Tabata Tsuyoshi, Yoshizawa Akiyasu C, Ogata Kosuke, Chang Chih-Hsiang, Araki Norie, Sugiyama Naoyuki, Ishihama Yasushi
Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan.
Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Mol Cell Proteomics. 2025 Jun 2;24(7):101010. doi: 10.1016/j.mcpro.2025.101010.
We propose UniScore as a metric for integrating and standardizing the outputs of multiple search engines in the analysis of data-dependent acquisition (DDA) data from LC/MS/MS-based bottom-up proteomics. UniScore is calculated from the annotation information attached to the product ions alone by matching the amino acid sequences of candidate peptides suggested by the search engine with the product ion spectrum. The acceptance criteria are controlled independently of the score values by using the false discovery rate based on the target-decoy approach. Compared to other rescoring methods that use deep learning-based spectral prediction, larger amounts of data can be processed using minimal computing resources. When applied to large-scale global proteome data and phosphoproteome data, the UniScore approach outperformed each of the conventional single search engines examined (Comet, X! Tandem, Mascot, and MaxQuant). Furthermore, UniScore could also be directly applied to peptide matching in chimeric spectra without any additional filters.
我们提出将统一评分（UniScore）作为一种度量标准，用于在基于液相色谱-串联质谱（LC/MS/MS）的自下而上蛋白质组学中对数据依赖型采集（DDA）数据进行分析时，整合和标准化多个搜索引擎的输出结果。统一评分仅根据附着于产物离子的注释信息来计算，即通过将搜索引擎建议的候选肽段的氨基酸序列与产物离子谱进行匹配。通过使用基于目标-诱饵方法的错误发现率，接受标准独立于评分值进行控制。与其他使用基于深度学习的光谱预测的重新评分方法相比，使用最少的计算资源就能处理大量数据。当应用于大规模的全蛋白质组数据和磷酸化蛋白质组数据时，统一评分方法优于所考察的每一种传统单一搜索引擎（Comet、X! Tandem、Mascot和MaxQuant）。此外，统一评分也可以直接应用于嵌合光谱中的肽段匹配，无需任何额外的筛选。
