Tandem mass spectrometry has played a pivotal role in advancing proteomics,
enabling the high-throughput analysis of protein composition in biological
tissues. Many deep learning methods have been developed for \emph{de novo}
peptide sequencing task, i.e., predicting the peptide sequence for the observed
mass spectrum. However, two key challenges seriously hinder the further
advancement of this important task. Firstly, since there is no consensus for
the evaluation datasets, the empirical results in different research papers are
often not comparable, leading to unfair comparison. Secondly, the current
methods are usually limited to amino acid-level or peptide-level precision and
recall metrics. In this work, we present the first unified benchmark NovoBench
for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum
data, integrated models, and comprehensive evaluation metrics. Recent
impressive methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo
and $\pi$-HelixNovo are integrated into our framework. In addition to amino
acid-level and peptide-level precision and recall, we evaluate the models'
performance in terms of identifying post-tranlational modifications (PTMs),
efficiency and robustness to peptide length, noise peaks and missing fragment
ratio, which are important influencing factors while seldom be considered.
Leveraging this benchmark, we conduct a large-scale study of current methods,
report many insightful findings that open up new possibilities for future
development. The benchmark will be open-sourced to facilitate future research
and application.

本文介绍了第一个统一的鲁棒性肽段测序标准 NovoBench，该标准包括多样的质谱数据、整合模型和全面的评估指标，并通过对各种因素的评估，揭示了许多有启发性的发现，为未来的发展开辟了新的可能性。

NovoBench: 基于深度学习的蛋白质组学中 De Novo 肽段测序方法的基准测试

NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing  Methods in Proteomics

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。

AdaNovo：基于条件互信息的自适应全新肽段测序

AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional  Mutual Information

De novo peptide sequencing from mass spectrometry (MS) data is a critical
task in proteomics research. Traditional de novo algorithms have encountered a
bottleneck in accuracy due to the inherent complexity of proteomics data. While
deep learning-based methods have shown progress, they reduce the problem to a
translation task, potentially overlooking critical nuances between spectra and
peptides. In our research, we present ContraNovo, a pioneering algorithm that
leverages contrastive learning to extract the relationship between spectra and
peptides and incorporates the mass information into peptide decoding, aiming to
address these intricacies more efficiently. Through rigorous evaluations on two
benchmark datasets, ContraNovo consistently outshines contemporary
state-of-the-art solutions, underscoring its promising potential in enhancing
de novo peptide sequencing. The source code is available at
this https URL

ContraNovo 算法是一种创新算法，通过对质谱和肽段之间的关系进行对比学习，并将质量信息纳入肽段解码，从而更高效地解决肽段测序问题，并在两个基准数据集上得到了可靠的结果，显示出其在提升新肽段测序中的潜力。