DeepNovoV2: Better de novo peptide sequencing with deep learning

In proteomics, De novo peptide sequencing from tandem Mass Spectrometry (MS) data is the key technology for finding new peptide or protein sequences. It has successful applications in assemble monocolonal antibody sequences (mAbs)[1] and great potentials in identifying neoantigens for personalized cancer vaccines[2]. Given the importance of the de novo peptide sequencing technology, massive research have been done in this area and different tools have been proposed[3][4][5][6][7]. In 2017, Tran et al. first introduced deep learning to de novo peptide sequencing and proposed DeepNovo, a neural network based de novo peptide sequencing model[8] for Data Dependent Acquisition (DDA) MS data. Inspired by the success of image captioning model[9], DeepNovo integrated two fundamental types of neural networks, CNNs and LSTM, in order to extract features from both the spectrum and the "language model of peptides". In DeepNovo, each spectrum is represented as a long intensity vector and CNNs are applied on segments of this vector to extract features and make predictions of the next amino acid. CNNs have been proved as effective tools for pattern recognition in different applications like image classification, object detection and sentiment analysis[10][11][12]. By applying CNNs to the intensity vector, DeepNovo could learn from noisy spectrum.