Deep learning for peptide identification from metaproteomics datasets

Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learningbased algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github. com/Biocomputing-Research-Group/DeepFilter. Significance: The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of MS/MS data sets acquired from metaproteome samples. Systematical experiment results demonstrate that the DeepFilter identified up to 12% and 9% more peptide-spectrum-matches and proteins, respectively, compared with existing filtering algorithms, including Percolator, Q-ranker, PeptideProphet, and iProphet, on marine and soil microbial metaproteome samples with false discovery rate at 1%. The taxonomic analysis shows that DeepFilter found up to 7%, 10%, and 14% more species from marine, soil, and human gut samples compared with existing filtering algorithms. Therefore, DeepFilter was believed to generalize properly to new, previously unseen peptidespectrum-matches and can be readily applied in peptide identification from metaproteomics data.

Automated summary

This study introduces a deep-learning-based algorithm, DeepFilter, which improves peptide identification from metaproteomics data sets. Results show that DeepFilter outperforms existing filtering algorithms in terms of peptide-spectrum-matches and species discovery in marine, soil, and human gut samples. The algorithm is believed to be able to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification.