  De novo peptide sequencing aims to recover amino acid sequences of a peptide
from tandem mass spectrometry (MS) data. Existing approaches for de novo
analysis enumerate MS evidence for all amino acid classes during inference. It
leads to over-trimming on receptive fields of MS data and restricts MS evidence
associated with following undecoded amino acids. Our approach, DPST,
circumvents these limitations with two key components: (1) A confidence value
aggregation encoder to sketch spectrum representations according to
amino-acid-based connectivity among MS; (2) A global-local fusion decoder to
progressively assimilate contextualized spectrum representations with a
predefined preconception of localized MS evidence and amino acid priors. Our
components originate from a closed-form solution and selectively attend to
informative amino-acid-aware MS representations. Through extensive empirical
studies, we demonstrate the superiority of DPST, showing that it outperforms
state-of-the-art approaches by a margin of 12% - 19% peptide accuracy.
