Central limit theorem as an approximation for intensity-based scoring function

Rovshan Sadygov, James Wohlschlegel, Sung Kyu Park, Tao Xu, John R. Yates

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


In this paper, we present an intensity-based probability function to identify peptides from tandem mass spectra and amino acid sequence databases. The function is an approximation to the central limiting theorem, and it explicitly depends on the cumulative product ion intensities, number of product ions of a peptide, and expectation value of the cumulative intensity. We compare the results of database searches using the new scoring function and scoring functions from earlier algorithms, which implement hypergeometric probability, Poisson's model, and cross-correlation scores. For a standard protein mixture (tandem mass spectra generated from the mixture of five known proteins), we generate receiver operating curves with all scoring schemes. The receiver operating curves show that the shared peaks count-based probability methods (like Poisson and hypergeometric models) are the most specific for matching high-quality tandem mass spectra. The intensity-based (central limit model) and intensity-modeled (cross-correlation) methods are more sensitive when matching low-quality tandem mass spectra, where the number of shared peaks is insufficient to correctly identify a peptide. Cross-correlation methods show a small advantage over the intensity-based probability method.

Original languageEnglish (US)
Pages (from-to)89-95
Number of pages7
JournalAnalytical Chemistry
Issue number1
StatePublished - Jan 1 2006
Externally publishedYes

ASJC Scopus subject areas

  • Analytical Chemistry


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