Multi-stage framework to infer protein functional modules from mass spectrometry pull-down data with assessment of biological relevance

Protein functional modules are fundamental units in protein interaction networks. High-throughput Mass Spectrometry (MS) technology has become valuable for discovery of protein functional modules. Yet, their computational inference from MS pull-down data and biological significance evaluation are still challenging. This paper introduces an integrated multi-step framework for (1) assessing protein-protein interaction affinities, (2) constructing a genome-wide protein association map, (3) finding putative protein functional modules, and (4) evaluating their biological relevance. The protein affinity score utilizes copurification pattern of two proteins and adopts an information theoretic-approach to build the protein affinity map. Putative protein modules are then derived using a graph-theoretical approach. A two-stage statistical procedure assesses biological relevance of identified modules. On Saccharomyces cerevisiae's pull-down data (Nature, vol. 415, pp. 141-7, 2002), the scoring scheme outperformed other methods by at least 10% in F₁-measure, and statistical tests identified 489 protein modules enriched in all of three general GO categories with p-values less than 0.05.