Reconstructing Genome-Wide Protein–Protein Interaction Networks Using Multiple Strategies with Homologous Mapping

Abstract

BackgroundOne of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein–protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.ResultsHere, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10−40), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.ConclusionsExperimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.

Membrane protein-regulated networks across human cancers

“…2d). We compared the PPI prediction accuracies of our S SIM , six individual or combined scoring methods 13,14 , the STRING database (v. 10.0) 15 , the FpClass method 16 , and the generalized interolog mapping method 17 (Supplementary Table 3 and Supplementary Note 1) using sets of positive and negative cases (see “Methods” and Supplementary Fig. 3).…”

“…2a–d) to calculate their interaction scores ( S SIM ). S SIM consists of the interacting region similarity ( S irs ), the quality of the PPI template ( S qul ), the normalized joint sequence similarity ( S jss ) 13,14 , the normalized ranking of joint sequence similarity ( S rank ) 14 , the evolutionary conserved score across multiple species ( S es ) 14 , and the network topology score in a human PPI network ( S topo ). S SIM is defined as…”

Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease

“…We do recognize the limitation of structure-based features, such as limited coverage. However, we believe that the limitations can be addressed via various computational prediction methods, such as structure prediction in the presence of mutation [ 39 ], reconstruction of protein-protein interactions [ 40 ], ligand binding site predictions [ 41 ], and stability predictions [ 42 ]. In the future, we would like to include more structure-based features utilizing computational methods such as binding site predictions, and stability predictions and also increase coverage by including structure prediction methods.…”

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

“…In order to transfer the known interactions to the Cp strains, we employed the so-called interolog mapping which was already successfully utilized in several other studies [48–50] and essentially corresponds to the “generalized interolog mapping” method as described in Yu et al [57]. …”

“…The interolog mapping method was already successfully applied in several other studies, for example to predict the interactions in Mycobacterium tuberculosis [48], Leishmania spp. [49], mouse [50] and Bacillus licheniformis [51]. …”