Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide
analyses rely on tandem mass spectrometry and the
extensive separation of proteolytic mixtures. This imposes considerable
instrumental time consumption, which is one of the main obstacles
in the broader acceptance of proteomics in biomedical and clinical
research. Recently, we presented a fast proteomic method termed DirectMS1
based on ultrashort LC gradients as well as MS1-only mass spectra
acquisition and data processing. The method allows significant reduction
of the proteome-wide analysis time to a few minutes at the depth of
quantitative proteome coverage of 1000 proteins at 1% false discovery
rate (FDR). In this work, to further increase the capabilities of
the DirectMS1 method, we explored the opportunities presented by the
recent progress in the machine-learning area and applied the LightGBM
decision tree boosting algorithm to the scoring of peptide feature
matches when processing MS1 spectra. Furthermore, we integrated the
peptide feature identification algorithm of DirectMS1 with the recently
introduced peptide retention time prediction utility, DeepLC. Additional
approaches to improve the performance of the DirectMS1 method are
discussed and demonstrated, such as using FAIMS for gas-phase ion
separation. As a result of all improvements to DirectMS1, we succeeded
in identifying more than 2000 proteins at 1% FDR from the HeLa cell
line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated
and analyzed during the current study have been deposited to the ProteomeXchange
Consortium via the PRIDE partner repository with the data set identifier
PXD023977.