Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

Abstract

BackgroundWe can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.ResultsWe present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.ConclusionsWe have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.Executable files and examples are given at: http://www.bhsai.org/downloads/padds/

Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

“…coli dataset. It was already shown by Memisevic et al and Roy et al that DDA and AAC were powerful features for predicting PPIs [ 19 ],[ 20 ]. Furthermore, we performed 10-fold cross-validation on best subset of features (DDA, Degree and AAC) and compared the result with 5-fold cross-validation.…”

“…It was shown previously that domain-domain association plays an important role in protein-protein interactions [ 19 ]. Considering that we had used domain-domain association as the first feature, Maximum Degree (maximum no.…”

Prediction of Interactions between Viral and Host Proteins Using Supervised Machine Learning Methods

“…We focused on forty-four features of protein pairs to produce feature vectors ( Table S6 ). First, occurrence frequency of viral-host domain-domain association was used since domain-domain association plays an important role in protein-protein interactions [11] . Second, common domains observed in virus and host proteins were chosen and represented as binary format [0,1] (absence and presence of common domain observed in virus and host proteins in a particular protein pair represented by 0 and 1, respectively).…”

“…Jansen et al has developed a Bayesian networks approach to predict PPIs in yeast [4] , while Lin et al shows that Random Forest (RF) model may be more effective than Bayesian networks for predicting PPIs [5] . In addition, a number of computational methods are also available in order to predict PPIs based on domain information [9] – [11] . However, relatively few methods have so far been proposed to predict interspecies (specifically host-pathogen) PPIs [3] , [12] – [16] .…”

UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks

“…Recently, many studies have been implemented to identify protein domains and also produced a great volume of protein domain data [31], [32], [33], which facilitates our study of cellular life at the atomic level. For example, under the assumption that protein interactions are mediated by domain interactions, some researchers focus on predicting protein-protein interactions by making use of domain data [34], [35]. A group of researchers integrates protein domain information into PPI network and constructs structural network, based on which a series of research works are carried out, such as analyzing human disease [36], [37], explaining the relationship between hub proteins and their essentiality [38], constructing signal pathway [39] and so on.…”