Interactome data and databases: different types of protein interaction

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of 'protein-protein interactions', i.e. the 'interactome'. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of 'what a protein-protein interaction is' and 'which types of protein-protein interactions are found in a living cell'. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.     

Glycosaminoglycan interaction networks and databases

Glycosaminoglycans are complex polysaccharides exhibiting a large structural and conformational diversity. These key biological players organize the extracellular matrix, contribute to cell–matrix interactions, and regulate cell signaling. Natural and synthetic libraries of glycosaminoglycans have been spotted on microarrays to find glycosaminoglycan partners and determine the size and the chemical groups promoting protein binding. Advances in glycosaminoglycan sequencing allow the characterization of glycosaminoglycan sequences interacting with proteins, and glycosaminoglycan-mediated pull-down proteomics can identify glycosaminoglycan-binding proteins at a proteome scale in various biological samples. The analysis of the glycosaminoglycan interaction networks generated using these data gives insights into the molecular and cellular mechanisms underlying glycosaminoglycan functions. These interactomes can also be used to design inhibitors targeting specific GAG interactions for therapeutic purpose.     

hmChIP: a database and web server for exploring publicly available human and mouse ChIP-seq and ChIP-chip data

hmChIP is a database of genome-wide chromatin immunoprecipitation (ChIP) data in human and mouse. Currently, the database contains 2016 samples from 492 ChIP-seq and ChIP-chip experiments, representing a total of 170 proteins and 11 069 914 protein-DNA interactions. A web server provides interface for database query. Protein-DNA binding intensities can be retrieved from individual samples for user-provided genomic regions. The retrieved intensities can be used to cluster samples and genomic regions to facilitate exploration of combinatorial patterns, cell-type dependencies, and cross-sample variability of protein-DNA interactions. AVAILABILITY: http://jilab.biostat.jhsph.edu/database/cgi-bin/hmChIP.pl.     

Tissue-specific splicing of disordered segments that embed binding motifs rewires protein interaction networks

Alternative inclusion of exons increases the functional diversity of proteins. Among alternatively spliced exons, tissue-specific exons play a critical role in maintaining tissue identity. This raises the question of how tissue-specific protein-coding exons influence protein function. Here we investigate the structural, functional, interaction, and evolutionary properties of constitutive, tissue-specific, and other alternative exons in human. We find that tissue-specific protein segments often contain disordered regions, are enriched in posttranslational modification sites, and frequently embed conserved binding motifs. Furthermore, genes containing tissue-specific exons tend to occupy central positions in interaction networks and display distinct interaction partners in the respective tissues, and are enriched in signaling, development, and disease genes. Based on these findings, we propose that tissue-specific inclusion of disordered segments that contain binding motifs rewires interaction networks and signaling pathways. In this way, tissue-specific splicing may contribute to functional versatility of proteins and increases the diversity of interaction networks across tissues.     

A meta-analysis of the publicly available bacterial and archaeal sequence diversity in saline soils

An integrated view of bacterial and archaeal diversity in saline soil habitats is essential for understanding the biological and ecological processes and exploiting potential of microbial resources from such environments. This study examined the collective bacterial and archaeal diversity in saline soils using a meta-analysis approach. All available 16S rDNA sequences recovered from saline soils were retrieved from publicly available databases and subjected to phylogenetic and statistical analyses. A total of 9,043 bacterial and 1,039 archaeal sequences, each longer than 250 bp, were examined. The bacterial sequences were assigned into 5,784 operational taxonomic units (OTUs, based on ≥97 % sequence identity), representing 24 known bacterial phyla, with Proteobacteria (44.9 %), Actinobacteria (12.3 %), Firmicutes (10.4 %), Acidobacteria (9.0 %), Bacteroidetes (6.8 %), and Chloroflexi (5.9 %) being predominant. Lysobacter (12.8 %) was the dominant bacterial genus in saline soils, followed by Sphingomonas (4.5 %), Halomonas (2.5 %), and Gemmatimonas (2.5 %). Archaeal sequences were assigned to 602 OTUs, primarily from the phyla Euryarchaeota (88.7 %) and Crenarchaeota (11.3 %). Halorubrum and Thermofilum were the dominant archaeal genera in saline soils. Rarefaction analysis indicated that less than 25 % of bacterial diversity, and approximately 50 % of archaeal diversity, in saline soil habitats has been sampled. This analysis of the global bacterial and archaeal diversity in saline soil habitats can guide future studies to further examine the microbial diversity of saline soils.     

Current status of publicly available sarcoma cell lines for use in proteomic studies

Cell lines are valuable resources for proteomic studies and can be used as tools to verify the significance of proteomic findings. Here, the authors overview the current status of the publicly available sarcoma cell lines. The authors surveyed seven major cell banks and found that the diversity observed in the sarcoma cell banks was largely insufficient; sarcoma cell lines are available for only a limited histological subtype. They also observed a number of issues with the pathological diagnosis of the cell lines, limitations in their behavioral diversity, and various unmet needs. Well characterized cell lines with accurate diagnosis based on modern diagnosis criteria should be available from public cell banks. The authors conclude that additional cell lines, along with detailed genetic and pathological analyses, should be prepared and deposited in order to promote sarcoma-specific proteomic research. The authors focused on sarcoma cell lines, but their discussion can be applied to the other cancers.     

