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Results for “Protein Interaction Mapping”

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PubMed2006

Mapping protein-protein interaction by 13C'-detected heteronuclear NMR spectroscopy.

The copper-mediated protein-protein interaction between yeast Atx1 and Ccc2 has been examined by protonless heteronuclear NMR and compared with the already available (1)H-(15)N HSQC information. The observed chemical shift variations are analyzed with respect to the actual solution structure, available through intermolecular NOEs. The advantage of using the CON-IPAP spectrum with respect to the (1)H-(15)N HSQC resides in the increased number of signals observed, including those of prolines. CBCACO-IPAP experiments allow us to focus on the interaction region and on side-chain carbonyls, while a newly designed CEN-IPAP experiment on side-chains of lysines. An attempt is made to rationalize the chemical shift variations on the basis of the structural data involving the interface between the proteins and the nearby regions. It is here proposed that protonless (13)C direct-detection NMR is a useful complement to (1)H based NMR spectroscopy for monitoring protein-protein and protein-ligand interactions.

Carbon Isotopes↗
PubMed1994

elk-1 proteins interact with MAP kinases.

Mitogen activated protein kinases (MAP) or extracellular signal regulated protein kinases (ERK) are a family of protein serine/threonine kinases that are activated very rapidly in response to many extracellular stimuli. elk-1, an ets related gene codes for two transcriptional factors elk-1, which regulates c-fos transcription and delta elk-1, both of which are substrates for MAP kinases. A part of the C-terminal transcriptional activation domain (ETA-2) which is common to both the proteins was previously shown to function as an activator of MAP kinases. In this report, in an attempt to investigate the mechanism of activation of MAP kinases, purified preparations of recombinant elk-1 and P44mpk/ERK-1/ERK-2 proteins were used to show the association of elk-1 proteins with MAP kinases. The specific interactions of elk-1 proteins with MAP kinases were confirmed by co-immunoprecipitation studies. Thus elk-1 proteins appear to regulate the activity of MAP kinases by interacting with them ensuring a conformational change and stimulating their autophosphorylation and activation property. The activation was dependent on the presence of ATP and Mg2+. In vitro phosphorylation of elk-1 protein was not regulatory for autonomous DNA binding activity of elk-1 protein. Cells which were exposed to EGF showed a rapid stimulation of an elk-1 specific kinase activity, probably MAP kinase which phosphorylated MBP and was found to be associated with immobilized GST-elk-1. Furthermore, dephosphorylation studies indicate that elk-1 proteins can activate only tyrosine phosphorylated MAP kinase. These results demonstrate the presence of an alternative pathway/mechanism (other than MAP kinase kinase, MAPKK/Mek) for the activation of MAP kinases with tyrosine phosphorylation occurring before serine/threonine autophosphorylation and activation by elk-1 proteins.

3T3 Cells↗
PubMed2004

Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.

Cell Line↗
PubMed2026

Quantitative interactome mapping of skeletal muscle insulin resistance.

Protein-protein interactions (PPIs) are dynamic and critical to adaptive homeostasis. While there have been massive efforts to catalogue proteome-wide PPIs, global quantification of changes remains a challenge. Here, we integrate dynamic protein correlation profiling - mass spectrometry (PCP-MS) and quantitative cross linking-mass spectrometry (qXL-MS) using multiplexed stable isotope labelling to characterise global PPI remodelling following the development of chronic skeletal muscle insulin resistance (IR) with or without acute insulin stimulation. We quantify >7,000 unique PPIs amongst 5,346 proteins and show changes in the interactome network dominate the proteome response. Our data show the dysregulation of protein processing in the endoplasmic/sarcoplasmic reticulum involving changes in PPIs with protein chaperones and disulfide isomerases is a major hallmark of skeletal muscle IR. Mechanistically, we show the dysregulation of PPIs with Protein-Disulfide Isomerase 6 (PDIA6) regulates cysteine oxidation and insulin sensitivity. Taken together, we show in vivo quantitative interactome mapping is a powerful approach to understand disease mechanisms and provide new insights into protein network re-organisations with IR.

