Most of the peptide mapping workflows rely on MS2 identified peptides for quantification. Occasionally, however, you may find you want to quantify peptides with no MS2 spectra identified.
Using Protein Metrics’ software you can accomplish this with in-silico peptides. In the broadest sense, in-silico is a way to generate an XIC for any peptide. This allows consistent quantitation of peptides even when MS2 spectra are absent or of poor quality.
The Peptide Manager tool allows the user to fine-tune in-silico processing. It combines all In-silico functionalities previously found in the Peptide Analysis Edit > In-silico menu with a color-coded preview of the project changes and enhanced control over which rows and columns are affected.
- Import CSV file: A list of in-silico peptides may be imported into the project.
- Intersect CSV: Merges the current list of peptides with those in the CSV file
- Enables annotation transfer of corresponding columns “Comment, labels and validation type”
- Feature Finder unknowns matched with in-silico content.
- Add missing samples: Identifies missing peptide and fills in those sequences with equivalent peptides in the sample files (same functionality as “Add missing via existing peptides”)
- Export CSV: exports the peptides to a *.csv file. This file is then available for import into a different project.
An example of an application of the Intersect CSV function is using it in combination with Feature Finder, which allows for the addition of unidentified MS1-only features to a peptide project. Once MS1 features of significant intensity are found, you may use their masses and retention times to identify them through a secondary method (for example common HCPs, contaminants, or a focused Wildcard search). The new identifications can be merged with the existing project using Intersect CSV, and the previous unknowns will be updated with new IDs and annotations.
Example results of an “Intersect” operation, where previously identified features (Unknowns) are matched with the list of peptides in the CSV file. Those that matched on mass and time are renamed and highlighted.