Evaluation of DDA Library-Free Strategies for Phosphoproteomics and Ubiquitinomics Data-Independent Acquisition Data

Phosphoproteomics and ubiquitinomics data-independentacquisition(DIA) mass spectrometry (MS) data is typically analyzed by using adata-dependent acquisition (DDA) spectral library. The performanceof various library-free strategies for analyzing phosphoproteomicsand ubiquitinomics DIA MS data has not been evaluated. In this study,we systematically compare four commonly used DDA library-free approachesincluding Spectronaut's directDIA, DIA-Umpire, DIA-MSFragger,and in silico-predicted library for analysis of phosphoproteomicsSWATH, DIA, and diaPASEF data as well as ubiquitinomics diaPASEF data.Spectronaut's directDIA shows the highest sensitivity for phosphopeptidedetection not only in synthetic phosphopeptide samples but also inphosphoproteomics SWATH-MS and DIA data from real biological samples,when compared to the other three library-free strategies. For phosphoproteomicsdiaPASEF data, Spectronaut's directDIA and the in silico-predictedlibrary based on DIA-NN identify almost the same number of phosphopeptidesas a project-specific DDA spectral library. However, only about 30%of the total phosphopeptides are commonly identified, suggesting thatthe library-free strategies for phospho-diaPASEF data need furtherimprovement in terms of sensitivity. For ubiquitinomics diaPASEF data,the in silico-predicted library performs the best among the four workflowsand detects similar to 50% more K-GG peptides than a project-specificDDA spectral library. Our results demonstrate that Spectronaut'sdirectDIA is suitable for the analysis of phosphoproteomics SWATH-MSand DIA MS data, while the in silico-predicted library based on DIA-NNshows substantial advantages for ubiquitinomics diaPASEF MS data.

Automated summary

This study compares four commonly used DDA library-free approaches for analyzing phosphoproteomics and ubiquitinomics DIA MS data, revealing Spectronaut's directDIA as the top performer for phosphopeptide detection and an in silico-predicted library based on DIA-NN as the best option for ubiquitinomics diaPASEF data analysis.