Fusing literature and full network data improves disease similarity computation.

BACKGROUND: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. RESULTS: Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively. CONCLUSIONS: Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

Revealing the role of oxidation state in interaction between nitro/amino-derived particulate matter and blood proteins.

Surface oxidation states of ultrafine particulate matter can influence the proinflammatory responses and reactive oxygen species levels in tissue. Surface active species of vehicle-emission soot can serve as electron transfer-mediators in mitochondrion. Revealing the role of surface oxidation state in particles-proteins interaction will promote the understanding on metabolism and toxicity. Here, the surface oxidation state was modeled by nitro/amino ligands on nanoparticles, the interaction with blood proteins were evaluated by capillary electrophoresis quantitatively. The nitro shown larger affinity than amino. On the other hand, the affinity to hemoglobin is 10(3) times larger than that to BSA. Further, molecular docking indicated the difference of binding intensity were mainly determined by hydrophobic forces and hydrogen bonds. These will deepen the quantitative understanding of protein-nanoparticles interaction from the perspective of surface chemical state.

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases.

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.

Structural and functional analysis of the loading acyltransferase from avermectin modular polyketide synthase.

The loading acyltransferase (AT) domains of modular polyketide synthases (PKSs) control the choice of starter units incorporated into polyketides and are therefore attractive targets for the engineering of modular PKSs. Here, we report the structural and biochemical characterizations of the loading AT from avermectin modular PKS, which accepts more than 40 carboxylic acids as alternative starter units for the biosynthesis of a series of congeners. This first structural analysis of loading ATs from modular PKSs revealed the molecular basis for the relaxed substrate specificity. Residues important for substrate binding and discrimination were predicted by modeling a substrate into the active site. A mutant with altered specificity toward a panel of synthetic substrate mimics was generated by site-directed mutagenesis of the active site residues. The hydrolysis of the N-acetylcysteamine thioesters of racemic 2-methylbutyric acid confirmed the stereospecificity of the avermectin loading AT for an S configuration at the C-2 position of the substrate. Together, these results set the stage for region-specific modification of polyketides through active site engineering of loading AT domains of modular PKSs.

Molecular insight into the steric shielding effect of PEG on the conjugated staphylokinase: biochemical characterization and molecular dynamics simulation.

PEGylation is a successful approach to improve potency of a therapeutic protein. The improved therapeutic potency is mainly due to the steric shielding effect of PEG. However, the underlying mechanism of this effect on the protein is not well understood, especially on the protein interaction with its high molecular weight substrate or receptor. Here, experimental study and molecular dynamics simulation were used to provide molecular insight into the interaction between the PEGylated protein and its receptor. Staphylokinase (Sak), a therapeutic protein for coronary thrombolysis, was used as a model protein. Four PEGylated Saks were prepared by site-specific conjugation of 5 kDa/20 kDa PEG to N-terminus and C-terminus of Sak, respectively. Experimental study suggests that the native conformation of Sak is essentially not altered by PEGylation. In contrast, the bioactivity, the hydrodynamic volume and the molecular symmetric shape of the PEGylated Sak are altered and dependent on the PEG chain length and the PEGylation site. Molecular modeling of the PEGylated Saks suggests that the PEG chain remains highly flexible and can form a distinctive hydrated layer, thereby resulting in the steric shielding effect of PEG. Docking analyses indicate that the binding affinity of Sak to its receptor is dependent on the PEG chain length and the PEGylation site. Computational simulation results explain experimental data well. Our present study clarifies molecular details of PEG chain on protein surface and may be essential to the rational design, fabrication and clinical application of PEGylated proteins.

Mining breast cancer genes with a network based noise-tolerant approach.

BACKGROUND: Mining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it's important to design methods that work robustly with respect to noise. RESULTS: Gene Ontology (GO) annotations were often utilized in cancer gene mining works. However, the vast majority of GO annotations were computationally derived, thus not completely accurate. A set of genes annotated with breast cancer enriched GO terms was adopted here as a set of source data with realistic noise. A novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human Protein-Protein Interaction (PPI) network. Performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. Results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. When noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did. CONCLUSIONS: A novel noise tolerant method was proposed to mine breast cancer genes. Compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. To the best of our knowledge, it's the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. The sorted gene list can be valuable for breast cancer research. The proposed quantitative noise analysis method may also prove useful for other data integration efforts. It is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes.

Phenyl linker-induced dense PEG conformation improves the efficacy of C-terminally monoPEGylated staphylokinase.

PEGylation can improve the protein efficacy by prolonging serum half-life and reducing proteolytic sensitivity and immunogenicity. However, PEGylation may decrease the bioactivity of a protein by interfering with binding of its substrate or receptors. Here, staphylokinase (SAK), a thrombolysis agent for therapy of myocardial infarction, was mono-PEGylated at the C-terminus of SAK far from its bioactive domain. Phenyl, propyl, and amyl moieties were used as linkers between SAK and polyethylene glycol (PEG), respectively. Flexible propyl and amyl linkers lead to loose conformation. In contrast, rigid and hydrophobic phenyl linker induces dense PEG conformation that can extensively shield most domains adjacent to C-terminus (e.g., the antigen epitopes and proteolytic sites) of SAK and inefficiently shield its bioactive domain. As compared with loose PEG conformation, dense PEG conformation is more efficient to maintain the bioactivity, increase the plasma half-life, and decrease the proteolytic sensitivity and immunogenicity of the PEGylated SAK.

Assigning confidence scores to protein-protein interactions.

Screens for protein-protein interactions using assays like the yeast two-hybrid system have generated volumes of useful data. The protein interactions from these screens have been used to develop a better understanding of the functions of individual proteins, regulatory pathways, molecular machines, and entire biological systems. The value of this data, however, is limited by the inherent frequency of false positives that arise in most protein interaction screens. Appreciable numbers of false positives can crop up in both low-throughput and high-throughput screens, and even in screens that employ stringent criteria for defining a positive. A number of classification systems have been used to help distinguish false positives from biologically relevant true positives. This chapter describes a system for assigning a confidence score to each interaction based on the probability that it is a true positive. Such confidence scores can be used to prioritize interactions for validation. The scores are also useful for network analysis methods that take advantage of probabilistic edge weights. The scoring method does not rely on gold standard datasets of reliable true positives and true negatives, and thus circumvents the challenges associated with obtaining such datasets. Moreover, the scoring method uses data features that are largely assay-independent, making it useful for interactions obtained from a variety of different technologies and screening methods.

A protein network-guided screen for cell cycle regulators in Drosophila.

BACKGROUND: Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. RESULTS: We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3) complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. CONCLUSIONS: Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results also show that protein network data can be used to minimize false negatives and false positives and to more efficiently identify comprehensive sets of regulators for a process. Finally, our data provides a high confidence set of genes that are likely to play key roles in regulating the cell cycle or cell survival.

DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila.

DroID (http://droidb.org/), the Drosophila Interactions Database, is a comprehensive public resource for Drosophila gene and protein interactions. DroID contains genetic interactions and experimentally detected protein-protein interactions curated from the literature and from external databases, and predicted protein interactions based on experiments in other species. Protein interactions are annotated with experimental details and periodically updated confidence scores. Data in DroID is accessible through user-friendly, intuitive interfaces that allow simple or advanced searches and graphical visualization of interaction networks. DroID has been expanded to include interaction types that enable more complete analyses of the genetic networks that underlie biological processes. In addition to protein-protein and genetic interactions, the database now includes transcription factor-gene and regulatory RNA-gene interactions. In addition, DroID now has more gene expression data that can be used to search and filter interaction networks. Orthologous gene mappings of Drosophila genes to other organisms are also available to facilitate finding interactions based on gene names and identifiers for a number of common model organisms and humans. Improvements have been made to the web and graphical interfaces to help biologists gain a comprehensive view of the interaction networks relevant to the genes and systems that they study.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

MOTIVATION: High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space. RESULTS: We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

Cost-effective strategies for completing the interactome.

Comprehensive protein-interaction mapping projects are underway for many model species and humans. A key step in these projects is estimating the time, cost and personnel required for obtaining an accurate and complete map. Here we modeled the cost of interaction-map completion for various experimental designs. We showed that current efforts may require up to 20 independent tests covering each protein pair to approach completion. We explored designs for reducing this cost substantially, including prioritization of protein pairs, probability thresholding and interaction prediction. The best experimental designs lowered cost by fourfold overall and >100-fold in early stages of mapping. We demonstrate the best strategy in an ongoing project in Drosophila melanogaster, in which we mapped 450 high-confidence interactions using 47 microtiter plates, versus thousands of plates expected using current designs. This study provides a framework for assessing the feasibility of interaction mapping projects and for future efforts to increase their efficiency.

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions.

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 interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link. CONCLUSION: DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at http://www.droidb.org.

A proteome-wide protein interaction map for Campylobacter jejuni.

BACKGROUND: Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide. RESULTS: Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms. CONCLUSION: The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.

Computational approaches for predicting protein-protein interactions: a survey.

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.