Deep learning for peptide identification from metaproteomics datasets

TL;DR

This paper introduces DeepFilter, a deep learning algorithm that significantly improves peptide and protein identification rates from metaproteomics MS/MS datasets, outperforming existing tools.

Contribution

DeepFilter is a novel deep-learning-based method that enhances peptide-spectrum match confidence and increases identification rates in metaproteomics data analysis.

Findings

DeepFilter identified 20% more peptide-spectrum matches.

DeepFilter identified 10% more proteins.

False discovery rate was maintained at 1%.

Abstract

Metaproteomics are 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. 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 large MS/MS datasets acquired from metaproteome samples. In this paper, we proposed a deep-learning-based algorithm, called DeepFilter, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Compared with other post-processing tools, including Percolator, Q-ranker, PeptideProphet, and Iprophet, DeepFilter identified 20% and 10% more peptide-spectrum-matches and proteins, respectively, on marine microbial and soil microbial metaproteome samples with false discovery rate at 1%.