A domain-based approach to predict protein-protein interactions

Abstract

Background: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.

A Systemic Network for Chlamydia pneumoniae Entry into Human Cells

“…The network was analyzed by using Network Analyzer (http://med.bioinf.mpi-inf.mpg.de/netanalyzer/index.php). The activated networks were decomposed into functional modules based on topological interconnection intensity and gene function (http://www.geneontology.org/) (3,21,28,39,40). Genes were classified according to the gene ontology database (http://www.geneontology.org/) (21).…”

Predicting conformation of protein complexes by determining statistically significant domain-domain interactions

“…In the context of protein interaction, such domains and peptides act as recognition elements; we refer to these binding domains or recognized peptides simply as 'domains' in this study. Over the past few years with developments of highthroughput PPI detection technologies, many researchers have shown an interest in extracting domain-domain interactions (DDIs) from large-scale PPI data by statistical methods, demonstrating that the idea of DDIs explain the cause of PPIs in some measure (Sprinzak and Margalit 2001;Riley et al 2005;Singhal and Resat 2007;Liu et al 2009). Here, we statistically extracted DDIs from integrated PPI data of Arabidopsis by following a procedure described in the 'Method' section.…”

“…These studies have reported that the densely connected regions in a network correspond to known protein complexes or protein functional units. The other approach is the analysis of DDIs coming out from PPI data by statistics or machine learning intended to predict unknown PPIs (Sprinzak and Margalit 2001;Riley et al 2005;Singhal and Resat 2007;Liu et al 2009). These studies have shown that the concept of DDIs statistically extracted from large-scale PPI data can explain the makeup of PPIs to some extent.…”

Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm

“…Protein structural information and sequence conservation between interacting proteins have also been used (Aloy and Russell, 2003;Ogmen et al, 2005;Planas-Iglesias et al, 2013a,b). Some approaches based on previously predicted (known protein) domains that are responsible for the interactions have also been proposed (Kim et al, 2002;Han et al, 2004;Morrison et al, 2006;Singhal and Resat, 2007). However, all the above methods cannot be implemented if the basic information about the proteins (especially some structure information) is not available.…”