title: Identification of essential proteins based on ranking edge-weights in protein-protein interaction networks.
abstract: Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; Department of Information and Communication Technology, University of Trento, Povo, Italy; Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Sun
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.affiliation: Department of Information and Communication Technology, University of Trento, Povo, Italy.
authors.forename: Enrico
authors.initials: E
authors.lastname: Blanzieri
authors.affiliation: Institute of Biophysics, National Research Council, University of Trento, Povo, Italy.
authors.forename: Gabriella
authors.initials: G
authors.lastname: Viero
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
mesh_terms: D019295:Computational Biology; D004926:Escherichia coli / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000737:chemistry / Q000378:metabolism*; D008958:Models, Molecular; D060066:Protein Interaction Maps; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins