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Abstract Current database search algorithms for mass spectrometry proteomics typically fail to identify up to 75% of spectra. To mitigate the issue, tools such as the widely used Percolator and PeptideProphet leverage machine learning for rescoring. Though this can boost peptide and protein identification rates, the fact that they are trained to separate targets from decoys can lead to inaccurate false-discovery rate (FDR) estimates. In this paper, we propose a novel approach to peptide-spectrum matching, based on a pre-trained large deep learning model, which does not utilize decoys during training and does not require training a new model for every new sample. We trained the Tesorai model on over 100M real peptide-spectrum pairs and demonstrated that the approach performs robustly across a wide range of use cases including standard trypsin-digested human samples, immunopeptidomics, metaproteomics, single-cell and isobaric-labeled samples. In addition to providing robust FDR control, our method increases identifications by up to 170%. Furthermore, we incorporated it into a cloud-native, end-to-end system that can process hundreds of samples in just a few hours. To facilitate new discoveries, we made the model publicly available at www.tesorai.com .
Burq et al. (Tue,) studied this question.