In a manuscript published in Nature Communications, Ph.D. student Kevin Yang from the Alexey Nesvizhskii lab presented a new computational tool, MSBooster. MSBooster improves peptide and protein identification rates in proteomics by using deep learning-based predictions of peptides’ properties and their fragmentation spectra. MSBooster is illustrated using HLA immunopeptidomics, single-cell proteomics, and other datasets. The tool is fully integrated in the FragPipe computational platforms developed by the Nesvizhskii lab that is used by thousands of scientists around the world to analyze mass spectrometry-based proteomics data. 

Congratulations Kevin!

Paper cited:
Yang, K.L., Yu, F., Teo, G.C. et al. MSBooster: improving peptide identification rates using deep learning-based features. Nat Commun 14, 4539 (2023). https://doi.org/10.1038/s41467-023-40129-9