TY - JOUR. T1 - Linkers of Cell Polarity and Cell Cycle Regulation in the Fission Yeast Protein Interaction Network. AU - Vaggi, Federico. AU - Dodgson, James. AU - Bajpai, Archana. AU - Chessel, Anatole. AU - Jordán, Ferenc. AU - Sato, Masamitsu. AU - Carazo-Salas, Rafael Edgardo. AU - Csikász-Nagy, Attila. PY - 2012/10. Y1 - 2012/10. N2 - The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify linker proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key ...
How many ways protein protein interactions are regulated? - posted in Biochemistry: I wonder how many ways protein-protein interactions are regulated. I know a lot of protein protein interactions are modified by phosphorylaton or other modification. Is there other ways that mediate the protein protein interaction? Thanks!
Receptor tyrosine kinase EGFR is usually localized on plasma membrane inducing the progression of many cancers including malignancy in children (Bodey et al. In Vivo. 2005, 19:931-41), but it contains a nuclear localization signal (NLS) that mediates EGFR nuclear translocation (Lin et al. Nat Cell Biol. 2001, 3:802-8). In this report, we claim that NLS of EGFR has an old evolutionary origin. In particular, our analysis of protein-protein interaction maps reveals that nuclear EGFR (nEGFR) pathways are different from that of membrane EGFR and EGF is not found in nEGFR network, while androgen receptor (AR) is found, which suggests the evolution of prostate cancer, a well-known AR driven cancer, through changes in androgen- or EGF-dependence. Database analysis shows that nEGFR correlates with the tumor grades especially in prostate cancer patients. Structural prediction analysis indicates that NLS can compromise the differential protein binding to EGFR through stretch linkers with evolutionary ...
TY - JOUR. T1 - Association rulemining from yeast protein inetraction to assist protein-protein interaction prediction. AU - Chiu, Hung-Wen. AU - Hung , Fei-Hung PY - 2008. Y1 - 2008. N2 - Protein protein interaction (PPI) is very important information for constructing biological pathways in this systems biology era. Recently many PPI-related databases have been created by high-throughput wet-lab methods. However, in-silico methods developed to predict PPIs are significant techniques for obtaining the whole aspect of PPI networks. Functional regions of a protein defined by specific amino-acid sequences are the key components on determining the role the protein play in a biological process. Association rule mining is a popular data mining skill for finding the association of components in an itemset. Therefore, to mining the associations of functional regions of two interacting proteins will be helpful for PPI prediction. In this study, we collected yeast PPI data from DIP and IntAct, and ...
Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to
The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted
Understanding the roles and consequences of protein-protein interactions is a fundamental goal in cellular biology and a prerequisite for the development of molecular systems biology. The endeavor of cataloging protein interactions is primarily hindered by the throughput and reproducibility of existing technologies. Different techniques for mapping protein interactions are available, such as the two‐hybrid approach (Chien et al, 1991) and the LUMIER approach (Barrios‐Rodiles et al, 2005) and assay whether two proteins interact in a pair‐wise fashion. We have developed a high‐throughput platform combining immunoprecipitation and high‐throughput mass spectrometry (IP‐HTMS) to rapidly identify potentially novel protein interactions for a bait protein of interest. We (Ho et al, 2002) and others (Gavin et al, 2002) previously used this approach to map protein-protein interactions in yeast, creating invaluable data sets for yeast biology and extrapolation into mammalian biology.. Mapping ...
Profacgen provides One-Stop-Service on protein-protein interaction analysis, including Yeast two-hybrid, Pull-downs and Surface Plasmon Resonance (SPR) assay etc., to facilitate your scientific research. Our service can be tailored according to your specific requirements.
