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An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.

Yisu Peng1, Shantanu Jain1,2, Predrag Radivojac1

  • 1Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States.

Bioinformatics (Oxford, England)
|June 28, 2024
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Summary

A new decoy-free method improves false discovery rate estimation for cross-linking tandem mass spectrometry (XL-MS/MS). This approach enhances accuracy and speed for protein structure and interaction analysis.

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Area of Science:

  • Proteomics
  • Biochemistry
  • Computational Biology

Background:

  • Cross-linking tandem mass spectrometry (XL-MS/MS) is crucial for determining protein structure and interactions.
  • Accurate identification of chemically linked peptides is essential for XL-MS/MS data analysis.
  • Current false discovery rate (FDR) estimation relies on the target-decoy approach (TDA), which has limitations in accuracy and speed.

Purpose of the Study:

  • To develop a novel decoy-free framework for FDR estimation in XL-MS/MS.
  • To provide a more accurate and efficient alternative to the TDA for XL-MS/MS data analysis.

Main Methods:

  • Developed a decoy-free framework utilizing multi-sample mixtures of skew normal distributions.
  • Employed an expectation-maximization algorithm with constraints to estimate FDR.
  • Leveraged score distributions of first- and second-ranked peptide-spectrum matches.

Main Results:

  • The proposed decoy-free approach (DFA) demonstrates theoretical soundness.
  • Evaluated on ten datasets, the DFA shows competitive performance compared to TDA.
  • The DFA offers improvements in accuracy, estimation variance, and run time.

Conclusions:

  • The novel decoy-free framework is a viable alternative to TDA for XL-MS/MS.
  • This method enhances the reliability and efficiency of protein structure and interaction studies.
  • The DFA contributes to advancing biological discovery through improved proteomic data analysis.