Protein Interactions from Complexes: A Structural Perspective

By combining crystallographic information with protein-interaction data obtained through traditional experimental means, this paper determines the most appropriate method for generating protein-interaction networks that incorporate data derived from protein complexes. We propose that a combined method should be considered; in which complexes composed of five chains or less are decomposed using the matrix model, whereas the spoke model is used to derive pairwise interactions for those with six chains or more. The results presented here should improve the accuracy and relevance of studies investigating the topology of protein-interaction networks

Identification of protein complexes from co-immunoprecipitation data

Motivation: Advanced technologies are producing large-scale protein–protein interaction data at an ever increasing pace. A fundamental challenge in analyzing these data is the inference of protein machineries. Previous methods for detecting protein complexes have been mainly based on analyzing binary protein–protein interaction data, ignoring the more involved co-complex relations obtained from co-immunoprecipitation experiments

Identifying the topology of protein complexes from affinity purification assays

Motivation: Recent advances in high-throughput technologies have made it possible to investigate not only individual protein interactions, but also the association of these proteins in complexes. So far the focus has been on the prediction of complexes as sets of proteins from the experimental results. The modular substructure and the physical interactions within the protein complexes have been mostly ignored

Making the most of high-throughput protein-interaction data

Better methods of statistical analysis could make large-scale protein-interaction data more useful

Modeling synthetic lethality

Using new computational tools in yeast, multi-protein complexes were identified that share an unusually high number of synthetic genetic interactions

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

<p>Abstract</p> <p>Background</p> <p>In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.</p> <p>Results</p> <p>The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.</p> <p>Conclusions</p> <p>The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.</p

Global Functional Analyses of Cellular Responses to Pore-Forming Toxins

Here we present the first global functional analysis of cellular responses to pore-forming toxins (PFTs). PFTs are uniquely important bacterial virulence factors, comprising the single largest class of bacterial protein toxins and being important for the pathogenesis in humans of many Gram positive and Gram negative bacteria. Their mode of action is deceptively simple, poking holes in the plasma membrane of cells. The scattered studies to date of PFT-host cell interactions indicate a handful of genes are involved in cellular defenses to PFTs. How many genes are involved in cellular defenses against PFTs and how cellular defenses are coordinated are unknown. To address these questions, we performed the first genome-wide RNA interference (RNAi) screen for genes that, when knocked down, result in hypersensitivity to a PFT. This screen identifies 106 genes (∼0.5% of genome) in seven functional groups that protect Caenorhabditis elegans from PFT attack. Interactome analyses of these 106 genes suggest that two previously identified mitogen-activated protein kinase (MAPK) pathways, one (p38) studied in detail and the other (JNK) not, form a core PFT defense network. Additional microarray, real-time PCR, and functional studies reveal that the JNK MAPK pathway, but not the p38 MAPK pathway, is a key central regulator of PFT-induced transcriptional and functional responses. We find C. elegans activator protein 1 (AP-1; c-jun, c-fos) is a downstream target of the JNK-mediated PFT protection pathway, protects C. elegans against both small-pore and large-pore PFTs and protects human cells against a large-pore PFT. This in vivo RNAi genomic study of PFT responses proves that cellular commitment to PFT defenses is enormous, demonstrates the JNK MAPK pathway as a key regulator of transcriptionally-induced PFT defenses, and identifies AP-1 as the first cellular component broadly important for defense against large- and small-pore PFTs