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 co- purification 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 F
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-measure, and statistical tests identified 489 protein modules enriched in all of three general GO categories with p-values less than 0.05.