Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices

Abstract

De novo peptide sequencing is the key technology for finding novel peptides from mass spectra. The overall quality of sequencing results depends on the de novo peptide sequencing algorithm as well as the quality of mass spectra. Over the past decade, the resolution and accuracy of mass spectrometers have improved by orders of magnitude and higher-resolution mass spectra have been generated. How to effectively take advantage of those high-resolution data without substantially increasing the computational complexity remains a challenge for de novo peptide sequencing tools. Here we present PointNovo, a neural network-based de novo peptide sequencing model that can robustly handle any resolution levels of mass spectrometry data while keeping the computational complexity unchanged. Our extensive experiment results show PointNovo outperforms existing de novo peptide sequencing tools by capitalizing on the ultra-high resolution of the latest mass spectrometers. Increased resolution of mass spectroscopy can provide better data for sequencing, but also increases the computational complexity of analysing the data. Qiao and colleagues present here a neural network-based method that processes sequencing data of any resolution while improving the accuracy of predicted sequences.