A “chemical linearization” approach was
applied to
synthetic peptide macrocycles to enable their de novo sequencing from
mixtures using nanoliquid chromatography–tandem mass spectrometry
(nLC–MS/MS). This approachpreviously applied to individual
macrocycles but not to mixturesinvolves cleavage of the peptide
backbone at a defined position to give a product capable of generating
sequence-determining fragment ions. Here, we first established the
compatibility of “chemical linearization” by Edman degradation
with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker,
which are of major significance in therapeutics discovery. Then, using
macrocycle libraries of known sequence composition, the ability to
recover accurate de novo assignments to linearized products was critically
tested using performance metrics unique to mixtures. Significantly,
we show that linearized macrocycles can be sequenced with lower recall
compared to linear peptides but with similar accuracy, which establishes
the potential of using “chemical linearization” with
synthetic libraries and selection procedures that yield compound mixtures.
Sodiated precursor ions were identified as a significant source of
high-scoring but inaccurate assignments, with potential implications
for improving automated de novo sequencing more generally.