MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

Mass
spectrometry-based proteomics technologies are prime methods
for the high-throughput identification of proteins in complex biological
samples. Nevertheless, there are still technical limitations that
hinder the ability of mass spectrometry to identify low abundance
proteins in complex samples. Characterizing such proteins is essential
to provide a comprehensive understanding of the biological processes
taking place in cells and tissues. Still today, most mass spectrometry-based
proteomics approaches use a data-dependent acquisition strategy, which
favors the collection of mass spectra from proteins of higher abundance.
Since the computational identification of proteins from proteomics
data is typically performed after mass spectrometry analysis, large
numbers of mass spectra are typically redundantly acquired from the
same abundant proteins, and little to no mass spectra are acquired
for proteins of lower abundance. We therefore propose a novel supervised
learning algorithm, MealTime-MS, that identifies proteins in real-time
as mass spectrometry data are acquired and prevents further data collection
from confidently identified proteins to ultimately free mass spectrometry
resources to improve the identification sensitivity of low abundance
proteins. We use real-time simulations of a previously performed mass
spectrometry analysis of a HEK293 cell lysate to show that our approach
can identify 92.1% of the proteins detected in the experiment using
66.2% of the MS2 spectra. We also demonstrate that our approach outperforms
a previously proposed method, is sufficiently fast for real-time mass
spectrometry analysis, and is flexible. Finally, MealTime-MS’
efficient usage of mass spectrometry resources will provide a more
comprehensive characterization of proteomes in complex samples.