A protein domain co-occurrence network approach for predicting protein function and inferring species phylogeny

• Published in: PloS one Vol. 6; no. 3; p. e17906
• Main Authors: Wang, Zheng, Zhang, Xue-Cheng, Le, Mi Ha, Xu, Dong, Stacey, Gary, Cheng, Jianlin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.03.2011; Public Library of Science (PLoS)
• Subjects: Acids; Analysis; Animals; Arabidopsis; Bacteriology; Biological evolution; Biology; Bond, Christopher S; Caenorhabditis elegans - genetics; Computational Biology; Computer science; Drosophila melanogaster - genetics; Evolution; Genomes; Genomics; Humans; Informatics; Life sciences; Methods; Molecular biology; Ontology; Oryza; Phylogenetics; Phylogeny; Plant genetics; Plant sciences; Plants - genetics; Predictions; Protein Structure, Tertiary - genetics; Proteins; Proteins - genetics; Saccharomyces cerevisiae - genetics; Similarity; Social networks; Species; Taxa; Taxonomy; Detroit Michigan; United States > US; Missouri
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0017906

Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ(2), and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.