posted on 2023-05-26, 12:36authored byLewis Y. Geer, Joel Lapin, Douglas J. Slotta, Tytus D. Mak, Stephen E. Stein
The unbounded permutations of biological molecules, including
proteins
and their constituent peptides, present a dilemma in identifying the
components of complex biosamples. Sequence search algorithms used
to identify peptide spectra can be expanded to cover larger classes
of molecules, including more modifications, isoforms, and atypical
cleavage, but at the cost of false positives or false negatives due
to the simplified spectra they compute from sequence records. Spectral
library searching can help solve this issue by precisely matching
experimental spectra to library spectra with excellent sensitivity
and specificity. However, compiling spectral libraries that span entire
proteomes is pragmatically difficult. Neural networks that predict
complete spectra containing a full range of annotated and unannotated
ions can be used to replace these simplified spectra with libraries
of fully predicted spectra, including modified peptides. Using such
a network, we created predicted spectral libraries that were used
to rescore matches from a sequence search done over a large search
space, including a large number of modifications. Rescoring improved
the separation of true and false hits by 82%, yielding an 8% increase
in peptide identifications, including a 21% increase in nonspecifically
cleaved peptides and a 17% increase in phosphopeptides.