A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis

Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.

Automated summary

Tandem mass spectrometry (MS/MS) is widely used for analyzing complex proteomic samples. However, traditional peptide identification methods such as protein sequence database searching and spectral library searching have limitations in discriminating correct and incorrect spectrum assignments and identifying post-translationally modified peptides. To overcome these limitations, a hybrid search strategy that combines protein sequence database and spectral library searches has been developed, along with the use of Global PTM Discovery (G-PTM-D) to generate spectral libraries for various PTMs. This approach improves identification success rates and sensitivity.