Methods and systems for de novo peptide sequencing using deep learning

Abstract

The present systems and methods introduce deep learning to de novo peptide sequencing from tandem mass spectrometry data. The systems and methods achieve improvements in sequencing accuracy over existing systems and methods and enables complete assembly of novel protein sequences without assisting databases. The present systems and methods are re-trainable to adapt to new sources of data and provides a complete end-to-end training and prediction solution, which is advantageous given the growing massive amount of data. The systems and methods combine deep learning and dynamic programming to solve optimization problems.