Less missing values—evaluation of proteomics workflows for the quantification of (small) proteins

Quantitative information on protein abundance is crucial to understand biological processes and is therefore frequently gathered in proteomic studies. However, the quality of a quantitative proteomic dataset is greatly affected by the number of missing values, which need to be minimized to produce robust and meaningful data. In this context, small proteins (≤100 amino acids) pose specific analytical challenges, which hinder their efficient identification and quantitative characterization in complex proteomes. In this study, methods for sample preparation and MS-data processing are systematically evaluated for their contribution to identification and quantification of small proteins of <i>Clostridioides difficile</i> 630 Δ<i>erm</i>. Results show that small protein enrichment can enhance the number of identified and quantified proteins also for low abundant small proteins. Through application of spectral libraries for identification of MS spectra the number of robustly quantified proteins is increased and a lower limit of their detection is reached. Additionally, the dataset presented here is currently the most comprehensive protein repository for <i>C. difficile</i> covering 84.7% of the predicted proteome and 61.4% of all predicted small proteins of this important pathogen.