Improvement of protein quantification for proteins with shared peptides by using bipartite peptide-protein graphs

Abstract

In bottom-up proteomics, proteins are enzymatically digested to peptides, smaller amino acid chains, which are then measured via mass spectrometry. The received peptide quantities need to be summarized to protein quantities to obtain biological insights (protein quantification). This is complicated by the presence of shared peptides that occur in multiple protein sequences. The relationship between peptides and corresponding proteins can be represented as bipartite graphs. A novel protein quantification method, called bppgQuant, is proposed which calculates protein ratios from peptide ratios. It uses the structures of the bipartite peptide-protein graphs to build an equation system. As this system is not exactly solvable in many cases, an optimization problem is formulated to find solutions which minimize the sum of squared error terms. bppgQuant is evaluated and compared to the methods SCAMPI and PQP using four different quantitative datasets from different organisms, that contain known protein ratios. In summary, bppgQuant showed good results in comparison to SCAMPI and PQP, especially when protein nodes not needed to explain the measured peptide ratios were removed before optimization.