interactions

We present a novel approach that relies on the affinity capture of protein interaction partners from a complex mixture, followed by their covalent fixation via UV‐induced activation of incorporated diazirine photoreactive amino acids (photo‐methionine and photo‐leucine). The captured protein complexes are enzymatically digested and interacting proteins are identified and quantified by label‐free LC/MS analysis. Using HeLa cell lysates with photo‐methionine and photo‐leucine‐labeled proteins, we were able to capture and preserve protein interactions that are otherwise elusive in conventional pull‐down experiments. Our approach is exemplified for mapping the protein interaction network of protein kinase D2, but has the potential to be applied to any protein system. Data are available via ProteomeXchange with identifiers PXD005346 (photo amino acid incorporation) and PXD005349 (enrichment experiments).     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

An integrated probabilistic model for functional prediction of proteins.

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression networks, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, tandem affinity purification (TAP) protein complex data, and protein domain information. We study the recall and precision of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the recall from 57% to 87% when the precision is set at 57%-an increase of 30%.     

Structural comparison and classification of alpha‐helical transmembrane domains based on helix interaction patterns

Structural classification of membrane proteins is still in its infancy due to the relative paucity of available three‐dimensional structures compared with soluble proteins. However, recent technological advances in protein structure determination have led to a significant increase in experimentally known membrane protein folds, warranting exploration of the structural universe of membrane proteins. Here, a new and completely membrane protein specific structural classification system is introduced that classifies α‐helical membrane proteins according to common helix architectures. Each membrane protein is represented by a helix interaction graph depicting transmembrane helices with their pairwise interactions resulting from individual residue contacts. Subsequently, proteins are clustered according to similarities among these helix interaction graphs using a newly developed structural similarity score called HISS. As HISS scores explicitly disregard structural properties of loop regions, they are more suitable to capture conserved transmembrane helix bundle architectures than other structural similarity scores. Importantly, we are able to show that a classification approach based on helix interaction similarity closely resembles conventional structural classification databases such as SCOP and CATH implying that helix interactions are one of the major determinants of α‐helical membrane protein folds. Furthermore, the classification of all currently available membrane protein structures into 20 recurrent helix architectures and 15 singleton proteins demonstrates not only an impressive variability of membrane helix bundles but also the conservation of common helix interaction patterns among proteins with distinctly different sequences. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Prediction of Lipid-Binding Regions in Cytoplasmic and Extracellular Loops of Membrane Proteins as Exemplified by Protein Translocation Membrane Proteins

The presence of possible lipid-binding regions in the cytoplasmic or extracellular loops of membrane proteins with an emphasis on protein translocation membrane proteins was investigated in this study using bioinformatics. Recent developments in approaches recognizing lipid-binding regions in proteins were found to be promising. In this study a total bioinformatics approach specialized in identifying lipid-binding helical regions in proteins was explored. Two features of the protein translocation membrane proteins, the position of the transmembrane regions and the identification of additional lipid-binding regions, were analyzed. A number of well-studied protein translocation membrane protein structures were checked in order to demonstrate the predictive value of the bioinformatics approach. Furthermore, the results demonstrated that lipid-binding regions in the cytoplasmic and extracellular loops in protein translocation membrane proteins can be predicted, and it is proposed that the interaction of these regions with phospholipids is important for proper functioning during protein translocation.     

Connecting the dots in Huntington's disease with protein interaction networks

Analysis of protein-protein interaction networks is becoming important for inferring the function of uncharacterized proteins. A recent study using this approach has identified new proteins and interactions that might be involved in the pathogenesis of the neurodegenerative disorder Huntington's disease, including a GTPase-activating protein that co-localizes with protein aggregates in Huntington's disease patients.     

Understanding the interaction of proteins to ion exchange chromatographic supports: A surface energetics approach

Ion exchange chromatography is one of the most widely used chromatographic technique for the separation and purification of important biological molecules. Due to its wide applicability in separation processes, a targeted approach is required to suggest the effective binding conditions during ion exchange chromatography. A surface energetics approach was used to study the interaction of proteins to different types of ion exchange chromatographic beads. The basic parameters used in this approach are derived from the contact angle, streaming potential, and zeta potential values. The interaction of few model proteins to different anionic and cationic exchanger, with different backbone chemistry, that is, agarose and methacrylate, was performed. Generally, under binding conditions, it was observed that proteins having negative surface charges showed strong to lose interaction (20 kT for Hannilase to 0.5 kT for IgG) with different anionic exchangers (having different positive surface charges). On the contrary, anionic exchangers showed almost no interaction (0–0.1 kT) with the positively charged proteins. An inverse behavior was observed for the interaction of proteins to cationic exchangers. The outcome from these theoretical calculations can predict the binding behavior of different proteins under real ion exchange chromatographic conditions. This will ultimately propose a better bioprocess design for protein separation.     

Conserved network motifs allow protein-protein interaction prediction

MOTIVATION: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. RESULTS: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20 and 40% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. AVAILABILITY: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.     

A correlated motif approach for finding short linear motifs from protein interaction networks

Background  

An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.     

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.     

Identification of the ligands of protein interaction domains through a functional approach

The identification of protein-protein interaction networks has often given important information about the functions of specific proteins and on the cross-talk among metabolic and regulatory pathways. The availability of entire genome sequences has rendered feasible the systematic screening of collections of proteins, often of unknown function, aimed to find the cognate ligands. Once identified by genetic and/or biochemical approaches, the interaction between two proteins should be validated in the physiologic environment. Herein we describe an experimental strategy to screen collections of protein-protein interaction domains to find and validate candidate interactors. The approach is based on the assumption that the overexpression in cultured cells of protein-protein interaction domains, isolated from the context of the whole protein, could titrate the endogenous ligand and, in turn, exert a dominant negative effect. The identification of the ligand could provide us with a tool to check the relevance of the interaction because the contemporary overexpression of the isolated domain and of its ligand could rescue the dominant negative phenotype. We explored this approach by analyzing the possible dominant negative effects on the cell cycle progression of a collection of phosphotyrosine binding (PTB) domains of human proteins. Of 47 PTB domains, we found that the overexpression of 10 of them significantly interfered with the cell cycle progression of NIH3T3 cells. Four of them were used as baits to identify the cognate interactors. Among these proteins, CARM1, interacting with the PTB domain of RabGAP1, and EF1alpha, interacting with RGS12, were able to rescue the block of the cell cycle induced by the isolated PTB domain of the partner protein, thus confirming in vivo the relevance of the interaction. These results suggest that the described approach can be used for the systematic screening of the ligands of various protein-protein interaction domains also by using different biological assays.     

Interaction proteome analysis of Xanthomonas Hrp proteins

Because of the importance of the type III protein-secretion system in bacteria-plant interaction, its function in bacterial pathogenesis of plants has been intensively studied. To identify bacterial proteins interacting with Xanthomonas hrp gene products that are involved in pathogenicity, we performed the glutathione-bead binding analysis of Xanthomonas lysates containing GST-tagged Hrp proteins. Analysis of glutathione-bead bound proteins by 1-DE and MALDI-TOF has demonstrated that Avr proteins, RecA, and several components of the type III secretion system interact with HrpB protein. This proteomic approach could provide a powerful tool in finding interaction partners of Hrp proteins whose roles in host-pathogen interaction need further studies.