Generalized method for probability-based peptide and protein identification from tandem mass spectrometry data and...

Area of Science:

• Proteomics
• Computational Biology
• Mass Spectrometry

Background:

• Tandem mass spectrometry (MS/MS) proteomics requires robust computational methods to minimize false positives in peptide and protein identification.
• Existing peptide identification programs often report significance measures that are difficult to interpret as error rates, such as the false discovery rate (FDR).

Purpose of the Study:

• To develop and validate a computational framework for accurate estimation of p-values and false discovery rates in peptide and protein identification from MS/MS data.
• To provide a generic protein scoring scheme for reliable protein-level FDR estimation.

Main Methods:

• Modeling frequency distributions of database search scores (from MASCOT, X!TANDEM, OMSSA, InsPecT) using generalized lambda distributions.
• Estimating peptide-level p-values and FDRs from these distributions.
• Defining a generic protein scoring scheme and using score distribution simulations for protein-level p-value and FDR estimation.

Main Results:

• Generalized lambda distributions accurately estimated p-values and FDRs for peptide identifications across multiple search engines.
• The generic protein scoring scheme enabled accurate estimation of protein-level p-values and FDRs.
• The methods were validated on four diverse datasets with varying numbers of MS/MS spectra (40,000 to 285,000).

Conclusions:

• The proposed computational approach using generalized lambda distributions provides accurate error rate estimation for peptide and protein identification in proteomics.
• This method enhances the reliability and interpretability of results from various peptide identification software.
• The developed protein scoring scheme offers a robust way to assess protein-level confidence in MS/MS-based proteomics studies.