We have recently shown by formally modelling human protein interaction networks (PINs) as metric spaces and classified proteins into zones based on their distance from the topological centre that hub proteins are primarily centrally located. We also showed that zones closest to the network centre are enriched for critically important proteins and are also functionally very specialised for specific house keeping functions. We proposed that proteins closest to the network centre may present good therapeutic targets. Here, we present multiple pieces of novel functional evidence that provides strong support for this hypothesis. We found that the human PINs has a highly connected signalling core, with the majority of proteins involved in signalling located in the two zones closest to the topological centre. The majority of essential, disease related, tumour suppressor, oncogenic and approved drug target proteins were found to be centrally located. Similarly, the majority of proteins consistently expressed
Title:Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?. VOLUME: 19 ISSUE: 2. Author(s):Shiliang Li, Xiaojuan Yu, Chuanxin Zou, Jiayu Gong and Xiaofeng Liu. Affiliation:School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.. Keywords:Drug targets, protein-protein interactions network, support vector machine, network topological properties.. Abstract:Identification of potential druggable targets utilizing protein-protein interactions network (PPIN) has been emerging as a hotspot in drug discovery and development research. However, it remains unclear whether the currently used PPIN topological properties are enough to discriminate the drug targets from non-drug targets. In this study, three-step classification models using different network topological properties were designed and implemented using support vector machine (SVM) to compare the enrichment of known drug targets from ...
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.
TY - JOUR. T1 - A top-down approach to infer and compare domain-domain interactions across eight model organisms. AU - Guda, Chittibabu. AU - King, Brian R.. AU - Pal, Lipika R.. AU - Guda, Purnima. PY - 2009/3/31. Y1 - 2009/3/31. N2 - Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting ...
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 ...
Cancer stem cells (CSCs) are a small subpopulation of cells within tumors with stem cell property. Increased evidence suggest that CSCs could be responsible for chemoresistance and recurrence in colorectal cancer (CRC). However, a reliable therapeutic target on CSCs is still lacking. Here we describe a two-step strategy to generate CSC targets with high selectivity for colon stem cell markers, specific proteins that are interacted with CSC markers were selected and subsequently validated in a survival analysis. TMEM17 protein was found and its biological functions in CRC cells were further examined. Finally, we utilized the Gene Set Enrichment Analysis (GSEA) to investigate the potential mechanisms of TMEM17 in CRC. By combining protein-protein interaction (PPI) database and high-throughput gene profiles, network analysis revealed a cluster of colon CSCs related genes. In the cluster, TMEM17 was identified as a novel CSCs related gene. The results of in-vitro functional study demonstrated that TMEM17
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) ...
TY - JOUR. T1 - A markov clustering based link clustering method to identify overlapping modules in protein-protein interaction networks. AU - Wang, Yan. AU - Wang, Guishen. AU - Meng, Di. AU - Huang, Lan. AU - Blanzieri, Enrico. AU - Cui, Juan. PY - 2016/4/1. Y1 - 2016/4/1. N2 - Previous studies indicated that many overlapping structures exist among the modular structures in protein-protein interaction (PPI) networks, which may reflect common functional components shared by different biological processes. In this paper, a Markov clustering based Link Clustering (MLC) method for the identification of overlapping modular structures in PPI networks is proposed. Firstly, MLC method calculates the extended link similarity and derives a similarity matrix to represent the relevance among the protein interactions. Then it employs markov clustering to partition the link similarity matrix and obtains overlapping network modules with significantly less parameters and threshold constraints compared to most ...
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 ...
TY - GEN. T1 - Combining gene expression profiles and protein-protein interactions for identifying functional modules. AU - Wang, Dingding. AU - Ogihara, Mitsunori. AU - Zeng, Erliang. AU - Li, Tao. PY - 2012/12/1. Y1 - 2012/12/1. N2 - Identifying functional modules from protein-protein interaction networks is an important and challenging task. This paper presents a new approach called PPIBM which is designed to integrate gene expression data analysis and clustering of protein-protein interactions. The proposed approach relies on a Bayesian model which uses as its base protein-protein interactions given as part of input. The proposed method is evaluated with standard measures and its performance is compared with the state-of-the-art network analysis methods. Experimental results on both real-world data and synthetic data demonstrate the effectiveness of the proposed approach.. AB - Identifying functional modules from protein-protein interaction networks is an important and challenging task. This ...