Towards Inter- and Intra- Cellular Protein Interaction Analysis: Applying the Betweenness Centrality Graph Measure for Node Importance
In recent years, protein-protein interaction (PPI) datasets have been recognized as important resources to elucidate various biological processes and cellular mechanisms. The prediction of protein complexes from PPIs (see, for example, survey papers [1-3]) is one of the most challenging inference problems from PPIs because protein complexes are essential entities in the cell. Proteins functions are manifested in the form of a protein complex. Thus, the identification of protein complexes is necessary for the precise description of biological systems.. For protein complex prediction, many computational methods have been proposed, which were directly or indirectly designed based on the observation that densely connected subgraphs, or clusters of proteins, of a whole PPI network often overlap with known complexes. This observation is often valid for relatively large protein complexes. However, small complexes, consisting of two or three proteins, form a major category of the known complexes of an ...
Protein Interaction Analysis ProteOn XPR36 Protein Interaction Array System 248 Instrument 248 Software 249 Regulatory Tools 249 Sensor Chips 250 Kits, Reagents, and Consumables 250 Ordering Information
단백질 상호 작용은 세포의 기능의 핵심이다. 열량 및 분광 기술은 일반적으로 그 특성을하는 데 사용됩니다. 여기에서 우리는 Shwachman - 다이아몬드 증후군 (SBDS)에 돌연변이 단백질과 신장 인자 같은 1는 GTPase (EFL1) ...
Researchers from the Institut Curie and from the Paris-based biotechnology company Hybrigenics announced today that they have built a protein-protein interaction map of the fruit fly, Drosophila melanogaster. This simple model organism allows them to study a reference set of proteins that includes most of those known to be involved in human cancer. Since proteins function in networks, the systematic identification of the physical interactions that occur between proteins will help understanding their biological function, and improve our capacity to intervene and, ultimately, to discover novel, more specific therapeutic targets. Their results are published in the March 1st issue of Genome Research ...
SVM and RF contributed 15 and 10 unique predictions, respectively, that were confirmed experimentally. Thus, the two methods were somewhat complementary and, if used together, may provide better coverage of true predictions. On the other hand, using the overlap of predictions from both SVM and RF provides a more conservative and, hence, more reliable list of protein interactions that could be used as a starting point for further investigations.. The Cbf11 interactors predicted were significantly enriched for the experimentally determined targets, both in the case of SVM (Fishers exact test, P = 10−14), RF (Fishers P = 10−28), and in the overlap of the two methods (Fishers P = 10−28). So far, there are no known genetic interactions for Cbf11 and no functional interactions due to the protein not being conserved in budding yeast. For this reason, our method for predicting its physical association partners can only be compared with selecting proteins at random from the whole genome. The ...
Some experimental protocols, such as tandem affinity purification (TAP), generate complex data sets, depicting interactions in which more than two proteins are involved at the same time (n-ary interactions).. However, interaction data are often stored in tabular formats that aim to be amenable to quick, comprehensive searches. It may be desirable to convert these complexes into sets of binary interactions to simplify and speed up searches. There are two algorithms that will perform such conversion: the matrix model and the spoke model 2, as depicted in Figure 10. In this hypothetical example, take the bottom right protein complex (marked "reality"). A tandem affinity experiment (far left) might tell you that each of the other five proteins interact with the red bait protein in the middle. In reality, the red protein has only one interactor, which is the yellow protein. As you can see, both algorithms are somewhat mis-leading, but as the spoke model generates up to 3 times fewer false positives ...
Abstract Background Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. Description DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted
Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein-protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as
Background The individual contribution of genes in the HLA region to the risk of developing type 1 diabetes (T1D) is confounded by the high linkage disequilibrium (LD) in this region. Using a novel approach we have combined genetic association data with information on functional protein-protein interactions to elucidate risk independent of LD and to place the genetic association into a functional context. Methodology/Principal Findings Genetic association data from 2300 single nucleotide polymorphisms (SNPs) in the HLA region was analysed in 2200 T1D family trios divided into six risk groups based on HLA-DRB1 genotypes. The best SNP signal in each gene was mapped to proteins in a human protein interaction network and their significance of clustering in functional network modules was evaluated. The significant network modules identified through this approach differed between the six HLA risk groups, which could be divided into two groups based on carrying the DRB1*0301 or the DRB1*0401 allele. Proteins
Many nonsynonymous single nucleotide polymorphisms (nsSNPs) are disease causing due to effects at protein-protein interfaces. We have integrated a database of the three-dimensional (3D) structures of human protein/protein complexes and the humsavar database of nsSNPs. We analyzed the location of nsSNPS in terms of their location in the protein core, at protein-protein interfaces, and on the surface when not at an interface. Disease-causing nsSNPs that do not occur in the protein core are preferentially located at protein-protein interfaces rather than surface noninterface regions when compared to random segregation. The disruption of the protein-protein interaction can be explained by a range of structural effects including the loss of an electrostatic salt bridge, the destabilization due to reduction of the hydrophobic effect, the formation of a steric clash, and the introduction of a proline altering the main-chain conformation. ...
Here, we described the multiplex in vivo measurement of 1,379 protein-protein interactions in 14 environmental conditions, to our knowledge the most extensive direct study of how protein interaction networks respond dynamically to extrinsic environmental perturbations. The most striking finding was the prevalence of dynamic binary complexes. More than half of the PPIs we considered (757 of 1,379) responded to at least one perturbation. The environmental perturbations that yielded the largest number of changes relative to our reference condition were respiratory growth in ethanol, heat shock, oxidative stress, and DNA damage. That these responses were the most profound might have been expected, as these conditions are likely to have been frequently experienced in the evolutionary history of yeast (Gasch & Werner‐Washburne, 2002; Gasch, 2007), allowing for selection and maintenance of a complex adaptive regulatory strategy. We observed that proteins with certain functions were more likely to ...
VisualComplexity.com is a unified resource space for anyone interested in the visualization of complex networks. The projects main goal is to leverage a critical understanding of different visualization methods, across a series of disciplines, as diverse as Biology, Social Networks or the World Wide Web.
Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins. Come learn web-based gene annotation, gene ID conversion, pathway enrichment, and protein-protein interaction networks and automate the process ...
An online database that integrates the extracellular protein interaction network. ARNIE allows users to browse the network by clicking on individual proteins, or by specifying the spatiotemporal parameters using the drop-down menus. Clicking on connector lines will allow users to compare stage-matched expression patterns for genes encoding interacting proteins. Additionally, users can rapidly search for their genes in the network using the BLAST server provided.
Noncoding genetic variation is a major driver of phenotypic diversity, but functional interpretation is challenging. To better understand common genetic variation associated with brain diseases, we defined noncoding regulatory regions for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with variants in transcriptional enhancers and promoters in neurons, sporadic Alzheimers disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting disease-risk variants in cell-type-specific enhancers to promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia, but not in neurons or astrocytes. These findings revise and expand the list of genes likely to be influenced by noncoding variants in AD and suggest the probable cell types in which they function. ...
Predicting Protein Functions from Protein Interaction Networks: 10.4018/978-1-60566-398-2.ch012: Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of
Specific protein-protein interactions form a major part of the basic organization of living cells. The structure of a protein complex holds information about the relative mutual organization of two proteins in a frozen state, but not about the kinetic or thermodynamic parameters that are central to their function. For many years I have been interested in understanding the relationships between the structures of transient protein-protein interactions and their binding activity. To address this, I adopted a multidisciplinary approach including: wet biophysical bench work, protein-design and engineering, bioinformatics, and algorithm development and applied the gained knowledge and techniques to address biological questions. As part of this I-CORE I am investigating structure/function aspects of type I interferon-receptor interactions and how these are related to signaling. Through a comprehensive analysis of the structure, energetics and dynamics of IFN recognition by its receptor subunits, and of ...
Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (Oct 2005). "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173-8. doi:10.1038/nature04209. PMID 16189514 ...
