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

Abstract 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.