Defining protein complexes is critical to virtually all aspects of cell biology because most cellular processes are regulated by stable or more dynamic protein interactions. Elucidation of the protein-protein interaction network around transcription factors is essential to fully understand their function and regulation. In the last decade, new technologies have emerged to study protein-protein interactions under near-physiological conditions. We have developed a high-throughput tandem affinity purification (TAP)/mass spectrometry (MS) platform for cell suspension cultures to analyze protein complexes in Arabidopsis thaliana. This streamlined platform follows an integrated approach comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, TAP adapted for plant cells, and tandem matrix-assisted laser desorption ionization MS for the identification of purified proteins. Recently, we evaluated the GS tag, originally developed to ...
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 ...
Eukaryotic cells have evolved an intricate system to resolve DNA damage to prevent its transmission to daughter cells. This system, collectively known as the DNA damage response (DDR) network, includes many proteins that detect DNA damage, promote repair, and coordinate progression through the cell cycle. Because defects in this network can lead to cancer, this network constitutes a barrier against tumorigenesis. The modular BRCA1 carboxyl-terminal (BRCT) domain is frequently present in proteins involved in the DDR, can exist either as an individual domain or as tandem domains (tBRCT), and can bind phosphorylated peptides. We performed a systematic analysis of protein-protein interactions involving tBRCT in the DDR by combining literature curation, yeast two-hybrid screens, and tandem affinity purification coupled to mass spectrometry. We identified 23 proteins containing conserved BRCT domains and generated a human protein-protein interaction network for seven proteins with tBRCT. This study ...
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
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The rapid adoption of Internet technology has accelerated the establishment of platforms for virtual interaction that overcome the inherent time and space limitations of face-to-face communication. The objective of this study is to investigate the individual and network level mechanisms that characterize interactions on these electronic knowledge networks (EKNs). Toward that goal, we develop a simulation model of a thread-based asynchronous EKN and provide results based on 330 runs of the model (simulating a total of 3,643,942 messages generated by 38,860 authors). This study contributes to our understanding of electronic knowledge networks by demonstrating the importance of structural characteristics in influencing participant behaviors. We focus specifically on the role of preferential attachment (the tendency to associate with the most popular participants) and mutuality (the tendency to maintain symmetry in relationships
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.
Computational Framework for Analysis of Prey-Prey Associations in Interaction Proteomics Identifies Novel Human Protein-Protein Interactions and Networks
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
MedAI, provides customers with professional prediction of protein-protein interaction solutions according to their detailed requirements.
Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis. This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed
In a differential gene experiment, a cell perturbation can be measured on a microarray before and after the perturbation. The information from these microarrays can then be used to inference genetic pathways and protein-protein interaction networks. In this paper we reverse this idea somewhat and measure a cell perturbation through microarrays and then rely on a protein interaction map to assess which proteins are most likely influenced by the specific perturbation. This in turn helps to elucidate the functional effect the perturbation has on the cell system. The first part of the paper focuses on the propagation model we developed to obtain this information. The second part of the paper reports on a specific experiment that was driven by the interpretation we obtained through such a gene influence network. We applied a PC12 cell line that allows doxocyclin-dependent expression of constitutive active mitogen-activated protein kinase-activated protein kinase (MAPKAPK5 or MK5) to compare the
Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches - split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens - to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link
Genetics and biochemistry have been used to map many of the individual pathways that establish and maintain cell polarity in yeast, but Drees et al. (page 549) have now produced the equivalent of an aerial photograph of these processes. Using a high-throughput yeast two-hybrid screen, the authors assayed the universe of likely protein-protein interactions involved in cell polarity development. The resulting protein interaction map provides tantalizing insights and identifies dozens of potential mechanistic connections worth closer examination.. The authors used 68 yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases as baits in parallel two-hybrid screens covering ∼90% of the predicted Saccharomyces cerevisiae ORFs. The screen uncovered 128 novel protein-protein interactions, including 44 involving previously uncharacterized proteins. The appearance of known interactions in the screen, along with subcellular localization studies, ...
