Mascot DDA workflow for LC-MS/MS

In a bottom-up proteomics experiment, the goal is to identify all peptides present in the sample. This is almost always accomplished by searching the MS/MS spectra against a protein sequence database or spectral library.

Identifying and quantifying proteins from LC-MS/MS runs in DDA mode has four steps:

1. Mascot Distiller processes the raw data into peak lists
2. Mascot Server identifies peptides
3. Mascot Server infers proteins from the peptide matches
4. Mascot Distiller provides protein quantitation and produces a report

All steps can be automated using the Mascot Daemon client shipped with Mascot Server.

The workflow is exactly the same for Mascot DDA and Mascot DIA, but peak picking and scoring differ.

1. Peak picking

The starting point of data analysis is a ‘raw’ file from a mass spectrometer, which contains survey (MS) scans followed by one or more MS/MS scans. In DDA mode, each MS/MS scan is assumed to contain fragments from one precursor.

Mascot Server does not read raw data, so the first step is to convert the raw file into peak lists, also called peak picking or peak detection. This is an important step as it removes irrelevant noise, deisotopes and decharges peaks, reduces the file size and encodes important metadata for downstream use.

Depending on the LC separation and isolation window width, a small percentage of MS/MS spectra may include fragments from multiple peptides. These chimeric spectra are more typical in highly multiplexed samples like metaproteomics. If they are present, it is beneficial to deconvolute them before the database search.

2. Database search

The protein database search finds the ‘best’ candidate peptide for each MS/MS spectrum based on the precursor mass (from the survey scan) and the MS/MS fragment peaks.

Mascot does not make guesses about the relative intensities of fragment peaks. Instead, the algorithm tries to maximise the number of peaks explained by the candidate peptide, including any neutral loss peaks.

The score of a peptide match reflects how well the database entry matches the observed MS/MS spectrum. Mascot calculates the probability that the match has occurred by chance, and the score is simply -10log₁₀(p).

Probabilistic scoring is a necessary but not sufficient condition for high quality search results. Mascot Server automatically searches the peak lists against a decoy database of reversed or randomised protein sequences. The count of decoy matches provides an excellent estimate of the number of false positives in the results from the target database, which is reported as the false discovery rate.

We also recommend rescoring the results using machine learning. It is especially beneficial with ‘difficult’ data sets like endogenous peptides, proteogenomics and metaproteomics. Mascot Server includes Percolator and MS²Rescore.

The tutorial Searching uninterpreted MS/MS data explains Mascot database search parameters and how to choose a sequence database.

3. Protein inference

The database search identifies peptides. All proteomics workflows must do an additional step to identify proteins, called protein inference.

By default, Mascot only displays proteins that have at least one statistically significant peptide match that is unique to the protein hit (not shared with any other protein sequence). Proteins without unique peptide evidence – subset proteins – are hidden. Mascot does display sameset proteins, where two or more proteins share exactly the same peptide evidence. Because there is nothing in the mass spectra to differentiate between samesets, a ‘lead’ protein is selected and displayed by sorting protein accessions in alphanumeric order.

Mascot always shows a protein score for protein hits, but this is not a probabilistic score. Common myths about protein scores discusses a few misunderstandings that recur in the literature. Creating a list of confidently identified proteins is very easy using Mascot’s Report Builder.

4. Quantitation

In most experiments, the goal is to not only identify proteins but also quantify the relative amounts across samples. The Mascot approach is identify first, then quantify. Once you have a list of peptide matches from the database search, peptides that meet the quality criteria are quantified. Then, proteins are quantified based on peptide quantitation.

The most popular protocols today are label-free quantitation (LFQ) and iTRAQ™/TMT® labelling. LFQ is based on extracted ion chromatograms (XICs) for precursors, whose source is the survey (MS) scans in the raw file. iTRAQ™/TMT® labelling produces reporter ions in the MS/MS spectrum, whose relative intensities are used as the peptide’s intensity in each sample. Regardless of the protocol, proteins are quantified based on peptide intensities or intensity ratios.

Mascot Distiller supports other protocols too, and it’s a large topic. A good starting point is the Quantitation module in our free online training course, and please e-mail us if you have any questions about the best approach.

A table of raw numbers is hardly ever the finish line. Viewing iTRAQ™ and TMT® results can be done directly in the Protein Family Summary report. For other protocols, Reporting quantitation datasets with Mascot Distiller shows what you can do with the built-in reports and how to write custom reports. You can also export the data in CSV format for further processing in R or Perseus, or tabulate expression data from multiple analyses with Mascot Daemon. The Distiller project file (.rov) can also be imported in third-party tools like Scaffold by Proteome Software.

Third-party software for peak picking and quantitation

If you don’t have suitable software for peak picking, we recommend Mascot Distiller, which has been designed to work with raw data from any instrument. If you are new to Distiller, you are welcome to send us a typical raw file and ask us to return a suitable processing options file. Just email support@matrixscience.com for details.

We recommend Mascot Distiller for peak picking and quantitation, because it produces excellent results with any instrument. However, Mascot Server is widely used for DDA analysis and several different options exist for peak picking. Every instrument data system can create an MGF file (Agilent, Bruker, SCIEX, Shimadzu, Thermo, Waters), and free tools such as ProteoWizard msConvert also create suitable MGF files from DDA runs.

There are also many third-party options for DDA quantitation from Mascot search results, such as Thermo Proteome Discoverer™, Proteome Software Scaffold, Genedata Expressionist and a variety of open source or free tools too numerous to list here.

For example, Mascot Server integrates natively with Thermo Proteome Discoverer™:

1. Thermo Proteome Discoverer™ processes the raw data into peak lists
2. Mascot Server identifies peptides
3. Thermo Proteome Discoverer™ infers proteins from the peptide matches
4. Thermo Proteome Discoverer™ provides protein quantitation and produces a report

Alternatively, peak picking could be done with one software tool and quantitation with a different one:

1. ProteoWizard msConvert processes the raw data into peak lists
2. Mascot Server identifies peptides
3. Proteome Software Scaffold infers proteins from the peptide matches
4. Proteome Software Scaffold provides protein quantitation and produces a report

iTRAQ™ is a trademark of SCIEX, and TMT® is a registered trademark of Proteome Sciences.