Integrating experimental and literature protein-protein interaction data for protein complex prediction

Background: Accurate determination of protein complexes is crucial for understanding cellular organization and function. High-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data, allowing prediction of protein complexes from PPI networks. However, the high-throughput data often includes false positives and false negatives, making accurate prediction of protein complexes difficult. Method: The biomedical literature contains large quantities of PPI data that, along with high-throughput experimental PPI data, are valuable for protein complex prediction. In this study, we employ a natural language processing technique to extract PPI data from the biomedical literature. This data is subsequently integrated with high-throughput PPI and gene ontology data by constructing attributed PPI networks, and a novel method for predicting protein complexes from the attributed PPI networks is proposed. This method allows calculation of the relative contribution of high-throughput and biomedical literature PPI data. Results: Many well-characterized protein complexes are accurately predicted by this method when apply to two different yeast PPI datasets. The results show that (i) biomedical literature PPI data can effectively improve the performance of protein complex prediction; (ii) our method makes good use of high-throughput and biomedical literature PPI data along with gene ontology data to achieve state-of-the-art protein complex prediction capabilities.