TY - JOUR. T1 - Protein interaction networks revealed by proteome coevolution. AU - Cong, Qian. AU - Anishchenko, Ivan. AU - Ovchinnikov, Sergey. AU - Baker, David. N1 - Funding Information: This project has been funded in part with Washington Research Foundation, National Institute of General Medical Sciences (grant no. R01-GM092802-07), National Institute of Allergy and Infectious Diseases (contract no. HHSN272201700059C), and Office of the Director of the National Institutes of Health (grant no. DP5OD026389). This research used resources of the National Energy Research Scientific Computing Center (contract no. DE-AC02-05CH11231).. PY - 2019. Y1 - 2019. N2 - Residue-residue coevolution has been observed across a number of protein-protein interfaces, but the extent of residue coevolution between protein families on the whole-proteome scale has not been systematically studied. We investigate coevolution between 5.4 million pairs of proteins in Escherichia coli and between 3.9 millions pairs in ...
mentha archives evidence collected from different sources and presents these data in a complete and comprehensive way. Its data comes from manually curated protein-protein interaction databases that have adhered to the IMEx consortium. The aggregated data forms an interactome which includes many organisms. mentha is a resource that offers a series of tools to analyse selected proteins in the context of a network of interactions. Protein interaction databases archive protein-protein interaction (PPI) information from published articles. However, no database alone has sufficient literature coverage to offer a complete resource to investigate the interactome. menthas approach generates every week a consistent interactome (graph). Most importantly, the procedure assigns to each interaction a reliability score that takes into account all the supporting evidence. mentha offers eight interactomes (Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli ...
Although protein-peptide interactions are estimated to constitute up to 40% of all protein interactions, relatively little information is available for the structural details of these interactions. Peptide-mediated interactions are a prime target for drug design because they are predominantly present in signaling and regulatory networks. A reliable data set of nonredundant protein-peptide complexes is indispensable as a basis for modeling and design, but current data sets for protein-peptide interactions are often biased towards specific types of interactions or are limited to interactions with small ligands. In PepX (http://pepx.switchlab.org), we have designed an unbiased and exhaustive data set of all protein-peptide complexes available in the Protein Data Bank with peptide lengths up to 35 residues. In addition, these complexes have been clustered based on their binding interfaces rather than sequence homology, providing a set of structurally diverse protein-peptide interactions. The final ...
virus mentha archives evidence about viral interactions collected from different sources and presents these data in a complete and comprehensive way. Its data comes from manually curated protein-protein interaction databases that have adhered to the IMEx consortium. virus mentha is a resource that offers a series of tools to analyse selected proteins in the context of a network of interactions. Protein interaction databases archive protein-protein interaction (PPI) information from published articles. However, no database alone has sufficient literature coverage to offer a complete resource to investigate the interactome. virus menthas approach generates every week a consistent interactome (graph). Most importantly, the procedure assigns to each interaction a reliability score that takes into account all the supporting evidence. virus mentha offers direct access to viral families such as: Orthomyxoviridae, Orthoretrovirinae and Herpesviridae plus, it offers the unique possibility of searching ...
The primary constituent of the amyloid plaque, beta-amyloid (Abeta), is thought to be the causal toxic moiety of Alzheimers disease. However, despite much work focused on both Abeta and its parent protein, amyloid precursor protein (APP), the functional roles of APP and its cleavage products remain to be fully elucidated. Protein-protein interaction networks can provide insight into protein function, however, high-throughput data often report false positives and are in frequent disagreement with low-throughput experiments. Moreover, the complexity of the CNS is likely to be under represented in such databases. Therefore, we curated the published work characterizing both APP and Abeta to create a protein interaction network of APP and its proteolytic cleavage products, with annotation, where possible, to the level of APP binding domain and isoform. This is the first time that an interactome has been refined to domain level, essential for the interpretation of APP due to the presence of ...
Looking for online definition of Reduced expression in cancer protein in the Medical Dictionary? Reduced expression in cancer protein explanation free. What is Reduced expression in cancer protein? Meaning of Reduced expression in cancer protein medical term. What does Reduced expression in cancer protein mean?
We have mapped SARS CoV-2 human Protein-Protein Interactions from A SARS-CoV-2 protein interaction map reveals targets for drug repurposing human drug target interacted with SARS CoV-2 are highlighted in black arrow. ...
Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either …
CTD curates specific chemical-gene and -protein interactions in vertebrates and invertebrates from published references. You may search for specific types of interactions by selecting a term or terms in this field. Each interaction has a degree and type as defined below. Degree. Each chemical-gene interaction is qualified by a degree: increases, decreases, affects, or does not affect (e.g., Chemical X increases expression of Gene Y mRNA). The affects degree is used when the reference does not describe a more specific degree. Interactions having the does not affect degree are excluded from our public data. An interaction type must be selected in order to filter by degree(s). At least one degree must be checked. Type. To select or deselect multiple interaction types, hold the Ctrl key (PC) or ⌘/Open-Apple/Command key (Mac) while clicking. Interaction types are searched in this hierarchy: ...
