Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification.

ABSTRACT: Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that ProteoRift can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduct

SUBMITTER: Tariq U 

PROVIDER: S-EPMC11370541 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature