De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.deep learning | MS | de novo sequencing P roteomics research focuses on large-scale studies to characterize the proteome, the entire set of proteins, in a living organism (1-5). In proteomics, de novo peptide sequencing from tandem MS data plays the key role in the characterization of novel protein sequences. This field has been actively studied over the past 20 y, and many de novo sequencing tools have been proposed, such as PepNovo, PEAKS, NovoHMM, MSNovo, pNovo, UniNovo, and Novor among others (6-19). The recent "gold rush" into mAbs has undoubtedly elevated the application of de novo sequencing to a new horizon (20-23). However, computational challenges still remain, because MS/MS spectra contain much noise and ambiguity that require rigorous global optimization with various forms of dynamic programming that have been developed over the past decade (8-10, 12, 13, 15-19, 24).In this study, we introduce neural networks and deep learning to de novo peptide sequencing and achieve major breakthroughs on this well-studied problem. Deep learning has recently brought about a revolution in many research fields (25), repeatedly breaking state of the art records in image processing (26, 27), speech recognition (28), and natural language processing (29). It now forms the core of the artificial intelligence platforms of several technology giants, such as Google, Facebook, and Microsoft, as well as many startups in the industry. Deep learning has also made its way into biological sciences (30) [for instance, in the field of genomics, where deep neural network models have been developed for predicting the effects of noncoding single-nucleotide ...