rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and...

Area of Science:

• Proteomics
• Biochemistry
• Mass Spectrometry

Background:

• Post-translational modifications (PTMs) are crucial for protein function.
• Identifying rare PTMs is challenging with conventional methods.
• Automated validation is needed to improve PTM identification accuracy.

Purpose of the Study:

• To develop and validate an automated method (rPTMDetermine) for identifying rare PTMs.
• To enhance the accuracy and confidence of PTM identification using mass spectrometry.
• To discover novel PTMs and sites missed by standard database searches.

Main Methods:

• Developed rPTMDetermine, a semisupervised methodology using linear discriminant analysis (LDA).
• Employed similarity scoring of tandem mass spectrometry (MS/MS) data for modified and unmodified peptides.
• Applied the method to validate known and discover novel PTMs in a stroke model proteome.

Main Results:

• Validated 99 of 125 identified 3-nitrotyrosyl-containing (nitrated) peptides.
• Corrected errors from incorrect monoisotopic peak assignments, validating additional nitrated peptides.
• Retrieved 236 unique nitrated peptides, including 113 novel nitration sites, with 25 verified against synthetic analogues.
• Identified 296 unique nitrated peptides from the cerebral cortex proteome of a Macaca fascicularis stroke model.
• Extended rPTMDetermine to validate tryptophan oxidation peptides.

Conclusions:

• rPTMDetermine provides a robust, automated strategy for validating rare PTMs.
• The method significantly enhances PTM discovery and confidence in identification.
• This approach complements conventional database searching for comprehensive proteome analysis.