Tandem mass spectrometry has played a pivotal role in advancing proteomics,
enabling the analysis of protein composition in biological samples. Despite the
development of various deep learning methods for identifying amino acid
sequences (peptides) responsible for observed spectra, challenges persist in
\emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify
amino acids with post-translational modifications (PTMs) due to their lower
frequency in training data compared to canonical amino acids, further resulting
in decreased peptide-level identification precision. Secondly, diverse types of
noise and missing peaks in mass spectra reduce the reliability of training data
(peptide-spectrum matches, PSMs). To address these challenges, we propose
AdaNovo, a novel framework that calculates conditional mutual information (CMI)
between the spectrum and each amino acid/peptide, using CMI for adaptive model
training. Extensive experiments demonstrate AdaNovo's state-of-the-art
performance on a 9-species benchmark, where the peptides in the training set
are almost completely disjoint from the peptides of the test sets. Moreover,
AdaNovo excels in identifying amino acids with PTMs and exhibits robustness
against data noise. The supplementary materials contain the official code.

AdaNovo 是一个新颖的框架，通过计算光谱与每个氨基酸 / 肽之间的条件互信息（CMI），利用 CMI 进行自适应模型训练，以解决串级质谱在鉴定具有后转录修饰的氨基酸时遇到的挑战，并且在鉴定肽和抗数据噪声方面表现出色。