应用非同质荷比规则提高蛋白质组学检索结果的可信度和灵敏度

如何筛选合理的数据库匹配结果对于基于质谱的蛋白质组学研究至关重要。但是目前，基于打分体系和反转数据库的筛选方法都无法有效的避免假阳性和假阴性匹配的存在。因此，本文提出了一种系统的搜索策略: 非同质荷比检索规则 (INMZS)。在该策略中，所有匹配结果都需要检查相关匹配质荷比的分享程度，只有那些相关质荷比均为专有匹配时，蛋白质才会被作为可信结果保留，策略还采用迭代搜索方法以提高鉴定低丰度组分的灵敏度。最终，所有的匹配结果由诱饵数据库方法进行评估以得到最终结果列表。INMZS策略在标准蛋白质混合物和大规模人肝蛋白质组数据上进行了模拟及应用，结果显示，INMZS规则和诱饵数据库评估方法的结和可以有效的保证蛋白质组学数据匹配的可信度及灵敏度，可以广泛适用于基于二维凝胶电泳及非shotgun技术的蛋白组学研究中。

Iterative Non‐m/z‐sharing Rule for Confident and Sensitive Protein Identification of Non‐shotgun Proteomics

Selecting reasonable matches from the database search results is crucial to mass spectrometry‐based proteomics identification. However, the current score‐based filter solution and decoy database methods are not effective enough to prevent all false positive and false negative selections. In this study, a systematic search strategy named iterative non‐m/z‐sharing (INMZS) analysis was proposed to address the problem. In the strategy, all search results were screened based on the share status of corresponding matched m/z, only the proteins that matched with exclusive m/z information were reserved for the confident matches. The researchers did further iterative search to improve the identification of minor components in a spot. Finally, identifications were confirmed by decoy database evaluation for the final phase of protein identification. Simulation and application tests of INMZS were implemented on a large human liver data set and standard protein cocktails. The result shows that INMZS plus decoy database evaluation is efficient in ensuring the confidence and sensitivity of 2‐DE or similar non‐shotgun based proteome identification.