Protein Identification Strategies: Towards a Hybrid Approach to Control FDR

Tandem Mass Spectrometry has made a massive advance as a main technology for peptide sequencing. It generates a flood of data that represents significant challenges for their interpretation. With the emergence of tandem mass spectrometry data (MS/MS), a lot of computational methods have been developed to infer peptide sequences from them. However, the correct identification of all proteins present in a sample is of utmost importance to provide a suitable biological interpretation. Mostly, there are two strategies, which are widely used for peptide/protein identification: (i) Database searching strategy consists of comparing the experimental spectra acquired through the mass spectrometer, with the identified spectra belonging to peptide libraries. (ii) De novo sequencing strategy predicts, without the assistance of peptide database, amino acids sequence from its tandem mass spectrum. These two strategies differ in term of their ability and effectiveness to control the False Discovery Rate (FDR). In this paper, we introduce a review study of database searching and de novo sequencing for peptide identification. Some background information of mass spectrometry and peptide/protein identification are first outlined. Afterward, we discuss the process and tools of both database searching and de novo strategies. Aiming to control the False Discovery Rate, an effective and hybrid approach, which combines database searching and de novo strategies, is proposed in this work.