Rual JF، Venkatesan K، Hao T، Hirozane-Kishikawa T، Dricot A، Li N، Berriz GF، Gibbons FD، Dreze M، Ayivi-Guedehoussou N، Klitgord N، Simon C، Boxem M، Milstein S، Rosenberg J، Goldberg DS، Zhang LV، Wong SL، Franklin G، Li S، Albala JS، Lim J، Fraughton C، Llamosas E، Cevik S، Bex C، Lamesch P، Sikorski RS، Vandenhaute J، Zoghbi HY، Smolyar A، Bosak S، Sequerra R، Doucette-Stamm L، Cusick ME، Hill DE، Roth FP، Vidal M (2005). "Towards a proteome-scale map of the human protein-protein interaction network.". Nature. 437 (7062): 1173-8. PMID 16189514. doi:10.1038/nature04209. ...
Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M (October 2005). "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173-8. doi:10.1038/nature04209. PMID 16189514 ...
Mass spectrometry is a powerful analytical technique that can accurately measure the molecular masses of individual biomolecules, including peptides, proteins, and large intact protein assemblies. Chaits lab specializes in the development of mass spectrometers and other tools and methods for investigating a variety of biological and biochemical phenomena. Knowledge of the makeup, structure, and dynamics of protein assemblies is key to understanding many cellular processes. The Chait lab devises new tools, including those based on quantitative mass spectrometry, to identify and study the protein interactions within these assemblies. Another primary goal of the lab is to derive a functional definition of cellular protein assemblies.. The lab has recently developed potent approaches for elucidating proximal, distal, and transient protein-protein interactions in cellular milieus, and for determining distance restraints between amino-acid residues within large protein assemblies by chemical ...
Sandrock, TM, ODell, J L and Adams, A E M (1997) Allele-specific suppression by formation of new protein-protein interactions. Genetics, 147 (4). pp. 1635-42. Full text not available from this repository ...
Sigma-Aldrich offers abstracts and full-text articles by [D Trisciuoglio, M Desideri, V Farini, T De Luca, M Di Martile, M G Tupone, A Urbani, S DAguanno, D Del Bufalo].
To understand living cells one must study them as systems rather than as a collection of individual molecules. The abstract representation of intracellular systems as networks is fruitful, because it provides the ability to study these systems as a whole by ignoring details of individual components, but retaining the complexity of the interactions. This chapter will review the discoveries made through application of approaches from the science of complex networks to Protein Interaction Networks, i.e. undirected networks in which the nodes represent proteins, and pairs are connected by edges if the proteins physically interact. Over the last decade the experimental techniques for measuring protein interactions has been highly improved and large numbers of new protein interactions have been elucidated. Therefore, along with the reviewed concepts and discoveries, we provide a re-evaluation of several previous conclusions by analyzing a set of high quality networks from the organism S. ...
The study of cytosolic protein complexes, calledsystems or functional proteomics, is complicated by the challenges associated with purifying unadulterated, functional complexes, and with developing analytical methods for studying protein structure that can accommodate high molecular masses, or weak and transient protein-protein interactions
Signal transmission progresses via a series of transient protein-protein interactions and protein movements, which require diffusion within a cell packed with different molecules. Yeast Hog1, the effector protein kinase of the High Osmolarity Glycerol pathway, translocates transiently from the cytosol to the nucleus during adaptation to high external osmolarity. We followed the dynamics of osmostress-induced cell volume loss and Hog1 nuclear accumulation upon exposure of cells to different NaCl concentrations. While Hog1 nuclear accumulation peaked within five minutes following mild osmotic shock it was delayed up to six-fold under severe stress. The timing of Hog1 nuclear accumulation correlated with the degree of cell volume loss and the cells capacity to recover. Also the nuclear translocation of Msn2, the transcription factor of the general stress response pathway, is delayed upon severe osmotic stress suggesting a general phenomenon. We show by direct measurements that the general diffusion rate of
Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the protein network. The disease-associated IDPs may provide potential targets for drugs modulating protein-protein interaction networks. Therefore, novel strategies for drug discovery based on
The use of λ repressor fusions to study protein-protein interactions in E. coli was first described by Hu and others (1). Since then, the repressor system has been employed by several laboratories to...
