DPST also proposes a transformer-based model but introduces a set of inductive biases to constrain its search space. First, it reframes the de novo sequencing task in a Bayesian setting, where the amino acid posterior probabilities are predicted from spectrum information and amino acid priors. Higher prior probabilities are given to amino acids that minimize the difference between the precursor mass and the expected peptide mass calculated with dynamic programming. Second, the DPST encoder assigns a confidence value to each peak based on its consistency with neighboring peaks, prioritizing those separated by amino acid masses in the encoded spectrum representation. Finally, the DPST decoder comprises two branches: the global branch makes predictions from the entire spectrum representation, while the local branch focuses on the spectrum part corresponding to the currently predicted peptide prefix.