Papers › PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
Kaiyuan Liu, Yuzhen Ye, Haixu Tang
The de novo peptide sequencing, which does not rely on a comprehensive target sequence database, provided us a way to identify novel peptides from tandem mass (MS/MS) spectra. However, current de novo sequencing algorithms suffer from lower accuracy and coverage, which hinders their applications in proteomics. In this paper, we present PepNet, a fully convolutional neural network (CNN) for high accuracy de novo peptide sequencing. It takes an MS/MS spectrum (represented as a high dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. Our model was trained using a total of 30 million high-energy collisional dissociation (HCD) MS/MS spectra from multiple human peptide spectral libraries. The evaluation results show that PepNet significantly outperformed currently best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) at both peptide level accuracy and positional level accuracy. In addition, PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool of database search engines for peptide identification in proteomics.
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