Reconstructing genome-wide protein-protein interaction networks using multiple strategies with homologous mapping

Yu-Shu Lo; Sing-Han Huang; Yong-Chun Luo; Chun-Yu Lin; Jinn-Moon Yang

doi:10.1371/journal.pone.0116347

Reconstructing genome-wide protein-protein interaction networks using multiple strategies with homologous mapping

PLoS One. 2015 Jan 20;10(1):e0116347. doi: 10.1371/journal.pone.0116347. eCollection 2015.

Authors

Yu-Shu Lo¹, Sing-Han Huang¹, Yong-Chun Luo¹, Chun-Yu Lin¹, Jinn-Moon Yang²

Affiliations

¹ Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.
² Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan; Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, Taiwan.

Abstract

Background: One 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.

Results: Here, 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.

Conclusions: Experimental 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Drosophila
Humans
Models, Theoretical
Protein Interaction Mapping*
Protein Interaction Maps / genetics
Protein Interaction Maps / physiology*
Software

Grants and funding

This paper was supported by National Science Council, partial supports of Ministry of Education and National Health Research Institutes (NHRI-EX100-10009PI). This paper is also particularly supported by “Center for Bioinformatics Research of Aiming for the Top University Program” of the National Chiao Tung University and Ministry of Education, Taiwan. The authors also thank Core Facility for Protein Structural Analysis supported by National Core Facility Program for Biotechnology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.