Blog
The promise of spectrum-centric DIA
We’re working on a universal spectrum-centric solution for the analysis of DIA data, called Mascot DIA. A preview was presented at our ASMS 2025 Breakfast Meeting, whose slides are free to download. But what is spectrum-centric searching and what is it meant to solve? Complementary approaches “Peptide-centric” and “spectrum-centric” analysis are broad technical terms that are mainly of interest to [...]
Identify more proteins using Mascot and Proteome Discoverer with this one small trick
Mascot Server is often used with software packages like Thermo Proteome Discoverer™ (PD). We recently made it possible to enable machine learning on the server side by specifying it in the Mascot instrument definition, which is also the topic of one of our ASMS 2025 posters. An important detail was overlooked in the initial instructions, which is: you should disable [...]
Adding a custom AI/ML adapter to Mascot
Mascot Server 3.0 and later ship with MS2Rescore for predicting retention time and spectral similarity. What if you wanted to use a different AI/ML system for predictions? Mascot Server has an application programming interface (API) exactly for this purpose. The tutorial below includes a fully working Python script that you can use as a starting point for your own adapter. [...]
Big databases and large peak lists
When it comes to sequence database searches, how large is too large to handle? This is actually two questions in one: a search can be large because the database has millions of protein sequences; or it can be large because of the number of peak lists. Mascot Server has no limits because of its design, which makes it ideal for [...]
Mascot Distiller quantitates more proteins with machine learning
Mascot Distiller can now use machine learning with LFQ, TMT and other quantitation methods, thanks to API changes made in the recent Mascot Server 3.1 release. Machine learning typically increases the number of identified peptides at a fixed false discovery rate, which often leads to a substantial boost in the number of proteins quantified. We demonstrate this with an LFQ [...]
Using machine learning with Mascot and Proteome Discoverer
Update: There is a newer version of this article, Identify more proteins using Mascot and Proteome Discoverer with this one small trick. The PD setup instructions have been updated accordingly. Mascot Server 3.1 is a patch release that enhances integration with Thermo Proteome Discoverer™ and fixes a few important bugs introduced in Mascot Server 3.0. The full list of improvements [...]
Mascot vs FragPipe: Uncovering endogenous proteolytic processing
Studying endogenous proteolytic processing, or N-terminomics, typically involves selective enrichment of protein N-termini. An alternative is to use the standard shotgun LC-MS/MS approach with the unenriched sample, which requires the database search to identify semi-specific peptides. Mascot Server 3.0 includes MS2PIP machine learning models for fragment intensity prediction, which can give a big boost to semi-specific identifications. Recent versions of [...]
Best MS2PIP model for Thermo Orbitrap
Mascot Server 3.0 greatly improves protein and peptide identification rates with Thermo Orbitrap instruments. The new version ships with MS2PIP, which provides fragment intensity predictions. When the database search results are correlated with predicted spectra, it boosts the number of statistically significant matches even with straightforward tryptic digests. CID and HCD models For qualitative work and label-free quantitation, Mascot Server [...]
Tutorial: Selecting the best MS2PIP model
Mascot Server 3.0 can refine database search results using predicted fragment intensities. The predictions are provided by MS2PIP, and Mascot ships with suitable models for common instrument types. This tutorial shows how to select the best model for your instrument and experiment. What is MS2PIP? MS2PIP is a tool for predicting the MS/MS fragmentation spectrum from a peptide sequence, charge [...]
Tutorial: Selecting the best DeepLC model
Mascot Server 3.0 can refine database search results using predicted retention times. The predictions are provided by DeepLC, and Mascot ships with twenty models for different gradient lengths, column types and peptide properties. This tutorial shows how to select the best model for your experiment. What is DeepLC? DeepLC is a retention time predictor that uses a convolutional neural network [...]
Predicted RT and fragment intensity in Mascot Server 3.0
A release candidate of the next version of Mascot Server is running on this website. One of the headline features in the preliminary release notes is refining results with machine learning, which includes integration with MS2Rescore. Below is a preview for you to enjoy while we are beta testing the new release. What is MS2Rescore? Mascot Server has shipped with [...]
How does rescoring with machine learning work?
Mascot Server ships with Percolator, which is an algorithm that uses semi-supervised machine learning to improve the discrimination between correct and incorrect spectrum identifications. This is often termed rescoring with machine learning. What exactly does it mean, and how does it work? Identifying correct matches using a score threshold When you submit a search against the target protein sequence database, [...]
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