Using ion mobility data to improve peptide identification: intrinsic amino acid size parameters

Area of Science:

• Proteomics
• Analytical Chemistry
• Biophysical Chemistry

Background:

• Peptide ion identification in proteomics relies on comparing experimental drift times (tD) with mobility databases.
• Existing mobility databases are incomplete and difficult to verify, limiting their utility.
• Accurate ion mobility prediction is crucial for reliable peptide identification.

Purpose of the Study:

• To develop a novel method for enhancing peptide ion identification in proteomics using ion mobility data.
• To propose a method employing intrinsic amino acid size parameters for ion mobility prediction.
• To assess the accuracy of mobility predictions against experimental data.

Main Methods:

• Determined intrinsic amino acid size parameters for doubly charged peptide ions from a yeast proteome.
• Utilized these parameters to predict ion mobilities.
• Compared prediction accuracy with a polynomial fit to drift time versus molecular weight data.
• Developed a peptide ion scoring scheme based on predictive enhancement.

Main Results:

• Predicted ion mobilities using intrinsic amino acid size parameters showed higher accuracy than polynomial fits.
• Observed a >2-fold improvement in prediction accuracy for specific peptide ions (arginine-terminated, 12 residues).
• Demonstrated the feasibility of using predicted mobilities to rank peptide sequence assignments.

Conclusions:

• Intrinsic amino acid size parameters offer a more accurate method for predicting peptide ion mobility.
• This predictive enhancement significantly aids peptide ion identification in proteomics.
• The proposed scoring scheme provides a practical tool for improving the reliability of proteomic analyses.