Some 30 years after its discovery, phage display remains one of the most widely used methods of in vitro selection. Initially developed to revolutionize the generation of therapeutic antibodies, phage display is now the first choice for screening artificial binding proteins. Artificial binding proteins can be used as reagents to study protein-protein interactions, target posttranslational modifications, and distinguish between homologous proteins. They can also be used as research and affinity reagents, for diagnostic purposes, and as therapeutics. However, the ability to identify isoform-specific reagents remains highly challenging. We describe an adapted phage display protocol using an artificial binding protein (Affimer) for the selection of isoform-selective binding proteins. ...
Proteome is a complement of proteins expressed in a cell at given time and proteomics means global analysis of this protein complement. Proteomics investigates the global changes of proteins in cells and tissues in response to a stimulus (for example temperature change, drug or nutrient treatment, or growth phase). It also studies protein-protein interactions. Proteomics came into prominence after 1997 and quickly became a popular research avenue, holding much greater importance than scientists initially suspected. The main reason for this is the fact that based on the genomic sequence it is impossible to predict how the gene products (proteins) are going to behave. Proteins are regulated at the level of protein translation, subsequently they can be modified by addition of various molecules (sugar, for example). Proteins can have varying half-lives, and their intracellular distribution can be predicted only with limited certainty.. ...
Background Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia. Methodology/Principal Findings We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological
Video articles in JoVE about g2 phase include Cell Cycle Analysis in the C. elegans Germline with the Thymidine Analog EdU, Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols, Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM), Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis.
This protein protein interaction antibody pair set comes with two antibodies to detect the protein-protein interaction, one against the NEFL protein, and the other against the APP protein for use in in situ Proximity Ligation Assay. See Publication Reference below. (DI0062) - Products - Abnova
This protein protein interaction antibody pair set comes with two antibodies to detect the protein-protein interaction, one against the HGF protein, and the other against the MET protein for use in in situ Proximity Ligation Assay. See Publication Reference below. (DI0046) - 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.
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.
Significance Analysis of INTeractome (SAINT) is a software package for scoring protein‐protein interactions based on label‐free quantitative proteomics data (e
BRIEF DESCRIPTION:. The PhD student post will be part of an interdisciplinary team working on the quantitative and systems analysis of signaling networks in colon cancer. This study is funded through Science Foundation Ireland (SFI). The project will investigate how signalling and protein interaction networks are context-specific quantitatively modulated in (patho)physiologically-relevant primary cells and in vivo-like 3D model systems. Specifically, we will study how protein interactions control cellular phenotypes by investigating (i) how they generate cell type specific vs. general functions; (ii) how pathogenic mutations affect these interactions and downstream signalling; and (iii) how signal flow changes can be validated by generating mutations that rewire networks in a designed fashion.. The student will gain valuable knowledge in the analysis of signal transduction networks in colon cancer, protein analysis techniques, and standard methods used in molecular and cell biology. The student ...
BACKGROUND: Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular ...
The superiority of KNN over KM is not surprising. With KNN, a protein of interest is clustered with the top K most signature-similar proteins in the network. With KM, a protein of interest is clustered with a signature-closest centre protein, i.e. with the centre protein having the highest signature similarity with it (see §3). However, the signature-closest centre protein is not necessarily the most signature-similar protein in the network. Additionally, KNN allows for overlap between clusters, whereas KM does not. Thus, with KNN, known cancer genes can be positioned in multiple clusters, and therefore the number of possible clusters that are significantly enriched with cancer genes might be higher for KNN than for KM. Additionally, proteins perform the function or participate in a disease by interacting with other proteins within a functional module, but also with proteins across modules. Thus, it might be biologically relevant to allow for the overlap between clusters. The better performance ...
Creative Biolabs offers high-throughput X-ray crystallography or Protein crystallography services for protein-protein interaction assays.
