1) Click the de novo sequencing toolbar icon or select “de novo” from the “Tools” menu.
2) Enter the settings as shown: parent ion error tolerance to 0.1 daltons. Fragment mass error tolerance to 0.8 daltons, enzyme to trypsin, carboxymethyl as a fixed modification.
Note that we are not going to preprocess this data “on the fly” as we have already preprocessed the data during the data refinement stage. We will also choose to report only one peptide per spectrum for simplicity’s sake.

You can save the parameters that you used for future reference by clicking “Save As”. Click “OK” to commence analysis. The PEAKS auto de novo algorithm derives sequence candidates for each of the six spectra in our example data file. Once the de novo sequencing is completed the results file will appear in the “Project View Panel”.

Double click on the de novo file and the results will appear in the “Main Processing Window”.
Take a look at spectra ID 6. Notice that the [1] refers to the modification which in this case is Carboxymethyl.

At the top of the screen you will see the peptide candidates in the “Peptide Candidates Frame”. The peptide candidates are sorted by “ID”. Right next to the proposed sequence, the auto de novo “Total Local Confidence” (TLC) and “Average Local Confidence” (ALC%) confidence scores are shown. You will also see the m/z ratio, mass, retention time etc. listed for each peptide sequence. Below the “Peptide Candidates Frame” is the “Ion Table Frame”. Each amino acid is color coded by confidence level with the masses for matched a, b and c ions listed in blue and for the matched x, y and z ions listed in red. Below the “Ion Table Frame” is the “Spectrum View Frame”. This frame is useful for seeing the strength of the ms/ms peaks that PEAKS has set as ions. Here the alignment of the assigned b (blue) and y (red) ions with the entire spectrum corresponding to the selected peptide can be observed. By clicking on the tabs at the bottom of the page, you can view the corresponding survey scan, the spectrum alignment and the error map.

Click here to continue on to the next tutorial, dedicated to database protein identification.