Deep Novo A+: Improving the Deep Learning Model for De Novo Peptide Sequencing with Additional Ion Types and Validation Set

Abstract

Background: De novo peptide sequencing is one of the key technologies in proteomics, which can extract peptide sequences directly from tandem mass spectrometry (MS/MS) spectra without any protein databases. Since the accuracy and efficiency of de novo peptide sequencing can be affected by the quality of the MS/MS data, the DeepNovo method using deep learning for de novo peptide sequencing is introduced, which outperforms the other state-of-the-art de novo sequencing methods. Objective: For superior performance and better generalization ability, additional ion types of spectra should be considered and the model of DeepNovo should be adaptive. Methods: Two improvements are introduced in the DeepNovo A+ method: a_ions are added in the spectral analysis, and the validation set is used to automatically determine the number of training epochs. Results: Experiments show that compared to the DeepNovo method, the DeepNovo A+ method can consistently improve the accuracy of de novo sequencing under different conditions. Conclusion: By adding a_ions and using the validation set, the performance of de novo sequencing can be improved effectively.