Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification

*Motivation:* In current proteome research, peptide sequencing is probably the most widely used method for protein mixture identification. However, this peptide-centric method has its own disadvantages such as the immense volume of tandem Mass Spectrometry (MS) data for sequencing peptides. With the fast development of technology, it is possible to investigate other alternative techniques. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins for more than 15 years. Unfortunately, this technique is less accurate than peptide sequencing method and cannot handle protein mixtures, which hampers the widespread use of PMF technique. If we can remove these limitations, PMF will become a useful tool in protein mixture identification. *Results:* We first formulate the problem of PMF protein mixture identification as an optimization problem. Then, we show that the use of some simple heuristics enables us to find good solutions. As a result, we obtain much better identification results than previous methods. Moreover, the result on real MS data can be comparable with that of the peptide sequencing method. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF method in protein mixtures. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures.