  The unbounded permutations of biological molecules, including proteins and
their constituent peptides, presents 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.
