PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing

Proteomics utilizes tandem mass spectrometry (MS/MS) to determine peptide sequences, traditionally through database searches constrained by prior knowledge. De novo sequencing offers a database-free alternative but struggles with accurately modeling complex MS/MS spectra. Most current tools use autoregressive decoding, which is prone to error propagation and computationally slow. Here we present PowerNovo2, a non-autoregressive model based on generative normalizing flows. By leveraging variational inference, it effectively captures intricate token dependencies and peptide-level uncertainties. PowerNovo2 outperforms existing de novo tools in accuracy and speed, matching state-of-the-art autoregressive models like Casanovo while being 4.3 times faster. It also demonstrates competitive performance against other non-autoregressive methods such as π-PrimeNovo, particularly on long peptides and low-resolution spectra. As the first flow-based de novo sequencer, PowerNovo2 provides a scalable, accurate solution for large-scale proteomic applications.Author summary: Proteins play essential roles in all living organisms, and identifying their sequences is a key task in modern biology. One common way to study proteins is by using mass spectrometry, but determining peptide sequences from these data remains difficult, especially when reference databases are incomplete or unavailable. In this study, we present PowerNovo2, a new computational method that helps address this challenge.