Computational Prediction of Protein–Protein Interaction Networks: Algorithms and Resources

Abstract

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.

Efficient Regular Path Query Evaluation by Splitting with Unit-Subquery Cost Matrix

“…The graph is a network of protein-protein interactions which is used regularly in systems biology, for instance, to discover of protein functions and pathways in biological processes [6]. This graph has 52.050 nodes, 340.775 edges, and 649 labels.…”

Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale

“…Many types of information, such as phylogenetic profiling, gene fusion, co-expression, conservation of gene neighborhood, and gene ontology, can be used individually or together to predict protein-protein interactions [24][25][26][27][28][29]. Rhodes et al used a naive Bayes model that combines information for ortholog interaction, co-expression, shared gene ontology terms, and enriched domaindomain interaction pairs to predict novel interactions in human [30].…”

“…Some major repositories, and their characteristics, are listed in Table 1. A more detailed description of selected repositories has been provided by Zahiri et al [28 ].…”

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“…To overcome these limitations, bioinformatics methods are expected to be useful for identifying PPIs in functional proteomics. Various computational methods have been proposed to predict PPIs, which include genomic context-based methods [13][14][15][16][17], structure-based methods [18][19][20][21], and sequence-based methods [22][23][24][25][26][27][28][29][30][31][32]. Genomic context-based methods such as gene-cluster and gene-neighbor methods are based on the search of pairs of genes that show a correlated position or behavior; these genes are assumed to encode proteins that interact with each other [13].…”