Exploiting publicly available biological and biochemical information for the discovery of novel short linear motifs

The function of proteins is often mediated by short linear segments of their amino acid sequence, called Short Linear Motifs or SLiMs, the identification of which can provide important information about a protein function. However, the short length of the motifs and their variable degree of conservation makes their identification hard since it is difficult to correctly estimate the statistical significance of their occurrence. Consequently, only a small fraction of them have been discovered so far. We describe here an approach for the discovery of SLiMs based on their occurrence in evolutionarily unrelated proteins belonging to the same biological, signalling or metabolic pathway and give specific examples of its effectiveness in both rediscovering known motifs and in discovering novel ones. An automatic implementation of the procedure, available for download, allows significant motifs to be identified, automatically annotated with functional, evolutionary and structural information and organized in a database that can be inspected and queried. An instance of the database populated with pre-computed data on seven organisms is accessible through a publicly available server and we believe it constitutes by itself a useful resource for the life sciences (http://www.biocomputing.it/modipath).     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Tissue specificity and the human protein interaction network

A protein interaction network describes a set of physical associations that can occur between proteins. However, within any particular cell or tissue only a subset of proteins is expressed and so only a subset of interactions can occur. Integrating interaction and expression data, we analyze here this interplay between protein expression and physical interactions in humans. Proteins only expressed in restricted cell types, like recently evolved proteins, make few physical interactions. Most tissue‐specific proteins do, however, bind to universally expressed proteins, and so can function by recruiting or modifying core cellular processes. Conversely, most ‘housekeeping’ proteins that are expressed in all cells also make highly tissue‐specific protein interactions. These results suggest a model for the evolution of tissue‐specific biology, and show that most, and possibly all, ‘housekeeping’ proteins actually have important tissue‐specific molecular interactions.     

ATID: a web-oriented database for collection of publicly available alternative translational initiation events

SUMMARY: Alternative translational initiation is an important cellular mechanism contributing to the diversity of protein products and functions. We develop a database that provides a comprehensive collection of alternative translational initiation events. The purpose of this alternative translational initiation database (ATID) is to facilitate the systematic study of alternative translational initiation of genes. The current version of database contains 300 genes from Homo sapiens, Mus musculus and other species. Each of the genes has two or more isoforms due to alternative translational initiation. Resources in ATID, including gene information, alternative products of genes and domain structures of isoforms, are provided through a user-friendly web interface. AVAILABILITY: The ATID database is available for public use at http://bioinfo.au.tsinghua.edu.cn/atie/.     

Nucleic acid and protein sequence databases

Nucleic acid and protein sequences contain a wealth of informationof interest to molecular biologists. The advent of molecularsequence databases provides a unique opportunity for the computeranalysis of all available sequences. Sequence databases servetwo main functions: (i) to facilitate comparisons with newlydetermined sequences, and (ii) to act as a source of data forthe generation and testing of hypotheses concerning molecularsequence organisation and evolution. The large amounts of sequencedata now becoming available require that algorithms for databasesearching be fast and efficient and considerable progress isbeing made in this area.     

Confirmation of human protein interaction data by human expression data

Background  

With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.     

Functional interaction between the pp71 protein of human cytomegalovirus and the PML-interacting protein human Daxx

The tegument protein pp71 (UL82) of human cytomegalovirus (HCMV) has previously been shown to transactivate the major immediate-early enhancer-promoter of HCMV. Furthermore, this protein is able to enhance the infectivity of viral DNA and to accelerate the infection cycle, suggesting an important regulatory function during viral replication. To gain insight into the underlying mechanisms that are used by pp71 to exert these pleiotropic effects, we sought for cellular factors interacting with pp71 in a yeast two-hybrid screen. Here, we report the isolation of the human Daxx (hDaxx) protein as a specific interaction partner of HCMV pp71. hDaxx, which was initially described as an adapter protein involved in apoptosis regulation, has recently been identified as a nuclear protein that interacts and colocalizes with PML in the nuclear domain ND10. In order to assess whether pp71 can also be detected in ND10 structures, a vector expressing pp71 in fusion with the green fluorescent protein was used for transfection of human fibroblasts. This revealed a colocalization of pp71 with the ND10 proteins PML and Sp100. In addition, cotransfection of a hDaxx expression vector resulted in an enhanced recruitment of pp71 to ND10. Targeting of pp71 to nuclear dots could also be observed in infected human fibroblasts in the absence of de novo viral protein synthesis. Moreover, cotransfection experiments revealed that pp71-mediated transactivation of the major immediate-early enhancer-promoter was synergistically enhanced in the presence of hDaxx. These results suggest an important role of hDaxx for pp71 protein function.