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The unbounded permutations of biological molecules, including proteins andtheir constituent peptides, presents a dilemma in identifying the components ofcomplex biosamples. Sequence search algorithms used to identify peptide spectracan be expanded to cover larger classes of molecules, including moremodifications, isoforms, and atypical cleavage, but at the cost of falsepositives or false negatives due to the simplified spectra they compute fromsequence records. Spectral library searching can help solve this issue byprecisely matching experimental spectra to library spectra with excellentsensitivity and specificity. However, compiling spectral libraries that spanentire proteomes is pragmatically difficult. Neural networks that predictcomplete spectra containing a full range of annotated and unannotated ions canbe used to replace these simplified spectra with libraries of fully predictedspectra, including modified peptides. Using such a network, we createdpredicted spectral libraries that were used to rescore matches from a sequencesearch done over a large search space, including a large number ofmodifications. Rescoring improved the separation of true and false hits by 82%,yielding an 8% increase in peptide identifications, including a 21% increase innonspecifically cleaved peptides and a 17% increase in phosphopeptides.

AIomics: exploring more of the proteome using mass spectral libraries extended by AI