Domain-Domain Interactions Underlying Herpesvirus-Human Protein-Protein Interaction Networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host

FunSimMat update: new features for exploring functional similarity

Quantifying the functional similarity of genes and their products based on Gene Ontology annotation is an important tool for diverse applications like the analysis of gene expression data, the prediction and validation of protein functions and interactions, and the prioritization of disease genes. The Functional Similarity Matrix (FunSimMat, http://www.funsimmat.de) is a comprehensive database providing various precomputed functional similarity values for proteins in UniProtKB and for protein families in Pfam and SMART. With this update, we significantly increase the coverage of FunSimMat by adding data from the Gene Ontology Annotation project as well as new functional similarity measures. The applicability of the database is greatly extended by the implementation of a new Gene Ontology-based method for disease gene prioritization. Two new visualization tools allow an interactive analysis of the functional relationships between proteins or protein families. This is enhanced further by the introduction of an automatically derived hierarchy of annotation classes. Additional changes include a revised user front-end and a new RESTlike interface for improving the user-friendliness and online accessibility of FunSimMat

The Lymantria dispar IPLB-Ld652Y Cell Line Transcriptome Comprises Diverse Virus-Associated Transcripts

The enhanced viral susceptibility of the gypsy moth (Lymantria dispar)-derived IPLB-Ld652Y cell line has made it a popular in vitro system for studying virus-related phenomena in the Lepidoptera. Using both single-pass EST sequencing and 454-based pyrosequencing, a transcriptomic library of 14,368 putatively unique transcripts (PUTs) was produced comprising 8,476,050 high-quality, informative bases. The gene content of the IPLB-Ld652Y transcriptome was broadly assessed via comparison with the NCBI non-redundant protein database, and more detailed functional annotation was inferred by comparison to the Swiss-Prot subset of UniProtKB. In addition to L. dispar cellular transcripts, a diverse array of both RNA and DNA virus-associated transcripts was identified within the dataset, suggestive of a high level of viral expression and activity in IPLB-Ld652Y cells. These sequence resources will provide a sound basis for developing testable experimental hypotheses by insect virologists, and suggest a number of avenues for potential research

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

DASMI: exchanging, annotating and assessing molecular interaction data

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet

Stability of domain structures in multi-domain proteins

Multi-domain proteins have many advantages with respect to stability and folding inside cells. Here we attempt to understand the intricate relationship between the domain-domain interactions and the stability of domains in isolation. We provide quantitative treatment and proof for prevailing intuitive ideas on the strategies employed by nature to stabilize otherwise unstable domains. We find that domains incapable of independent stability are stabilized by favourable interactions with tethered domains in the multi-domain context. Stability of such folds to exist independently is optimized by evolution. Specific residue mutations in the sites equivalent to inter-domain interface enhance the overall solvation, thereby stabilizing these domain folds independently. A few naturally occurring variants at these sites alter communication between domains and affect stability leading to disease manifestation. Our analysis provides safe guidelines for mutagenesis which have attractive applications in obtaining stable fragments and domain constructs essential for structural studies by crystallography and NMR

Protein Function Assignment through Mining Cross-Species Protein-Protein Interactions

Background: As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data. Methodology/Principal Findings: The proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42 % using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete. Conclusions/Significance: It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable t

The role of exon shuffling in shaping protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks.</p> <p>Results</p> <p>We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: <it>Homo sapiens</it>, <it>Mus musculus</it>, <it>Drosophila melanogaster</it>, <it>Caenorhabditis elegans</it>, <it>Cryptococcus neoformans</it> and <it>Arabidopsis thaliana</it>. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling.</p> <p>Conclusions</p> <p>Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.</p

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN