IDDI: integrated domain-domain interaction and protein interaction analysis system

Background

Deciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.

Method

We integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.

Result

IDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.

     

Mitochondrial protein import: from transport pathways to an integrated network

Mitochondria, the powerhouses of the cell, import most of their proteins from the cytosol. It was originally assumed that mitochondria imported precursor proteins via a general pathway but recent studies have revealed a remarkable variety of import pathways and mechanisms. Currently, five different protein import pathways can be distinguished. However, the import machineries cooperate with each other and are connected to other systems that function in the respiratory chain, mitochondrial membrane organization, protein quality control and endoplasmic reticulum-mitochondria junctions. In this Opinion, we propose that mitochondrial protein import should not be seen as an independent task of the organelle and that a network of cooperating machineries is responsible for major mitochondrial functions.     

ADMETNet:The knowledge base of pharmacokinetics and toxicology network

<正>A good drug or drug candidate should not only interact with its target molecule effectively and specifically,but also be absorbed into the body,distributed to the right location,metabolized into right compounds,and eliminated out of the body in proper manner.The processes of drug getting into and out of the body involve Absorption,Distribution,Metabolism,and Excretion(ADME),which is     

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

Background  

The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.     

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.     

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

Background  

Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking.     

Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.     

A network synthesis model for generating protein interaction network families

In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.     

The protein interaction network of the epithelial junctional complex: a system-level analysis

To acquire system-level understanding of the intercellular junctional complex, protein–protein interactions occurring at the junctions of simple epithelial cells have been examined by network analysis. Although proper hubs (i.e., very rare proteins with exceedingly high connectivity) were absent from the junctional network, the most connected (albeit nonhub) proteins displayed a significant association with essential genes and contributed to the “small world” properties of the network (as shown by in vivo and in silico deletion, respectively). In addition, compared with a random network, the junctional network had greater tendency to form modules and subnets of densely interconnected proteins. Module analysis highlighted general organizing principles of the junctional complex. In particular, two major modules (corresponding to the tight junctions and to the adherens junctions/desmosomes) were linked preferentially to two other modules that acted as structural and signaling platforms.     

PowerMarker: an integrated analysis environment for genetic marker analysis

SUMMARY: PowerMarker delivers a data-driven, integrated analysis environment (IAE) for genetic data. The IAE integrates data management, analysis and visualization in a user-friendly graphical user interface. It accelerates the analysis lifecycle and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker. AVAILABILITY: www.powermarker.net     

A knowledge base for predicting protein localization sites in eukaryotic cells

To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing a knowledge base by organizing various experimental and computational observations as a collection of if-then rules. Here we report an expert system, which utilizes this knowledge base, for predicting localization sites of proteins only from the information on the amino acid sequence and the source origin. We collected data for 401 eukaryotic proteins with known localization sites (subcellular and extracellular) and divided them into training data and testing data. Fourteen localization sites were distinguished for animal cells and 17 for plant cells. When sorting signals were not well characterized experimentally, various sequence features were computationally derived from the training data. It was found that 66% of the training data and 59% of the testing data were correctly predicted by our expert system. This artificial intelligence approach is powerful and flexible enough to be used in genome analyses.     

EPO-KB: a searchable knowledge base of biomarker to protein links

The knowledge base EPO-KB (Empirical Proteomic Ontology Knowledge Base) is based on an OWL ontology that represents current knowledge linking mass-to-charge (m/z) ratios to proteins on multiple platforms including Matrix Assisted Laser/Desorption Ionization (MALDI) and Surface Enhanced Laser/Desorption Ionization (SELDI)--Time of Flight (TOF). At present, it contains information on m/z ratio to protein links that were extracted from 120 published research papers. It has a web interface that allows researchers to query and retrieve putative proteins that correspond to a user-specified m/z ratio. EPO-KB also allows automated entry of additional m/z ratio to protein links and is expandable to the addition of gene to protein and protein to disease links. AVAILABILITY: http://www.dbmi.pitt.edu/EPO-KB