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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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Published on: August 17, 2015

Estimating false discovery rates for peptide and protein identification using randomized databases.

Gregory Hather1, Roger Higdon, Andrew Bauman

  • 1Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, WA 98101, USA.

Proteomics
|April 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a simple isotonic regression method for estimating false discovery rates (FDRs) in proteomics. The new method accurately identifies peptides and proteins, offering advantages in simplicity and flexibility.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical analysis

Background:

  • Mass spectrometry-based proteomics is crucial for analyzing biological samples.
  • Accurate false discovery rate (FDR) estimation is vital for reliable peptide and protein identification.

Purpose of the Study:

  • To develop a straightforward and robust method for estimating cumulative and local FDRs (LFDRs) in peptide identification.
  • To demonstrate the method's applicability to protein identification using peptide spectrum score summaries.

Main Methods:

  • Isotonic regression-based statistical modeling.
  • Comparison with existing FDR estimation techniques.
  • Application to peptide and protein identification datasets.

Main Results:

  • The isotonic regression method accurately estimates cumulative FDRs and LFDRs.
  • The method is computationally simple, score-monotonic, and distribution-assumption-free.
  • Demonstrated effective FDR/LFDR estimation for both peptide and protein identification.

Conclusions:

  • Isotonic regression provides a flexible and efficient approach for FDR/LFDR estimation in proteomics.
  • The study highlights the importance of considering both FDR and LFDR for accurate interpretation of results.
  • Moderate FDR can correspond to substantial LFDR, emphasizing nuanced data interpretation.