Neutron encoded labeling for peptide identification.

Area of Science:

• Proteomics
• Mass Spectrometry
• Biochemistry

Background:

• Metabolic labeling with heavy amino acids is standard for relative quantitation in proteomics.
• Mass shifts in labeled peptides can indicate the number of heavy amino acids.
• Existing methods lack precise amino acid counting capabilities.

Purpose of the Study:

• To introduce and validate NeuCode stable isotope labeling with amino acids in cell culture (SILAC) for accurate amino acid counting.
• To develop computational tools for large-scale amino acid counting using NeuCode SILAC data.
• To demonstrate the utility of amino acid counting in reducing peptide candidate search space.

Main Methods:

• Utilized NeuCode SILAC, a technique yielding precursor partners ~40 mDa apart.
• Developed the 'Amino Acid Counter' program for determining amino acid combinations.
• Applied accurate mass and amino acid counting (lysine, leucine) during database searching of MS/MS data.

Main Results:

• Counting lysine residues reduced median candidate sequences from 44 to 14.
• Counting lysines in product ions further reduced candidates to 7.
• Incorporating lysine and leucine counting decreased the search space 6-fold (43 to 7).
• ~20% of precursors were reduced to a single candidate using accurate mass plus lysine and leucine counting.

Conclusions:

• NeuCode SILAC coupled with computational analysis enables accurate amino acid counting.
• Amino acid counting significantly enhances the specificity of peptide identification in mass spectrometry.
• This approach facilitates precursor identification, potentially reducing the need for MS/MS analysis in some cases.