The use of the isotopic distribution as a complementary quality metric to assess tandem mass spectra results

详细信息    查看全文

• 作者：Fatemeh Zamanzad Ghavidel^a ; Inge Mertens^b ; ^c ; Geert Baggerman^b ; ^c ; Kris Laukens^d ; Tomasz Burzykowski^a ; ^e ; Dirk Valkenborg^a ; ^b ; ^c ; ^{dirk.valkenborg@vito.be" class="auth_mail}
• 关键词：Shotgun proteomics ; SEQUEST ; PSM ; Pearson's 蠂2 statistic
• 刊名：Journal of Proteomics
• 出版年：26 February, 2014
• 年：2014
• 卷：98
• 期：Complete
• 页码：150-158
• 全文大小：968 K

文摘

Shotgun proteomics is a powerful technology to study the protein population of a biological system. This approach employs tandem mass spectrometry for amino acid sequencing. Fragmented ion masses can be used in correlative database-searching, like SEQUEST or Mascot, to identify peptides. The database-search method depends upon a score function that evaluates matches between the predicted ions and the ions observed in the tandem mass spectrum. Principally, peptide identification based on tandem MS and database-search algorithms does not take into account information about isotope distributions of the precursor ions. To determine the effectiveness of these search algorithms in terms of their ability to distinguish between correct and incorrect peptide assignments, we propose an additional metric that quantifies the similarity between the theoretical isotopic distribution for the precursor ions selected for tandem MS and the experimental mass spectra by using Pearson's 蠂² statistic. The observed association between Pearson's 蠂² statistic and the score function indicates that good scores can be obtained for molecules which exhibit atypical isotope profiles, while low scores can be obtained for fragment spectra which have a clear peptide-like isotope pattern. These results demonstrate that Pearson's 蠂² statistic can be used in conjunction with the score of database-search algorithms to increase the sensitivity and specificity of peptide identification.

Biological significance

In this manuscript, we present a workflow that provides a new perspective on the quality of peptide-to-spectrum matches (PSM) employed in database-searching strategies for peptide identification. Additional views on a dataset can facilitate a more hypothesis-driven interpretation of the mass spectrometry signals. The similarity metric on the PSM scores contemplates the isotopic profile and results in a measure that conveys a degree of biomolecular similarity observed from the precursor of the selected tandem MS spectra. A close agreement between the PSM score and the similarity metric will result in a higher confidence for the identification of the selected precursor ion.