De novo peptide sequencing by deep learning

Significance Our method, DeepNovo, introduces deep learning to de novo peptide sequencing from tandem MS data, the key technology for protein characterization in proteomics research. DeepNovo achieves major improvement of sequencing accuracy over state of the art methods and subsequently enables complete assembly of protein sequences without assisting databases. Our model is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution, an important feature given the growing massive amount of data. Our study also presents an innovative approach to combine deep learning and dynamic programming to solve optimization problems. Abstract 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.