Insulin Resistance↗
PubMed2002

Transcription factors do it together: the hows and whys of studying protein-protein interactions.

Protein-protein interactions are intrinsic to virtually every cellular process. Recent breakthroughs in techniques to study protein-interaction and the availability of fully sequenced plant genomes have attracted many plant scientists to undertake the first steps in the field of protein interactions. High-throughput screening systems allow the discovery of protein functions. Even without performing laborious functional assays, in planta functional homologues and redundant proteins can be accurately predicted based on protein-interaction maps. Therefore, protein-protein-interaction screenings are an essential supplement to the current functional-genomics toolbox.

DNA-Binding Proteins↗
PubMed2003

Mapping protein: carbohydrate interactions.

Many biologically important interactions occur between proteins and carbohydrates. The examination of these interactions at the atomic level is critical not only in understanding the nature of these interactions and their biological role, but also in the design of effective modulators of these interactions. While experimentally obtained structural information is preferred, quite often this information is unavailable. In order to address this, several methods have been developed to probe the interactions between protein and carbohydrate in the absence of structural data. These methods map the interactions between protein and carbohydrate, and identify the groups involved, both at the carbohydrate and protein level. Here, we review these developments, and examine the strengths, weaknesses, and pitfalls of these methods.

Animals↗
PubMed2001

Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast.

In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. To study the large-scale patterns and evolution of interactions between protein domains, we view interactions between protein domains in terms of the interactions between structural families of evolutionarily related domains. This allows us to classify 8151 interactions between individual domains in the Protein Data Bank and the yeast Saccharomyces cerevisiae in terms of 664 types of interactions, between protein families. At least 51 interactions do not occur in the Protein Data Bank and can only be derived from the yeast data. The map of interactions between protein families has the form of a scale-free network, meaning that most protein families only interact with one or two other families, while a few families are extremely versatile in their interactions and are connected to many families. We observe that almost half of all known families engage in interactions with domains from their own family. We also see that the repertoires of interactions of domains within and between polypeptide chains overlap mostly for two specific types of protein families: enzymes and same-family interactions. This suggests that different types of protein interaction repertoires exist for structural, functional and regulatory reasons.

Binding Sites↗
PubMed2002

Protein interaction domain mapping for the selection of validated targets and lead compounds in the anti-infectious area.

Despite considerable progress in the analysis of microbial genomes, the number of validated targets suitable for the development of drugs acting on agents causing infectious diseases remains modest. The diversity of new chemical entities specific for such targets has almost not increased over the last years, while resistance to anti-infectious drugs has, in contrast, become a real threat in certain surroundings. New strategies are thus needed for selecting novel validated targets. We discuss here a combined approach which uses protein interaction mapping as the basic strategy to identify interacting domains which then serve to validate newly identified targets. The interactome of Helicobacter pylori is used as model to successively describe high-throughput protein interaction mapping, use of the H. pylori data to predict the interactome from other bacteria, analysis of interacting domains, and evaluation of the capacity of one such domain to block synthesis of flagella. The general applicability of this approach to target identification and validation, and to development of novel compounds is also discussed.

Bacterial Proteins↗
PubMed2003

Protein-DNA interaction mapping using genomic tiling path microarrays in Drosophila.

We demonstrate the use of a chromosomal walk (or "tiling path") printed as DNA microarrays for mapping protein-DNA interactions across large regions of contiguous genomic DNA in Drosophila melanogaster. Microarrays were constructed with genomic DNA fragments 430-920 bp in length, covering 2.9 million base pairs of the Adh-cactus region of chromosome 2 and 85,000 base pairs of the 82F region of chromosome 3. We performed DNA localization mapping for the heterochromatin protein HP1 and for the sequence-specific GAGA transcription factor, producing a comprehensive, high-resolution map of in vivo protein-DNA interactions throughout these regions of the Drosophila genome.

Amino Acid Motifs↗
PubMed2006

Mapping protein-protein interactions by mass spectrometry.