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Nuria Sánchez-Puig is the author of this article in the Journal of Visualized Experiments: Fluorescence Anisotropy as a Tool to Study Protein-protein Interactions
There are many characteristics of a protein-protein interaction that are important. Obviously, it is important to know which proteins are interacting. In many experiments and computational studies, the focus is on interactions between two different proteins. However, you can have one protein interacting with other copies of itself (oligomerization), or three or more different proteins interacting. The stoichiometry of the interaction is also important - that is, how many of each protein involved are present in a given reaction. Some protein interactions are stronger than others, because they bind together more tightly. The strength of binding is known as affinity. Proteins will only bind each other spontaneously if it is energetically favorable. Energy changes during binding are another important aspect of protein interactions. Many of the computational tools that predict interactions are based on the energy of interactions.. In recent years there has been a strong focus on predicting protein ...
Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial...
Background There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region. Methodology A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules. Results The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be
To understand tissue morphogenesis and disease pathogenesis, ultimately we must understand what happens at the cellular and molecular levels. To do this, we use a number of techniques. To identify new protein-protein interactions important for establishing the machinery through which junctions carry out structural and signaling functions, we use in vitro biochemistry and a variety of protein interaction screens, including recently emerging Bio-ID proteomic approaches that allow for mapping nearest neighbors in cells and tissues in situ. How these protein interactions are regulated by post-translational modifications, including phosphorylation and protein methylation is also being uncovered through the use of mass spectrometry approaches.. To look at the importance of these protein interactions in cells, state-of-the-art optical imaging techniques are being employed. Fluorescently-tagged wild type and mutant desmosome and adherens junction molecules are tracked during intercellular junction ...
Protein-protein interactions (PPIs) play a critical role in all cellular processes, ranging from cellular division to apoptosis. Elucidating and analyzing PPIs is thus essential to understanding the underlying mechanisms in biology. Indeed, this has been a major focus of research in recent years, providing a wealth of experimental data about protein associations [1-9]. Current PPI networks have been constructed using a number of techniques, such as yeast-two-hybrid (Y2H), co-immunopurification or coaffinity purification, followed by mass spectroscopy and curation of published low-throughput experiments [10-16]. Despite this tremendous push, the current coverage of PPIs is still rather poor (for example, , 10% of interactions in humans) [17]. Additionally, despite considerable improvements in high-throughput (HTP) techniques, they are still prone to spurious errors and systematic biases, yielding a significant number of false-positives and false-negatives [18-21]. This limitation impedes our ...
Institut National de la Sante et de la Recherche Medicale U124-IRCL, Lille, France. The BTB/POZ domain defines a newly characterized protein-protein interaction interface. It is highly conserved throughout metazoan evolution and generally found at the NH2 terminus of either actin-binding or, more commonly, nuclear DNA-binding proteins. By mediating protein binding in large aggregates, the BTB/POZ domain serves to organize higher order macromolecular complexes involved in ring canal formation or chromatin folding ...
Xin Guo Introducing... the DOMain-oriented Alignment of Interaction Networks (DOMAIN). Previous paradigms include the node-then-edge-alignment paradigm and direct-edge-alignment paradigm. In the latter, interactions are more likely to be conserved. Many studies have suggested that direct PPIs can be mediated by interactions of their domains. Their method follows the direct-edge-alignment paradigm. In the method: try to…
Protein-protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein-protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to ...
This protein protein interaction antibody pair set comes with two antibodies to detect the protein-protein interaction, one against the TRAF5 protein, and the other against the TRAF3 protein for use in in situ Proximity Ligation Assay. See Publication Reference below. (DI0413) - Products - Abnova
This protein protein interaction antibody pair set comes with two antibodies to detect the protein-protein interaction, one against the CDC20 protein, and the other against the BUB1B protein for use in in situ Proximity Ligation Assay. See Publication Reference below. (DI0076) - Products - Abnova
Wiki-Pi: a web resource for human protein-protein interactions. It shows genes and PPIs with information about pathways, protein-protein interactions (PPIs), Gene Ontology (GO) annotations including cellular localization, molecular function and biological process, drugs, diseases, genome-wide association studies (GWAS), GO enrichments, PDB ID, Uniprot ID, HPRD ID, and word cloud from pubmed abstracts.