Proteome Science: Fully automated protein complex prediction based on topological similarity and community structure
Chengwei Lei, Saleh Tamim, Alexander JR Bishop & Jianhua Ruan
Abstract
To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, a high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.
In addressing and solving these problems we present a novel random walk-based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.
Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interaction pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measures compared to the other protein complex predictions.
Since 2004, UT Health San Antonio, Greehey Children’s Cancer Research Institute’s (Greehey CCRI) mission has been to advance scientific knowledge relevant to childhood cancer, contribute to understanding its causes, and accelerate the translation of knowledge into novel therapies. Greehey CCRI strives to have a national and global impact on childhood cancer by discovering, developing, and disseminating new scientific knowledge. Our mission consists of three key areas — research, clinical, and education.
Stay connected with the Greehey CCRI on Facebook, Twitter, LinkedIn, and Instagram.