• Local Confidence is the confidence that a particular amino acid is present in the de novo peptide at a particular position. It is presented as a percentage.
• Total Local Confidence (TLC) is the sum of the local confidence scores (0 to 1) from each amino acid in the peptide sequence.
• Average Local Confidence (ALC) is the average of the TLC. It is TLC divided by the number of amino acids in the peptide sequence.

Usually you will find good peptides at 55% and above. This does not mean that the whole sequence is the correct sequence. This is because the beginning and end of the spectrum are much more difficult to interpret when it comes to de novo sequencing due to the high mass/low mass of the ions produced by these fragments. This is why we added the colour coding to the results so you can see where in the sequence there is higher confidence.

You can definitely chop off the low scoring amino acids from the ends of the de novo sequence tags. To do this, click on the score threshold slider button (green, yellow, red symbol at the top of the de novo results). Use the slider to set a score threshold and any amino acid below that threshold will be reduced to its mass in Daltons. Once you export the tags, the last column in the excel export will follow this confidence threshold.

Bottom line: result quality. PEAKS consistently gives more accurate peptide sequences and with better confidence than other programs. This is a result of PEAKS’ global optimization algorithms and sophisticated scoring schema.

The -10lgP score is simply a p-value on an increasing scale. For example, a p-value of 1% equals a -10lgP of 20. For a given score x, its equivalent P-value is the probability that a false identification has a score greater than or equal to x. For more information on the scoring system see the following PEAKS DB Score Tutorial.

PEAKS only requires the database to be in FASTA format, and the header should contain only standard ASCII characters.

Likely the database was simply not located by PEAKS. In PEAKS, go to Configuration then Database. Make sure you have the correct FASTA database type selected in the drop down menu and the correct database path selected in the path text box.

For PTMs with a neutral loss mass, PEAKS will search for both the monoisotopic mass of the modified amino acid as well as the neutral loss mass. PEAKS does not require the neutral loss mass to be there in order for the modified amino acid to be identified. It does help if it is present.

Please refer to PEAKS DB Score Tutorial for more details.

1. Using Mascot as an example, in Preferences, go to “Search Engine”, then “Mascot Settings”.
2. You will need to know the IP address or name of the Mascot server and Mascot version.
3. Enter this in the ‘Hostname (or IP address):’ text box. Often this is simply “//mascotserver” without quotes.
4. Enter the ‘Port:’ number, the default is 80.
5. Enter the virtual directory, the default is ‘/mascot’.
6. Select your version number in the ‘Version’ drop down menu.
7. If you need to use a user name and password to login, you can enter them in the sections below the version number.
8. Check the “Save Password” box so you do not have to enter it every time.
9. Click the “Apply” button to save any changes you have made.

*In the event that Mascot is installed on the local computer, type “localhost” in the Hostname (or IP address) field. For example if your connection reads “http://mascotserver/mascot”,

The peptide percent score reflects the quality of the peptide-spectrum match taking into account all search engines used. The score is calculated in accordance with the empirical calculation used in Peptide-Prophet.

The protein percent score is calculated from the peptide percent scores. The peptide percent scores are combined by a weighted sum for each protein. The PeptideProphet method is then applied to the whole set of proteins to get the final protein probability scores.

“-1” means that a ratio was found but it was not derived from any high scoring peptides. If you want the ratio to be displayed, you can raise the peptide score threshold set in the run parameters.

The most likely reason is users must have at least two samples and the same number of fractions in each sample.

When there is a potential memory issue, it is best to check PEAKS’ performance configuration to make sure the computer resource allocation is correct. The general rule when setting the performanceconfiguration.exe (found in C:\PEAKSStudioXplus folder by default) is to only allocate 75-80% of the computer’s physical resources to PEAKS.

Suggested Computer Resources and Performance Configurations

Above are the suggested computer resources and performance configuration to fully optimize PEAKS licenses. If manual configuration is required, determine the amount of threads and RAM that can be allocated for PEAKS. For every thread allocated to PEAKS, 3 GB of memory is required.

1. Best to have all files (sample files, project, and database) stored in the local drive.
2. Ensure power settings are set to never sleep
   • go to the start menu and open the control panel by typing ‘control panel’
   • in the top right search bar, type ‘power’ and open ‘change power settings’
   • switch to the high-performance option
   • also, select ‘change plan settings’ and then ‘change advanced power settings’
   • find ‘turn off hard disk power’ and switch it to 0, this means the hard disk power will not turn off
   • also turn all sleep options to ‘never’

1. All types of datafiles are not loading and the progress bar is stuck at 0%.
   This is likely related to a third party tool integrated into PEAKS called a derbyServer. For some reason, the read/write permissions associated with the folder may have changed. If you reset the folders with the following method, it should prevent this issue:
   • while logged into the account that uses PEAKS , make sure PEAKS is closed
   • go to C:\Users[your user name]\PEAKS_10.5 and delete defaultproject, defaultTaskDB, and derbyserver
   • when you restart PEAKS these folders will be recreated in their default format using default permissions and PEAKS should run smoothly.
2. Multiple files are added but some are not loaded successfully (green ¾ icon not filled in) or data loading progress bar goes up to 99% then fails
   This is more likely a memory issue, and it usually happens for larger raw files.
   Increase the raw_file_loader_heap_size=512 to 1024 (or 2048 if there is enough computer resources) from C:\Users[username]\PEAKS_10.0\PeaksInternal.properties file.

A general tip to make sure data loading and data refinement completed successfully:
Make sure MS1 and MS2 scan numbers are not 0. To check for this, click on the sample file and check the ‘Properties’ tab.

1. Check if there is enough free disk in the C: drive and the local drive the project is stored in. Large projects may require up to 50 GB free disk in the C: drive and double the current project size in the hard drive.
2. Check if there is anything in the parameters that might be causing this. In general, the modification list must be kept at minimum (around 3- 5 if possible) to decrease search space. Additional modifications can be specified in the PEAKS PTM search using the “Advanced Settings”.
3. Increase derby memory if there is enough computer resources. Go to C:\Users[username]\PEAKS_10.5\PeaksInternal.properties and edit the file in Notepad. Increase java_vmArg_derby=-Xmx1024m to 2048 (2 GB).

1. Check if PEAKS is able to read the fasta file correctly or if there is any invalid content by going to Preferences>Database and then click the “Validate Database” option.
2. Reduce batch size to prevent out of memory errors.
   • go to C:\PEAKSStudioXplus\algorithmpara and open batchSizeConfig.xml in notepad
   • then, change 5000 to 2500
   • this will reduce the size of the batches run through PEAKS DB
   • then, change 1.3 to 2.0
   • this will increase the amount of memory allocated to searching each spectrum

In some cases, a PEAKS search is completed but no peptides, or very few peptides are identified. This may happen if the specified mass error tolerances are stringent for the data. Below are the suggested mass error tolerances for each instrument.

Suggested Instrument Mass Error Tolerances

Above are the default mass error tolerances for each instrument. These can be adjusted as necessary by referring to Figure 2b. Scatterplot of PEAKS peptide score versus precursor mass error and Figure 6. Precursor mass error of peptide-spectrum matches (PSM) in filtered result.