a protein domain co-occurrence network approach for predicting protein function and inferring species phylogeny一种蛋白质域同现网络方法预测蛋白质功能和推断物种发展史.pdfVIP

A Protein Domain Co-Occurrence Network Approach for Predicting Protein Function and Inferring Species Phylogeny 1 2,3 3 1,2,4 2,3 1,2,4 Zheng Wang , Xue-Cheng Zhang , Mi Ha Le , Dong Xu , Gary Stacey , Jianlin Cheng * 1 Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America, 2 Christopher S. Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America, 3 Division of Plant Science, University of Missouri, Columbia, Missouri, United States of America, 4 Informatics Institute, University of Missouri, Columbia, Missouri, United States of America Abstract 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, x2, 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 nei