Uncovering Distinct Peptide Charging Behaviors in Electrospray Ionization Mass Spectrometry Using a Large-Scale Dataset

Electrospray ionization is a powerful and prevalent technique
used
to ionize analytes in mass spectrometry. The distribution of charges
that an analyte receives (charge state distribution, CSD) is an important
consideration for interpreting mass spectra. However, due to an incomplete
understanding of the ionization mechanism, the analyte properties
that influence CSDs are not fully understood. Here, we employ a machine
learning-based approach and analyze CSDs of hundreds of thousands
of peptides. Interestingly, half of the peptides exhibit charges that
differ from what one would naively expect (the number of basic sites).
We find that these peptides can be classified into two regimes (undercharging
and overcharging) and that these two regimes display markedly different
charging characteristics. Notably, peptides in the overcharging regime
show minimal dependence on basic site count, and more generally, the
two regimes exhibit distinct sequence determinants. These findings
highlight the rich ionization behavior of peptides and the potential
of CSDs for enhancing peptide identification.