Peptide Spectral Library

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Peptide Spectral Library is a curated, annotated and non-redundant collection/database of LC-MS/MS peptide spectra. One essential utility of Peptide Spectral Library is to serve as consensus templates supporting the identification of peptide/proteins based on the correlation between the templates with experimental spectra. The process of peptide/protein identification is called spectral library searching. Comparing to traditional peptide spectra identification approach, sequence database searching, spectral library searching offers many unique benefits, which are illustrated in the following sections.

Spectral library has been used in the small molecules mass spectra identification since the 1980s.[1] In the early year of shotgun proteomics, pioneer investigations suggested that similar approach might be appliable in shotgun proteomics for peptide/protein identification.[2] But until recent years, with the availability of millions of confidently identified MS/MS spectra, the implementation of Peptide Spectral Library shows practical value.

Tandem MS and Shotgun Proteomics[edit]

Modern tandem MS instruments combine features of fast duty cycle, exquisite sensitivity, and unprecedented mass accuracy Tandem MS, which is an ideal match for the large-scale protein identification and quantification in complex biological systems. In a shotgun proteomics approach, proteins in a complex mixture are digested by proteolytic enzymes, such as trypsin. Subsequently, one or more chromatographic separations are applied to resolve resulting peptides, which are then ionized and analyzed in a mass spectrometer. To acquire tandem MS, a particular peptide precursor is isolated, and fragmented in a mass spectrometer; the mass spectra corresponding to the fragments of peptide precursor is recorded, which is called tandem mass spectra. Tandem mass spectra contains specific information regarding the sequence of the peptide precursor, which can aid the identification of peptide/protein .

Protein Identification via Sequence Database Searching[edit]

Sequence Database Searching is widely used currently for mass spectra based protein identification. In this approach a protein sequence database is used to calculate all putative peptide candidates in the given setting (proteolytic enzymes, miscleavages, post-translational modifications). The sequence search engines use various heuristics to predict the fragmentation pattern of each peptide candidate. Such derivative patterns are used as template to find a sufficiently close match within experimental mass spectra, which serves as the basis for peptide/protein identification.[3] Many tools are developed for such practice, which supported many discoveries in the past, e.g. SEQUEST,[4] Mascot.[5]

Shortcomings of the Sequence Database Searching Workflow[edit]

Due to the complex nature of peptide fragmentation in a mass spectrometer, derivative fragmentation patterns fall short to reproduce experimental mass spectra, especically relative intensities among distinct fragments. Thus, Sequence Database Searching faces a bottleneck of limited specificity. Sequence Database Searching also demands vast search space, which still could not cover all possibility of peptide dynamics, exhibiting limited efficiency PTM(post-translational modifications). The searching process is sometimes painfully slow and requiring costly high-performance computers. In addition, the nature of Sequence Database Searching disconnects the research disocveries among different groups or at different times.

Advantages and Limitations of Spectral Library Searching[edit]

Firstly, much reduced search space will definitely decrease the searching time. Secondly, taking full advantage of all spectral features, including relative fragment intensities, neutral losses from fragments, and various additional specific fragments, the process of spectra searching will be more specific, and it will generally provide better discrimination between true and false matches. The spectral library searching is not appliable in situation where discovery of novel peptides or proteins is the goal. Fortunately, more and more high-quality mass spectra are being acquired by the collective contribution of the scientific community, which would continuously expand the coverage of Peptide Spectral Library.

Research Community Focused Peptide Spectral Library[edit]

For Peptide Spectra Library, to reach a maximal coverage is a long term goal, even with the support of scientific community and ever-growing proteomic technologies. However, the optimization for a particular module of the Peptide Spectra Library is a more manageable goal, e.g. the proteins in a particular organelle or relevant to a particular biological phenotype. For example, a researcher studying mitochondrial proteome, will likely foucs his/her analyses within protein modules within the mitochondria. The Research Community Focused Peptide Spectral Library supports targeted research in a comprehensive fashion for a particular research community.

The Cardiac-Specific Organellar Protein Atlas Knowledgebase(COPaKB)[edit]

COPa knowledgebase is being developed in an international consortium, which aims at providing a specialized, comprehensive, and interactive support to the cardiovascular community in protein biology at a systems scale. In this knowledgebase, orthogonal sets of proteomic knowledge are integrated, including mass spectra datasets, image datasets, gene-based datasets and clinical datasets. In a data federation framework, users can access all these datasets from a single web server.This spectral library component of it is built upon a modular infrastructure, which offers flexibilities to users and future updates. In the first release launched in July 2011, it included human mitochondrial module, mouse mitochondrial module, human proteasome module and mouse proteasome module. With the partnership with the scientific community, these modules will be continuously updated and new modules will also be integrated in the upcoming releases.


  1. ^ Domokos, L., Hennberg, D., and Weimann, B. 1984. Computer-aided identification of compounds by comparison of mass spectra. Anal. Chim. Acta 165:61-74.
  2. ^ Yates, J.R., 3rd, Morgan, S.F., Gatlin, C.L., Griffin, P.R., and Eng, J.K. 1998. Method to compare collision-induced dissociation spectra of peptides: Potential for library searching and subtractvie analysis. Anal. Chem., 70:3557-3565.
  3. ^ Strand, Kim (July 29, 2013). "Peptide Spectral Library overview". EP. Retrieved 5 August 2013. 
  4. ^ Eng,J.K. et al. (1994) An approach to correlate tandem mass-spectral data of peptides with amino-acid-sequences in a protein database. J. Am. Soc. Mass Spectrometry, 5,976-989.
  5. ^ Perkins, D.N. et al. (1999) Probability-based protein identification by searching sequence database using mass spectrometry data. Electrophoresis, 20, 3551-3567.

External links[edit]