Back in June of 2022 I left Austria to go on my first secondment in Ghent, Belgium to visit Alireza and the rest of the Compomics group. Besides getting to know the people,  enjoying the summer in the beautiful city and eating numerous waffles, I also did a small project together with members of the group. Specifically Arthur Declercq and Ralf Gabriels, who are the main developers of the well known rescoring platform MS²Rescore (https://github.com/compomics/ms2rescore) [1]. At that time I was working on making MS²Rescore compatible with MS Amanda [2], a well established database search engine for peptide identification, developed by my supervisor Viktoria Dorfer. 

This small project had the main purpose of me getting to know the tools developed at CompOmics and this eventually lead to a great collaboration between our two groups that resulted in this recent publication:

“MS²Rescore 3.0 is a modular, flexible, and user-friendly platform to boost peptide identifications, as showcased with MS Amanda 3.0” 

[Caption: Peptide identification result files from many different search engines, including MS Amanda, are parsed to MS²Rescore which will then perform data-driven rescoring, utilizing several different feature generators and rescoring engines. This leads to a higher number of confident identifications at the same false discovery rate (FDR) threshold or a similar number of confident identifications at a more stringent FDR threshold. MS²Rescore results can be easily inspected in the newly added HTML quality control reports.]

We’re excited to introduce the latest, enhanced versions of both MS²Rescore and MS Amanda. MS²Rescore 3.0 is highly modularized and flexible, making it easy to add new input formats through the python package psm_utils (https://github.com/compomics/psm_utils) [3], new feature generation modules and new rescoring modules. This version of MS²Rescore is available both as a command line interface, a graphical user interface and as a python API, making implementation into already existing workflows for peptide identification very easy.  Lastly, MS²Rescore 3.0 will output a HTML quality control report after the rescoring which allows users to assess the effect of data-driven rescoring on their identification workflow and the performance of individual features. With MS Amanda 3.0 come seven new columns in the output CSV file that can be used for rescoring search results as well as automatic integration with Percolator [4]. 

We demonstrate the flexibility of MS²Rescore 3.0 by connecting it with MS Amanda 3.0 and show how using MS²Rescore 3.0 in combination with MS Amanda 3.0 can increase the number of identified spectra in a challenging single-cell data set. 

It feels great knowing that what started as a small project during my secondment eventually ended up with me having my first publication. Since this was a highly collaborative process between our two research groups it is important to mention that Arthur Declercq and I are shared first authors and that Viktoria Dorfer and Ralf Gabriels are shared last authors. Thanks again to Arthur, Ralf, Viki and the rest of my co-authors! 

You can read the publication here:

https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00785

Or the preprint, free of charge, here:

https://chemrxiv.org/engage/chemrxiv/article-details/65b614e2e9ebbb4db9237db9

References

1. Declercq A, Bouwmeester R, Hirschler A, Carapito C, Degroeve S, Martens L, Gabriels R. MS²Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates. Mol Cell Proteomics. 2022 Aug;21(8):100266. doi: 10.1016/j.mcpro.2022.100266. Epub 2022 Jul 6. PMID: 35803561; PMCID: PMC9411678.
2. Dorfer V, Pichler P, Stranzl T, Stadlmann J, Taus T, Winkler S, Mechtler K. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J Proteome Res. 2014 Aug 1;13(8):3679-84. doi: 10.1021/pr500202e. Epub 2014 Jun 26. PMID: 24909410; PMCID: PMC4119474. 
3. Gabriels R, Declercq A, Bouwmeester R, Degroeve S, Martens L. psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results. J Proteome Res. 2023 Feb 3;22(2):557-560. doi: 10.1021/acs.jproteome.2c00609. Epub 2022 Dec 12. PMID: 36508242.
4. Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007 Nov;4(11):923-5. doi: 10.1038/nmeth1113. Epub 2007 Oct 21. PMID: 17952086.