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Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network (specifically, GRN) growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E. coli and yeast, we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of
Ewing RM, Chu P, Elisma F, Li H, Taylor P, Climie S, McBroom-Cerajewski L, Robinson MD, OConnor L, Li M, Taylor R, Dharsee M, Ho Y, Heilbut A, Moore L, Zhang S, Ornatsky O, Bukhman YV, Ethier M, Sheng Y, Vasilescu J, Abu-Farha M, Lambert JP, Duewel HS, Stewart II, Kuehl B, Hogue K, Colwill K, Gladwish K, Muskat B, Kinach R, Adams SL, Moran MF, Morin GB, Topaloglou T, Figeys D (2007). Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 3: 89. doi:10.1038/msb4100134. PMC 1847948. PMID 17353931 ...
TY - JOUR. T1 - Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle. AU - Wang, Haiying. AU - Zheng, Huiru. PY - 2014/12/4. Y1 - 2014/12/4. N2 - The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from Gene Ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity ...
Modules that switch protein-protein interactions on and off are important to develop artificial biology; for instance, to assemble orthogonal signaling pathways, to manage synthetic protein constructions dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system {couples} nucleotide-dependent switching of MinD dimerization to membrane targeting to set off spatiotemporal sample formation. Here …. De novo design of a reversible phosphorylation-dependent switch for membrane targeting Read More ». ...
A challenge for biomedical scientists today is to arrive at an understanding of cellular behavior on a global scale. The advent of DNA microarrays has greatly facilitated discovery of gene expression profiles associated with different cellular states. The problem of understanding cellular signaling at the level of the interacting proteins is in some ways more challenging. Ashman et al. discuss the current methods available for studying protein interactions on a global scale, as well as directions for the future. Technical hurdles exist at many stages, from the isolation of protein complexes, to the determination of their composition, to the software and databases needed to analyze the results of large-scale, high-throughput datasets. Ashman et al. suggest that, with advances in technology and cooperation among academia and industry, a global protein interaction map that underlies cellular behavior will emerge as an essential resource for basic and applied research.. ...
The discovery of new Protein-Protein Interaction (PPI) modulators is currently limited by the difficulties associated with the design and synthesis of selective small molecule inhibitors. Peptides are a potential solution for disrupting PPIs; however, they typically suffer from poor stability in vivo and limited tissue penetration hampering their wide spread use as new chemical biology tools and potential therapeutics. In this work, a combination of CuAAC chemistry, molecular modelling, X-ray crystallography, and biological validation allowed us to develop highly functionalised peptide PPI inhibitors of the protein CK2. The lead peptide, CAM7117, prevents the formation of the holoenzyme assembly in vitro, slows down proliferation, induces apoptosis in cancer cells and is stable in human serum. CAM7117 could aid the development of novel CK2 inhibitors acting at the interface and help to fully understand the intracellular pathways involving CK2. Importantly, the approach adopted herein could be ...
Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as junk sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the
When a cell is perturbed by external stimuli, it responds by adjusting the amount at which different types of proteins are needed. Transcriptional regulatory networks form the core of this cellular response system. However, the static wiring of these networks does not reveal which parts of the network are active under certain conditions and how perturbations are propagated through the network. For this reason there has been much interest in integrating the static network topology with gene expression data which reflect the dynamical or functional state of the network. In a pioneering paper, large changes were identified in the subnetworks of the transcriptional regulatory network of S. cerevisiae active under five different conditions [1]. In reality, the transcriptional regulatory network cannot be considered in isolation, but it is integrated with other networks such as the protein-protein interaction network [2]. In [3], a framework was developed which integrates protein-protein and ...
We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of
Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach fo
Associate Professor of Biomedical Informatics and Intelligent Systems, University of Pittsburgh - Cited by 1,733 - Translational Bioinformatics - Networks Analysis - Machine Learning - Protein-Protein Interaction Prediction
Transcriptional regulation complexes are large, multicomponent assemblies involving proteins with various enzymatic activities, adaptor functions and DNA recognition modules. We are looking at interplay between the components of these assemblies in order to understand the structural logic of how these complexes carry out intricate biological activities.. We are studying a family of BTB-zinc finger transcriptional regulators that include proteins implicated in development and/or in cancer. In these proteins, the BTB domain is a protein-protein interaction module that recruits activator and/or corepressor complexes to promoter sites recognized by the C-terminal zinc-finger regions. Our objective is to understand and characterize the protein-protein interaction network of these proteins.. For example, we have determined crystal structures of complexes between the BTB domain of BCL6 and the minimal binding region of the SMRT, NCoR and BCoR corepressors. BCL6 is a key oncoprotein in B-cell lymphoma ...