PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Abstract

AbstractAccurate and comprehensive peptide precursor ions are crucial to tandem mass spectrometry-based peptide identification. An identification engine can greatly benefit from the search space reduction hinted by credible and detailed precursors. Additionally, both the number of identifications and the spectrum explainability can be increased by considering multiple precursors per spectrum. Here, we propose PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for the following peptide identification. PepPre is evaluated both on regular and cross-linked peptides datasets, and compared with 11 methods in this paper. The experimental results show that PepPre achieves 203% more PSM and 68% more peptide identifications than instrument software for regular peptides, and 99% more PSM and 27% more peptide pair identifications for cross-linked peptides, which also outperforms all other evaluated methods. In addition to the increased identification numbers, further credibility evaluation evidence that the identifications are credible. Moreover, by widening the isolation window of data acquisition from 2 Th to 8 Th, the engine is able to identify at least 64% more PSMs with PepPre, demonstrating the potential advantages of large isolation windows.Graphical TOC Entry