Mass spectrometry is currently at the forefront of technologies for mapping protein-protein interactions, as it is a highly sensitive technique that enables the rapid identification of proteins from a variety of biological samples. When used in combination with affinity purification and/or chemical cross-linking, whole or targeted protein interaction networks can be elucidated. Several methods have recently been introduced that display increased specificity and a reduced occurrence of false-positives. In the future, information gained from human protein interaction studies could lead to the discovery of novel pathway associations and therapeutic targets.

Animals↗
PubMed2005

Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships.

MOTIVATION: Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein-protein interfaces to predict putative protein interaction pairs. RESULTS: We have obtained a large amount of putative protein-protein interaction (approximately 130,000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions.

Algorithms↗
PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗
PubMed2001

Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs".

Protein interaction maps have provided insight into the relationships among the predicted proteins of model organisms for which a genome sequence is available. These maps have been useful in generating potential interaction networks, which have confirmed the existence of known complexes and pathways and have suggested the existence of new complexes and or crosstalk between previously unlinked pathways. However, the generation of such maps is costly and labor intensive. Here, we investigate the extent to which a protein interaction map generated in one species can be used to predict interactions in another species.

Amino Acid Sequence↗
PubMed2003

Inference of common genetic network using fuzzy adaptive resonance theory associated matrix method.

Inferring genetic networks from gene expression data is the most challenging work in the post-genomic era. However, most studies tend to show their genetic network inference ability by using artificial data. Here, we developed the fuzzy adaptive resonance theory associated matrix (F-ART matrix) method to infer genetic networks and applied it to experimental time series data, which are gene expression profiles of Saccharomyces cerevisiae responding under oxidative stresses such as diamide, heat shock and H2O2. We preprocessed them using the fuzzy adaptive resonance theory and successfully identified genetic interactions by drawing a 2-dimensional matrix. The identified interactions between diamide and heat shock stress were confirmed to be the common interactions for two stresses, compared with the KEGG metabolic map, BRITE protein interaction map, and gene interaction data of other papers. In the predicted common genetic network, the hit ratio was 60% for the KEGG map. Several gene interactions were also drawn, which have been reported to be important in oxidative stress. This result suggests that F-ART matrix has the potential to function as a new method to extract the common genetic networks of two different stresses using experimental time series microarray data.

Journal Article↗
PubMed2005

Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map).

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.

Algorithms↗
PubMed2004

A protein interaction framework for human mRNA degradation.

The degradation of mRNA is an important regulatory step in the control of gene expression. However, mammalian RNA decay pathways remain poorly characterized. To provide a framework for studying mammalian RNA decay, a two-hybrid protein interaction map was generated using 54 constructs from 38 human proteins predicted to function in mRNA decay. The results provide evidence for interactions between many different proteins required for mRNA decay. Of particular interest are interactions between the poly(A) ribonuclease and the exosome and between the Lsm complex, decapping factors, and 5'-->3' exonucleases. Moreover, multiple interactions connect 5'-->3' and 3'-->5' decay proteins to each other and to nonsense-mediated decay factors, providing the opportunity for coordination between decay pathways. The interaction network also predicts the internal organization of the exosome and Lsm complexes. Additional interactions connect mRNA decay factors to many novel proteins and to proteins required for other steps in gene expression. These results provide an experimental insight into the organization of proteins required for mRNA decay and their coupling to other cellular processes, and the physiological relevance of many of these interactions are supported by their evolutionary conservation. The interactions also provide a wealth of hypotheses to guide future research on mRNA degradation and demonstrate the power of exhaustive protein interaction mapping in aiding understanding of uncharacterized protein complexes and pathways.

Carrier Proteins↗
PubMed2001

Mapping protein-protein interactions with combinatorial biology methods.

Extremely diverse, DNA-encoded libraries of peptides and proteins have been constructed that include a linkage between each polypeptide and the encoding DNA. Library members can be selected by virtue of a particular binding specificity, and their protein sequence can be deduced from the sequence of the cognate DNA. Such combinatorial biology methods have proven invaluable in both identifying natural protein-protein interactions and also in mapping the specificities and energetics of these interactions in fine detail.

Amino Acid Sequence↗