From pull-down data to protein interaction networks and complexes with biological relevance

Motivation: Recent improvements in high-throughput Mass Spectrometry(MS) technology have expedited genome-wide discovery of protein–proteininteractions by providing a capability of detecting proteincomplexes in a physiological setting. Computational inferenceof protein interaction networks and protein complexes from MSdata are challenging. Advances are required in developing robustand seamlessly integrated procedures for assessment of protein–proteininteraction affinities, mathematical representation of proteininteraction networks, discovery of protein complexes and evaluationof their biological relevance. Results: A multi-step but easy-to-follow framework for identifyingprotein complexes from MS pull-down data is introduced. It assessesinteraction affinity between two proteins based on similarityof their co-purification patterns derived from MS data. It constructsa protein interaction network by adopting a knowledge-guidedthreshold selection method. Based on the network, it identifiesprotein complexes and infers their core components using a graph-theoreticalapproach. It deploys a statistical evaluation procedure to assessbiological relevance of each found complex. On Saccharomycescerevisiae pull-down data, the framework outperformed othermore complicated schemes by at least 10% in F₁-measure and identified610 protein complexes with high-functional homogeneity basedon the enrichment in Gene Ontology (GO) annotation. Manual examinationof the complexes brought forward the hypotheses on cause offalse identifications. Namely, co-purification of differentprotein complexes as mediated by a common non-protein molecule,such as DNA, might be a source of false positives. Protein identificationbias in pull-down technology, such as the hydrophilic bias couldresult in false negatives. Contact: samatovan{at}ornl.gov Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren Present address: Department of Biomedical Informatics, VanderbiltUniversity, Nashville, TN 37232. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors.