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 proteinprotein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of proteinprotein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance. Results: A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10 in F-1-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.