Building and Searching Tandem Mass Spectral Libraries for Peptide Identification

Abstract

Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. Conceptually, the premise of this approach is that the tandem MS fragmentation pattern of a peptide under some fixed conditions is a reproducible fingerprint of that peptide, such that unknown spectra acquired under the same conditions can be identified by spectral matching. In actual practice, a spectral library is first meticulously compiled from a large collection of previously observed and identified tandem MS spectra, usually obtained from shotgun proteomics experiments of complex mixtures. Then, a query spectrum is then identified by spectral matching using recently developed spectral search engines. This review discusses the basic principles of the two pillars of this approach: spectral library construction, and spectral library searching. An overview of the software tools available for these two tasks, as well as a high-level description of the underlying algorithms, will be given. Finally, several new methods that utilize spectral libraries for peptide identification in ways other than straightforward spectral matching will also be described. Molecular & Cellular Proteomics 10: 10.1074/mcp.R111.008565, 1-10, 2011.In the past decade and a half, mass spectrometry-based proteomics has witnessed breathtaking advances. Today, many top research universities and institutes are equipped with proteomics facilities, and the ability to detect and quantify a large number of proteins in a high-throughput manner is having a positive and growing impact in life science research. Among the many proposed experimental workflows, the most widely practiced method is probably the "bottom-up" approach of shotgun proteomics. The key steps of this approach are: (1) proteins are digested into shorter peptides that are more amenable to liquid chromatography (LC)-MS analysis, (2) peptides are further fragmented by tandem mass spectrometry (MS/MS) 1 to yield characteristic fragmentation patterns, and (3) the MS/MS spectra are assigned to their originating peptides by various computational methods (1-4). This last step of assigning MS/MS spectra to their peptide identifications is often the rate-limiting step of the whole proteomics experiment, and has received well-deserved attention over the past decade. These computational methods can be generally classified into three groups, in terms of the "search space," i.e. the set of candidate sequences to consider as possible answers (Fig. 1). On one end of the search space scale are de novo sequencing methods (5-8), which made no initial assumption on what peptides might be present in the sample. Instead the algorithms consider exhaustively all permutations of the 20 amino acids as viable candidates, and try to infer the sequence directly from the MS/MS spectra. In the middle of the search space scale are sequence database searching methods (9 -13), which rely on available sequence databases to limit the search space to only peptides that are...

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“…Some tools take advantages of known spectra and use spectral library databases. 50 Many software packages are well-established for these purposes: examples include Sequest, 51 Mascot, 52 X!Tandem, 53 pfind, 54 Skyline, 55 Sonar, 56 ProbID, 57 Popitam, 58 and Andromeda. 59 For complete lists of software tools, please refer to the following recent reviews.…”

“…59 For complete lists of software tools, please refer to the following recent reviews. 48–50,60 Mascot and Sequest are the most widely used commercial search engines. Popular protein sequences database, for example, Uniprot, NCBInr, and International Protein Index (IPI), can be used to construct mass fingerprints database.…”

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“…The former are used to derive relatively unsophisticated in silico theoretical fragmentation spectra (Cottrell 1994;Eng et al 1994;Yates et al 1995;Geer et al 2004;Fenyo and Beavis 2003), while the latter contain previously identified experimental MS/MS spectra (Lam 2011)(NIST: http://peptide.nist.gov) and have been shown to outperform sequence database searching under optimal conditions . One can also generate a realistic spectral library from a sequence database using detailed MS/MS spectrum prediction (Zhang 2004(Zhang , 2005Yen et al 2011;Yen et al 2009), but this is a more complicated process.…”