UniHI 4: new tools for query, analysis and visualization of the human protein–protein interactome

Human protein interaction maps have become important tools of biomedical research for the elucidation of molecular mechanisms and the identification of new modulators of disease processes. The Unified Human Interactome database (UniHI, http://www.unihi.org) provides researchers with a comprehensive platform to query and access human protein–protein interaction (PPI) data. Since its first release, UniHI has considerably increased in size. The latest update of UniHI includes over 250 000 interactions between ∼22 300 unique proteins collected from 14 major PPI sources. However, this wealth of data also poses new challenges for researchers due to the complexity of interaction networks retrieved from the database. We therefore developed several new tools to query, analyze and visualize human PPI networks. Most importantly, UniHI allows now the construction of tissue-specific interaction networks and focused querying of canonical pathways. This will enable researchers to target their analysis and to prioritize candidate proteins for follow-up studies

UniHI: an entry gate to the human protein interactome

Systematic mapping of protein–protein interactions has become a central task of functional genomics. To map the human interactome, several strategies have recently been pursued. The generated interaction datasets are valuable resources for scientists in biology and medicine. However, comparison reveals limited overlap between different interaction networks. This divergence obstructs usability, as researchers have to interrogate numerous heterogeneous datasets to identify potential interaction partners for proteins of interest. To facilitate direct access through a single entry gate, we have started to integrate currently available human protein interaction data in an easily accessible online database. It is called UniHI (Unified Human Interactome) and is available at . At present, it is based on 10 major interaction maps derived by computational and experimental methods. It includes more than 150 000 distinct interactions between more than 17 000 unique human proteins. UniHI provides researchers with a flexible integrated tool for finding and using comprehensive information about the human interactome

Flexible web-based integration of distributed large-scale human protein interaction maps

Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data

FunSimMat: a comprehensive functional similarity database

Functional similarity based on Gene Ontology (GO) annotation is used in diverse applications like gene clustering, gene expression data analysis, protein interaction prediction and evaluation. However, there exists no comprehensive resource of functional similarity values although such a database would facilitate the use of functional similarity measures in different applications. Here, we describe FunSimMat (Functional Similarity Matrix, http://funsimmat.bioinf.mpi-inf.mpg.de/), a large new database that provides several different semantic similarity measures for GO terms. It offers various precomputed functional similarity values for proteins contained in UniProtKB and for protein families in Pfam and SMART. The web interface allows users to efficiently perform both semantic similarity searches with GO terms and functional similarity searches with proteins or protein families. All results can be downloaded in tab-delimited files for use with other tools. An additional XML–RPC interface gives automatic online access to FunSimMat for programs and remote services

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells

Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks

IntPred: a structure-based predictor of protein–protein interaction sites

Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein– protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/. Supplementary information Supplementary data are available at Bioinformatics online

Prioritization of candidate cancer genes—an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies

A Two-Hybrid Assay to Study Protein Interactions within the Secretory Pathway

Interactions of transcriptional activators are difficult to study using transcription-based two-hybrid assays due to potent activation resulting in false positives. Here we report the development of the Golgi two-hybrid (G2H), a method that interrogates protein interactions within the Golgi, where transcriptional activators can be assayed with negligible background. The G2H relies on cell surface glycosylation to report extracellularly on protein-protein interactions occurring within the secretory pathway. In the G2H, protein pairs are fused to modular domains of the reporter glycosyltransferase, Och1p, and proper cell wall formation due to Och1p activity is observed only when a pair of proteins interacts. Cells containing interacting protein pairs are identified by selectable phenotypes associated with Och1p activity and proper cell wall formation: cells that have interacting proteins grow under selective conditions and display weak wheat germ agglutinin (WGA) binding by flow cytometry, whereas cells that lack interacting proteins display stunted growth and strong WGA binding. Using this assay, we detected the interaction between transcription factor MyoD and its binding partner Id2. Interfering mutations along the MyoD:Id2 interaction interface ablated signal in the G2H assay. Furthermore, we used the G2H to detect interactions of the activation domain of Gal4p with a variety of binding partners. Finally, selective conditions were used to enrich for cells encoding interacting partners. The G2H detects protein-protein interactions that cannot be identified via traditional two-hybrid methods and should be broadly useful for probing previously inaccessible subsets of the interactome, including transcriptional activators and proteins that traffic through the secretory pathway

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength