A Linear-Time Algorithm for Predicting Functional Annotations from PPI Networks

Recent proteome-wide screening efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the systems biology community. Here, we address the problem of predicting the functional classes of proteins (i.e. GO annotations) based solely on the structure of the PPI network. We present a maximum likelihood formulation of the problem and the corresponding learning and inference algorithms. The time complexity of both algorithms is linear in the size of the PPI network, and our experimental results show that their accuracy in functional prediction outperforms current existing methods.

[1]  Limsoon Wong,et al.  Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions , 2006, BioDM.

[2]  Nir Friedman,et al.  Towards an Integrated Protein-Protein Interaction Network , 2005, RECOMB.

[3]  B. Schwikowski,et al.  A network of protein–protein interactions in yeast , 2000, Nature Biotechnology.

[4]  Serafim Batzoglou,et al.  Integrated Protein Interaction Networks for 11 Microbes , 2006, RECOMB.

[5]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[6]  Stanley Letovsky,et al.  Predicting protein function from protein/protein interaction data: a probabilistic approach , 2003, ISMB.

[7]  Mona Singh,et al.  Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps , 2005, ISMB.

[8]  S. L. Wong,et al.  A Map of the Interactome Network of the Metazoan C. elegans , 2004, Science.

[9]  Ting Chen,et al.  Mapping gene ontology to proteins based on protein-protein interaction data , 2004, Bioinform..

[10]  Alessandro Vespignani,et al.  Global protein function prediction from protein-protein interaction networks , 2003, Nature Biotechnology.

[11]  Ting Chen,et al.  Assessment of the reliability of protein-protein interactions and protein function prediction , 2002, Pacific Symposium on Biocomputing.

[12]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[13]  Adam J. Smith,et al.  The Database of Interacting Proteins: 2004 update , 2004, Nucleic Acids Res..

[14]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[15]  T. Takagi,et al.  Assessment of prediction accuracy of protein function from protein–protein interaction data , 2001, Yeast.

[16]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[17]  J. Wojcik,et al.  The protein–protein interaction map of Helicobacter pylori , 2001, Nature.

[18]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[19]  James R. Knight,et al.  A Protein Interaction Map of Drosophila melanogaster , 2003, Science.

[20]  R. Chanet,et al.  Protein interaction mapping: a Drosophila case study. , 2005, Genome research.

[21]  Ting Chen,et al.  An Integrated Probabilistic Model for Functional Prediction of Proteins , 2004, J. Comput. Biol..

[22]  S. Kasif,et al.  Whole-genome annotation by using evidence integration in functional-linkage networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Q Yang,et al.  Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet. , 2006, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.