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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Evaluating False Transfer Rates from the Match-between-Runs Algorithm with a Two-Proteome Model.

Matthew Y Lim1, João A Paulo1, Steven P Gygi1

  • 1Department of Cell Biology , Harvard Medical School , Boston , Massachusetts 02115 , United States.

Journal of Proteome Research
|September 25, 2019
PubMed
Summary
This summary is machine-generated.

Computational methods like MaxQuant's match-between-runs (MBR) reduce missing values in proteomics. While MBR transfers many identifications, few false transfers remain after applying the LFQ algorithm.

Keywords:
false transfer ratelabel-free quantitationmatch-between-runstwo-proteome mixture

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Computational Biology

Background:

  • Stochasticity in LC-MS/MS runs causes missing peptide abundance values.
  • Computational algorithms are used to impute missing data and improve identification rates.

Purpose of the Study:

  • To evaluate the error rate of false peptide identification transfers using MaxQuant's match-between-runs (MBR) algorithm.
  • To assess the impact of MBR on data completeness and accuracy in quantitative proteomics.

Main Methods:

  • A two-sample/two-proteome experimental design was employed.
  • 20 yeast-containing samples were run alongside 20 samples without yeast.
  • MaxQuant's match-between-runs (MBR) and LFQ algorithms were utilized for data processing.

Main Results:

  • MBR increased spectral identifications by approximately 40%.
  • 44% of identified yeast proteins were transferred to samples lacking yeast.
  • Only 2.7% of these false transfers were retained after applying the LFQ algorithm.

Conclusions:

  • MBR significantly increases peptide identifications but introduces numerous false transfers.
  • The MaxQuant LFQ algorithm effectively filters out most false positive identifications resulting from MBR.
  • MBR is a valuable tool for improving data completeness in proteomics, with minimal impact from false transfers on final datasets.