On the use of resampling tests for evaluating statistical significance of binding-site co-occurrence

Background  

In eukaryotes, most DNA-binding proteins exert their action as members of large effector complexes. The presence of these complexes are revealed in high-throughput genome-wide assays by the co-occurrence of the binding sites of different complex components. Resampling tests are one route by which the statistical significance of apparent co-occurrence can be assessed.     

Protein complex prediction based on <Emphasis Type="Italic">k</Emphasis>-connected subgraphs in protein interaction network

Background  

Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.     

Identifying protein complexes directly from high-throughput TAP data with Markov random fields

Background  

Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.     

A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks

Background

The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.

Results

In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.

Conclusions

In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.

     

A core-attachment based method to detect protein complexes in PPI networks

Background  

How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.     

Dynamic assembly,localization and proteolysis of the <Emphasis Type="Italic">Bacillus subtilis</Emphasis> SMC complex

Background  

SMC proteins are key components of several protein complexes that perform vital tasks in different chromosome dynamics. Bacterial SMC forms a complex with ScpA and ScpB that is essential for chromosome arrangement and segregation. The complex localizes to discrete centres on the nucleoids that during most of the time of the cell cycle localize in a bipolar manner. The complex binds to DNA and condenses DNA in an as yet unknown manner.     

Nuclear and plastid haplotypes suggest rapid diploid and polyploid speciation in the N Hemisphere <Emphasis Type="BoldItalic">Achillea millefolium</Emphasis>complex (Asteraceae)

Background  

Species complexes or aggregates consist of a set of closely related species often of different ploidy levels, whose relationships are difficult to reconstruct. The N Hemisphere Achillea millefolium aggregate exhibits complex morphological and genetic variation and a broad ecological amplitude. To understand its evolutionary history, we study sequence variation at two nuclear genes and three plastid loci across the natural distribution of this species complex and compare the patterns of such variations to the species tree inferred earlier from AFLP data.     

A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

Background  

Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.     

Structural disorder promotes assembly of protein complexes

Background  

The idea that the assembly of protein complexes is linked with protein disorder has been inferred from a few large complexes, such as the viral capsid or bacterial flagellar system, only. The relationship, which suggests that larger complexes have more disorder, has never been systematically tested. The recent high-throughput analyses of protein-protein interactions and protein complexes in the cell generated data that enable to address this issue by bioinformatic means.     

Analysis of the role of PCNA-DNA contacts during clamp loading

Background  

Sliding clamps, such as Proliferating Cell Nuclear Antigen (PCNA) in eukaryotes, are ring-shaped protein complexes that encircle DNA and enable highly processive DNA replication by serving as docking sites for DNA polymerases. In an ATP-dependent reaction, clamp loader complexes, such as the Replication Factor-C (RFC) complex in eukaryotes, open the clamp and load it around primer-template DNA.     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

Biogeographical patterns of endemic diversity and its conservation in Australia's artesian desert springs

Aim

Springs in the Australian arid zone are distinct from other waterways because they house a large number of endemic species. We aimed to assess spatial patterns in endemic diversity at a basin‐wide scale and whether environmental features can help to explain them. In doing so, we take the opportunity to summarize the current state of conservation in the system.

Location

Great Artesian Basin (GAB), arid and semiarid regions of eastern Australia

Methods

We combine data regarding the location of springs with published GIS layers regarding environmental characteristics and a literature review of all species and subspecies documented in the published literature to be endemic to GAB springs.

Results

We found evidence of 96 species and subspecies of fishes, molluscs, crustaceans and plants endemic to these springs. The majority of endemic species are invertebrates with geographical distributions limited to a single spring complex (<61 km²). Endemic taxa are concentrated in 75 of the 326 spring complexes. Spring complexes with a large number of springs, high connectivity via drainage basins and low rainfall were more likely to contain endemic taxa, but environmental models were poor predictors of diversity. Only 24% spring complexes with high conservation value are within conservation reserves, and the majority of endemic species are unassessed under the IUCN and Australian conservation legislation, particularly the invertebrates.

Main conclusions

Diversity in this system is underestimated given the current rate of species discovery and prevailing data deficiency for many taxa. Historical processes and species‐specific environmental requirements may be more important for explaining why diversity is concentrated in particular complexes. Almost a decade after this system was listed as endangered, most complexes of high conservation value remain outside of conservation reserves, and the endangered species status of many taxa, and particularly the invertebrates, remain unassessed.

     

High throughput protein-protein interaction data: clues for the architecture of protein complexes

Background

High-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex.

Results

Two-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins.

Conclusion

The constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.     

Parallel evolution of histophagy in ciliates of the genus Tetrahymena

Background  

Species of Tetrahymena were grouped into three complexes based on morphological and life history traits: the pyriformis complex of microstomatous forms; the patula complex of microstome-macrostome transformers; and the rostrata complex of facultative and obligate histophages. We tested whether these three complexes are paraphyletic using the complete sequence of the small subunit rDNA (SSrDNA).     

Engineering the cell surface display of cohesins for assembly of cellulosome-inspired enzyme complexes on <Emphasis Type="Italic">Lactococcus lactis</Emphasis>

Background  

The assembly and spatial organization of enzymes in naturally occurring multi-protein complexes is of paramount importance for the efficient degradation of complex polymers and biosynthesis of valuable products. The degradation of cellulose into fermentable sugars by Clostridium thermocellum is achieved by means of a multi-protein "cellulosome" complex. Assembled via dockerin-cohesin interactions, the cellulosome is associated with the cell surface during cellulose hydrolysis, forming ternary cellulose-enzyme-microbe complexes for enhanced activity and synergy. The assembly of recombinant cell surface displayed cellulosome-inspired complexes in surrogate microbes is highly desirable. The model organism Lactococcus lactis is of particular interest as it has been metabolically engineered to produce a variety of commodity chemicals including lactic acid and bioactive compounds, and can efficiently secrete an array of recombinant proteins and enzymes of varying sizes.     

Identification of MarvelD3 as a tight junction-associated transmembrane protein of the occludin family

Background  

Tight junctions are an intercellular adhesion complex of epithelial and endothelial cells, and form a paracellular barrier that restricts the diffusion of solutes on the basis of size and charge. Tight junctions are formed by multiprotein complexes containing cytosolic and transmembrane proteins. How these components work together to form functional tight junctions is still not well understood and will require a complete understanding of the molecular composition of the junction.     

Web-based analysis of the mouse transcriptome using Genevestigator

Background  

Gene function analysis often requires a complex and laborious sequence of laboratory and computer-based experiments. Choosing an effective experimental design generally results from hypotheses derived from prior knowledge or experimentation. Knowledge obtained from meta-analyzing compendia of expression data with annotation libraries can provide significant clues in understanding gene and network function, resulting in better hypotheses that can be tested in the laboratory.